Patent Publication Number: US-2016235352-A1

Title: Methods and systems for determining subject-specific parameters for a neuromodulation therapy

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS 
     This application is a continuation of pending application Ser. No. 11/647,700, filed Dec. 29, 2006, which application is a continuation of application Ser. No. 10/889,844, filed Jul. 12, 2004; which application claims benefit of U.S. Provisional Application No. 60/562,487, filed Apr. 14, 2004, and is a continuation-in-part of U.S. application Ser. No. 10/818,333, filed Apr. 5, 2004, which, in turn, claims benefit of U.S. Provisional Application No. 60/460,140, filed Apr. 3, 2003; U.S. application Ser. No. 10/818,333 is also a continuation-in-part of U.S. application Ser. No. 10/753,205, filed Jan. 6, 2004, which claims the benefit of U.S. Provisional Application No. 60/438,286, filed Jan. 6, 2003; U.S. application Ser. No. 10/753,205 is also a continuation-in-part of U.S. application Ser. No. 10/718,248, filed Nov. 20, 2003; which is a continuation-in-part of U.S. application Ser. No. 10/008,576, filed Nov. 11, 2001, now U.S. Pat. No. 6,819,956; which is a continuation-in-part of U.S. application Ser. No. 09/340,326, filed Jun. 25, 1999, now U.S. Pat. No. 6,366,813; which claims the benefit of U.S. Provisional Application No. 60/095,413, filed Aug. 5, 1998; U.S. application Ser. No. 10/718,248 also claims benefit of U.S. Provisional Application Nos. 60/427,699, filed Nov. 20, 2002, and 60/436,792, filed Dec. 27, 2002. 
    
    
     BACKGROUND 
     1. Field of the Invention 
     The present invention relates generally to neurological disease and, more particularly, to intracranial stimulation for optimal control of movement disorders and other neurological disease. 
     2. Related Art 
     There are a wide variety of treatment modalities for neurological disease including movement disorders such as Parkinson&#39;s disease, Huntington&#39;s disease, and Restless Leg Syndrome, as well as psychiatric disease including depression, bipolar disorder and borderline personality disorders. These treatment modalities are moderately efficacious; however, they suffer from several severe drawbacks. Each of these traditional treatment modalities and their associated limitations are described below. 
     One common conventional technique for controlling neurological disease includes the use of dopaminergic agonists or anticholinergic agents. Medical management using these techniques requires considerable iteration in dosing adjustments before an “optimal” balance between efficacy and side effect minimization is achieved. Variation, including both circadian and postprandial variations, causes wide fluctuation in symptomatology. This commonly results in alternation between “on” and “off” periods during which the patient possesses and loses motor functionality, respectively. 
     Another traditional approach for controlling movement disorders is tissue ablation. Tissue ablation is most commonly accomplished through stereotactic neurosurgical procedures, including pallidotomy, thalamotomy, subthalamotomy, and other lesioning procedures. These procedures have been found to be moderately efficacious. However, in addition to posing risks that are inherent to neurosurgical operations, these procedures suffer from a number of fundamental limitations. One such limitation is that tissue removal or destruction is irreversible. As a result, excessive or inadvertent removal of tissue cannot be remedied. 
     Furthermore, undesirable side effects, including compromise of vision and motor or sensory functions, are likely to be permanent conditions. In particular, bilateral interventions place the patient at considerable risk for developing permanent neurologic side effects, including incontinence, aphasia, and grave psychic disorders. An additional drawback to this approach is that the “magnitude” of treatment is constant. That is, it is not possible to vary treatment intensity over time, as may be required to match circadian, postprandial, and other fluctuations in symptomatology and consequent therapeutic needs. Thus, decrease in treatment “magnitude” is not possible while an increase in treatment “magnitude” necessitates reoperation. Some adjustment is possible through augmentation with pharmacologic treatment; however, these additional treatments are subject to the above-noted limitations related to drug therapy. 
     Another traditional approach for controlling movement disorders and other neurological disease includes tissue transplantation, typically from animal or human mesencephalic cells. Although tissue transplantation in humans has been performed for many years, it remains experimental and is limited by ethical concerns when performed using a human source. Furthermore, graft survival, as well as subsequent functional connection with intracranial nuclei, are problematic. The yield, or percentage of surviving cells, is relatively small and is not always predictable, posing difficulties with respect to the control of treatment “magnitude.” 
     Another traditional approach for controlling neurological disease is the continuous electrical stimulation of a predetermined neurological region. Chronic high frequency intracranial electrical stimulation is typically used to inhibit cellular activity in an attempt to functionally replicate the effect of tissue ablation, such as pallidotomy and thalamotomy. Acute electrical stimulation and electrical recording and impedance measuring of neural tissue have been used for several decades in the identification of brain structures for both research purposes as well as for target localization during neurosurgical operations for a variety of neurological diseases. During intraoperative electrical stimulation, reduction in tremor has been achieved using frequencies typically on the order of 75 to 330 Hz. Based on these findings, chronically implanted constant-amplitude electrical stimulators have been implanted in such sites as the thalamus, subthalamic nucleus and globus pallidus. 
     Chronic constant-amplitude stimulation has been shown to be moderately efficacious. However, it has also been found to be limited by the lack of responsiveness to change in patient system symptomatology and neuromotor function. Following implantation, a protracted phase of parameter adjustment, typically lasting several weeks to months, is endured by the patient while stimulation parameters are interactively adjusted during a series of patient appointments. Once determined, an “acceptable” treatment magnitude is maintained as a constant stimulation level. A drawback to this approach is that the system is not responsive to changes in patient need for treatment. Stimulation is typically augmented with pharmacological treatment to accommodate such changes, causing fluctuation of the net magnitude of treatment with the plasma levels of the pharmacologic agent. 
     As noted, while the above and other convention treatment modalities offer some benefit to patients with movement disorders, their efficacy is limited. For the above-noted reasons, with such treatment modalities it is difficult and often impossible to arrive at an optimal treatment “magnitude,” that is, an optimal dose or intensity of treatment. Furthermore, patients are subjected to periods of overtreatment and undertreatment due to variations in disease state. Such disease state variations include, for example, circadian fluctuations, postprandial (after meal) and nutrition variations, transients accompanying variations in plasma concentrations of pharmacological agents, chronic progression of disease, and others. 
     Moreover, a particularly significant drawback to the above and other traditional treatment modalities is that they suffer from inconsistencies in treatment magnitude. For example, with respect to drug therapy, a decrease in responsiveness to pharmacologic agents eventually progresses to eventually preclude effective pharmacologic treatment. With respect to tissue ablation, progression of disease often necessitates reoperation to extend pallidotomy and thalamotomy lesion dimensions. Regarding tissue transplantation, imbalances between cell transplant formation rates and cell death rates cause unanticipated fluctuations in treatment magnitude. For continuous electrical stimulation, changes in electrode position, electrode impedance, as well as patient responsiveness to stimulation and augmentative pharmacologic agents, cause a change in response to a constant magnitude of therapy. 
     Currently, magnets commonly serve as input devices used by patients with implantable stimulators, including deep brain stimulators, pacemakers, and spinal cord stimulators. Current systems require the patient to manually turn the system off at night time to conserve battery power and use such magnets to maintain system power. This presents considerable difficulty to many patients whose tremor significantly impairs arm function, as they are unable to hold a magnet in a stable manner over the implanted electronics module. Consequently, many patients are unable to turn their stimulators on in the morning without assistance. 
     What is needed, therefore, is an apparatus and method for treatment of patients with neurological disease in general and movement disorders in particular that is capable of determining and providing an optimal dose or intensity of treatment. Furthermore, the apparatus and method should be responsive to unpredictable changes in symptomatology and minimize alternations between states of overtreatment and undertreatment. The system should also be capable of anticipating future changes in symptomatology and neuromotor functionality, and being responsive to such changes when they occur. 
     SUMMARY OF THE INVENTION 
     One aspect of the invention is a method of monitoring a subject&#39;s response to a plurality of neuromodulation therapies. The method comprises delivering a neuromodulation therapy that is adapted to prevent and/or manage a symptom of a subject suffering from a neurological or psychiatric disorder, wherein the neuromodulation therapy is specified by a set of parameters, recording the set of parameters of the neuromodulation therapy and a subject&#39;s response to the neuromodulation therapy, delivering a subsequent neuromodulation therapy to the subject, wherein at least one parameter of the set of parameters of the subsequent neuromodulation therapy is automatically changed relative to the set of parameters of the previously delivered neuromodulation therapy, and recording the set of parameters of the subsequent neuromodulation therapy and a subject&#39;s response to the subsequent neuromodulation therapy. 
     In some embodiments the symptom comprises seizure activity and the neurological disorder comprises epilepsy. 
     In some embodiments recording the set of parameters of the neuromodulation therapy and the subject&#39;s response to the neuromodulation therapy and recording the set of parameters of the subsequent neuromodulation therapy and the subject&#39;s response to the subsequent neuromodulation therapy are performed in a memory in an implantable neuromodulation system. The method can further include wirelessly transmitting data that is indicative of the recorded parameters of the neuromodulation therapy and the subject&#39;s response to the neuromodulation therapy and data that is indicative of the recorded set of parameters of the subsequent neuromodulation therapy and the subject&#39;s response to the subsequent neuromodulation therapy from the memory of the implanted neuromodulation system to a memory of an external device that is external to the subject&#39;s body. 
     In some embodiments the method also includes analyzing the data that is indicative of the subject&#39;s response to the neuromodulation therapy and the subject&#39;s response to the subsequent neuromodulation therapy to determine a substantially optimal set of parameters for a later delivered neuromodulation therapy. The neuromodulation therapy and the subsequent neuromodulation therapy can be delivered to the subject using an implantable neuromodulation system. In some embodiments the method further comprises wirelessly programming the implantable neuromodulation system with the determined optimal parameters, while in some embodiments the implantable neuromodulation system is automatically programmed to use the substantially optimal set of stimulation parameters for a later delivered neuromodulation therapy. 
     In some embodiments the method also includes analyzing the subject&#39;s response to the neuromodulation therapy and subject&#39;s response to the subsequent neuromodulation therapy to determine efficacy of the neuromodulation therapy and efficacy of the subsequent neuromodulation therapy. 
     In some embodiments the method also includes wirelessly transmitting data that is indicative of the set of parameters of the neuromodulation therapy and the subject&#39;s response to the neuromodulation therapy and data that is indicative of the set of parameters of the subsequent neuromodulation therapy and the subject&#39;s response to the subsequent neuromodulation therapy from an implantable neuromodulation system to a memory of a device external to the subject&#39;s body, where recording the parameters of the neuromodulation therapy and the subject&#39;s response to the neuromodulation therapy and recording the parameters of the subsequent neuromodulation therapy and the subject&#39;s response to the subsequent neuromodulation therapy are performed in the memory of the device external to the subject&#39;s body. 
     In some embodiments the neuromodulation therapy comprises electrical stimulation delivered via electrodes and the set of parameters comprises at least one of electrode configuration, stimulation current, stimulation voltage, stimulation frequency, pulse width, frequency of pulses within a burst, pulses per burst, frequency of bursts, duration of bursts, and pulse train count. 
     In some embodiments the neuromodulation therapy comprises intracranial electrical stimulation. In some embodiments the neuromodulation therapy comprises peripheral nerve electrical stimulation such as vagus nerve electrical stimulation. 
     In some embodiments the neuromodulation therapy comprises delivery of medications. Delivery of medications can be performed with an implanted medication dispenser. 
     In some embodiments the neuromodulation therapy and subsequent neuromodulation therapy(ies) are delivered when the symptom is detected and/or predicted through analysis of an EEG signal from the subject. 
     In some embodiments the subject&#39;s response comprises a brain activity response that is monitored with one or more brain electrodes that are in communication with the implantable neuromodulation system. 
     Another aspect of the invention is a method of determining patient-specific, substantially optimal stimulation parameters of a stimulation signal for managing a patient with epilepsy. The method includes analyzing an EEG signal from a patient to detect and/or predict seizure activity, delivering a first stimulation signal to the patient with an implanted stimulation and recording unit when the analysis of the EEG signal detects and/or predicts seizure activity, wherein the stimulation signal comprises a set of stimulation parameters, recording the patient&#39;s response to the first stimulation signal, automatically determining parameters for a second stimulation signal, wherein the second stimulation signal comprises at least one changed parameter from the set of predetermined stimulation parameters of the first stimulation signal, delivering the second stimulation signal with the implanted stimulation and recording unit when analysis of the EEG signal from the patient detects and/or predicts subsequent seizure activity, recording the patient&#39;s response to the second stimulation signal, wirelessly transmitting data that is indicative of the patient&#39;s response to the first stimulation signal, the set of parameters of the first stimulation signal, response to the second stimulation signal, and set of parameter of the second stimulation signal to a device that is external to the patient&#39;s body, analyzing the patient&#39;s response to the first stimulation signal and response to the second stimulation signal to determine substantially optimal stimulation parameters, and programming the implanted stimulation and recording unit with the determined substantially optimal stimulation parameters. 
     Another aspect of the invention is a system for delivering a neural modulation output to a subject suffering from a neurological or psychiatric disorder. The system includes an assembly adapted to acquire a signal from a subject that is indicative or predictive of a symptom of the neurological or psychiatric disorder and to deliver a neural modulation output to the subject to manage and/or prevent the symptom of the neurological or psychiatric disorder, an implantable device coupled to the assembly, the implantable device configured to automatically sweep through a plurality of neural modulation outputs that comprise different sets of neural modulation parameters, and a memory that is adapted to store the different sets of stimulation parameters and the subject&#39;s response to the neural modulation outputs that comprise different sets of neural modulation parameters. 
     In some embodiments the neurological disorder comprises epilepsy and the symptom comprises seizure activity. 
     In some embodiments the memory is disposed in an external device and the system further comprises the external device that is configured to be external to the subject&#39;s body and is in wireless communication with the implantable device, the wireless communication being used to transmit the different sets of neural modulation parameters and the subject&#39;s response to the neural modulation outputs that comprise different sets of neural modulation outputs from the implantable device to the memory of the external device. At least one of the external device and implantable device can be configured to analyze the subject&#39;s response to the neural modulation outputs that comprise different sets of neural modulation parameters to determine efficacy of the neural modulation outputs. The implantable device can sweep through the different sets of neural modulation parameters to determine substantially optimal neural modulation parameters of the neural modulation output to treat the subject&#39;s neurological or psychiatric disorder. 
     In some embodiments the substantially optimal neural modulation parameters are manually programmed by a user using an external device that is in wireless communication with the implantable device. In other embodiments the implantable device is programmed to automatically use the substantially optimal neural modulation parameters for later delivered neural modulation outputs. 
     In some embodiments the neural modulation output comprises electrical stimulation and the assembly comprises a plurality of electrodes, wherein the neural modulation parameters comprise at least one of electrode configuration, stimulation current, stimulation voltage, stimulation frequency, pulse width, frequency of pulses within a burst, pulses per burst, frequency of bursts, duration of bursts, and pulse train count. 
     In some embodiments the assembly comprises a plurality of electrodes, wherein a subset of electrodes are configured to acquire the physiological signals and a subset of electrodes are configured to deliver the neural modulation output. 
     In some embodiments the neuromodulation output comprises intracranial electrical stimulation. In some embodiments the neuromodulation output comprises peripheral nerve electrical stimulation such as vagus nerve electrical stimulation. 
     In some embodiments the neuromodulation output comprises delivery of medications. 
     Further features and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention, are described in detail below with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is described with reference to the accompanying drawings. In the drawings, like reference numerals indicate identical or functionally similar elements. 
         FIG. 1  is a schematic diagram of one embodiment of the present invention implanted bilaterally in a human patient. 
         FIG. 2  is an architectural block diagram of one embodiment of the neurological control system of the present invention. 
         FIG. 3  is a block diagram of one embodiment of an intracranial recording electrode (ICRE) signal processor and an intracranial stimulating electrode (ICSE) signal processor each of which are included within the signal processor illustrated in  FIG. 2 . 
         FIG. 4  is a schematic diagram of a globus pallidus implanted with stimulating and recording electrodes in accordance with one embodiment of the present invention. 
         FIG. 5  is a block diagram of one embodiment of an EMG signal processor that is included in one embodiment of the signal processor illustrated in  FIG. 2 . 
         FIG. 6  is a block diagram of one embodiment of an EEG signal processor module that is included in one embodiment of the signal processor illustrated in  FIG. 2 . 
         FIG. 7  is a block diagram of one embodiment of an accelerometer signal processor that is incorporated into certain embodiments of the signal processor illustrated in  FIG. 2 . 
         FIG. 8  is a block diagram of one embodiment of an acoustic signal processor that is included in certain embodiments of the signal processor illustrated in  FIG. 2 . 
         FIG. 9  is block diagram of one embodiment of a peripheral nerve electrode (PNE) signal processor  237  that is implemented in certain embodiments of signal processor  71 . PNE signal 
         FIG. 10  is a schematic diagram of one embodiment of the signal processor illustrated in  FIG. 2 . 
         FIG. 11  is a schematic diagram of the patient-neural modulator system illustrated in  FIG. 2  illustrated to show its controller and observer components. 
         FIG. 12  is a schematic diagram of one embodiment of the control circuit illustrated in  FIG. 2 . 
         FIG. 13  is a schematic diagram of electrical stimulation waveforms for neural modulation. 
         FIG. 14  is a schematic diagram of one example of the recorded waveforms. 
         FIG. 15  is a schematic block diagram of an analog switch used to connect one or an opposing polarity pair of Zener diodes across the noninverting and inverting inputs of an intracranial recording electrode amplifier. 
         FIG. 16  is a diagram of a two coil embodiment of the power delivery unit. 
         FIG. 17  is a magnification of the configurations of  FIGS. 16 and 18  showing the magnetic flux penetrating the skin. 
         FIG. 18  is a diagram of a three coil embodiment of the power delivery unit. 
         FIG. 19  is a diagram of the power delivery unit with coil holder. 
         FIG. 20  is a diagram of the coil holder. 
         FIG. 21  is a diagram of the electromagnetic copies in proximity to the head of a patient. 
         FIG. 22  is a diagram of multiple coil embodiments. 
         FIG. 23  is a diagram of the paracranial design, with implanted components in close proximity to the patient&#39;s head. 
         FIG. 24  is a diagram of the power conversion unit that includes the electromagnetic coupling element. 
         FIG. 25  is a diagram of the power conversion circuit. 
         FIG. 26  is a diagram of the overall system. 
         FIG. 27  is a diagram of the stimulating and recording unit. 
         FIG. 28  is a diagram of the system enclosure secured to the calvarium. 
         FIG. 29  is a diagram of a lower profile design. 
         FIG. 30  is a diagram of a lower profile design with the system enclosure partially recessed into the calvarium. 
         FIG. 31  is a diagram of a lower profile design with the system enclosure fully recessed into the calvarium. 
         FIG. 32  is a diagram of a second lower profile design with the system enclosure fully recessed into the calvaium. 
         FIG. 33  is a diagram of a neurological control system. 
         FIG. 34  is a diagram of a second neurological control system. 
         FIG. 35  is a schematic diagram of one embodiment of the present invention implanted unilaterally in a human patient, with the system enclosure recessed in the calvarium, shown as a lateral view. 
         FIG. 36  is a schematic diagram of one embodiment of the present invention implanted unilaterally in a human patient, with the system enclosure recessed in the calvarium, shown as a anteroposterior view. 
         FIG. 37  is a schematic diagram of a cross section of calvarium with system enclosure shown implanted recessed within the calvarium. 
         FIG. 38  is a schematic diagram of a cross section of calvarium with drill bit shown in place after completion of process of drilling hole in calvarium. 
         FIG. 39  is a schematic diagram of a cross section of calvarium with drill bit, with a penetration detection release mechanism, shown in place after completion of process of drilling hole in calvarium. 
         FIG. 40  is a diagram depicting the path of the intracranial catheter and its connection to the electrical elements. 
         FIG. 41  depicts one design for the system enclosure for implantation in the calvarium. 
         FIGS. 42 and 43  depict a dual intracranial catheter design. 
         FIG. 44  is a schematic diagram of one embodiment of the present invention implanted bilaterally in a human patient. 
         FIG. 45  is a schematic diagram, lateral view, of one embodiment of the present invention implanted unilaterally in a human patient. 
         FIG. 46  is a schematic diagram, anteroposterior view, of one embodiment of the present invention implanted unilaterally in a human patient, with multiple catheters and neuromodulators. 
         FIG. 47  is a schematic diagram of one embodiment of the present invention, depicting a set of noninvasive and implanted sensors and neuromodulators in a human patient. 
         FIG. 48  is a schematic diagram of the dermatomal distributions recruited by neuromodulators. 
         FIG. 49  is a schematic diagram of a noninvasive version of neurological control system, with sensors and neuromodulators overlying dermatomal distributions recruited by neuromodulators. 
         FIG. 50  is a functional block diagram of neurological control system, with sensors and neuromodulators implanted in the temporal lobe. 
         FIG. 51  is a functional block diagram of neurological control system, with sensors and neuromodulators implanted a multiplicity of locations, including the temporal lobe and deep brain regions. 
         FIG. 52  is a functional block diagram of neurological control system, with sensors and neuromodulators implanted a multiplicity of locations, including the temporal lobe and deep brain regions, showing control input and control output waveforms versus time. 
         FIG. 53  is a functional block diagram of neurological control system, with sensors and neuromodulators implanted a multiplicity of locations, including the temporal lobe and deep brain regions, showing control input and control output waveforms versus time. 
         FIG. 54  is a timing diagram showing control input and control output waveforms versus time along with five representative elements of neural state vector X over time, during which time control input deviates outside target range. 
         FIG. 55  is a timing diagram showing control input and control output waveforms versus time along with five representative elements of neural state vector X over time, during which time control input remains within target range. 
         FIG. 56  is a timing diagram showing control input and control output waveforms versus time along with five representative elements of neural state vector X over time, during which time control input remains within target range, under conditions with and without the application of a perturbation. 
         FIG. 57  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with five representative elements of neural state vector X over time, during which time control input remains within target range, under conditions with and without the application of a perturbation. 
         FIG. 58  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time control input remains within normal range, under conditions without the application of a perturbation. Closed-loop neuromodulation control is turned on for a duration during which time control input is more tightly maintained in control range, a subset of normal range. 
         FIG. 59  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time control input deviates outside normal range into borderline range and into critical range, under conditions without the application of a perturbation. Closed-loop neuromodulation control remains off, and there are no EEG abnormalities nor neurological signs or symptoms. 
         FIG. 60  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time control input deviates outside normal range into borderline range and into critical range, under conditions without the application of a perturbation. Closed-loop neuromodulation control remains off, and EEG abnormalities develop and are followed by neurological signs or symptoms. 
         FIG. 61  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time control input remains within normal range, under conditions without the application of a perturbation. Closed-loop neuromodulation control remains on, and there are no EEG abnormalities nor neurological signs or symptoms. 
         FIG. 62  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time control input deviates outside normal range and closed-loop neuromodulation control is turned on, bringing control input back into control range, which is a subset of normal range. There are no EEG abnormalities nor neurological signs or symptoms during this time. 
         FIG. 63  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time control input deviates outside normal range and closed-loop neuromodulation control is turned on, bringing control input back into control range, which is a subset of normal range, following which time closed-loop neuromodulation control is turned back off. There are no EEG abnormalities nor neurological signs or symptoms during this time. 
         FIG. 64  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time a perturbation is applied and control input decreases but remains inside normal range. Closed-loop neuromodulation control remains off, and there are no EEG abnormalities nor neurological signs or symptoms during this time. 
         FIG. 65  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time a perturbation is applied and control input deviates outside normal range into borderline range and critical range and then returns spontaneously to normal range. Closed-loop neuromodulation control remains off, and there are no EEG abnormalities nor neurological signs or symptoms during this time. 
         FIG. 66  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time a perturbation is applied and control input deviates outside normal range into borderline range and critical range, then back into borderline range and normal range and again into borderline range and critical range, following which EEG abnormalities develop and which are followed by neurological signs or symptoms. Closed-loop neuromodulation control remains off during this time. 
         FIG. 67  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time a perturbation is applied and control input decreases but remains inside control range, which is a subset of normal range. Closed-loop neuromodulation control remains on, and there are no EEG abnormalities nor neurological signs or symptoms during this time. 
         FIG. 68  is a timing diagram showing EEG waveforms, control input and control output waveforms versus time along with one representative element of neural state vector X over time, during which time a perturbation is applied and control input deviates outside control range yet remains within normal range. Closed-loop neuromodulation control is turned on, and control input is brought back within control range. Control input remains within normal range and there are no EEG abnormalities nor neurological signs or symptoms during this time. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a schematic diagram of one embodiment of the intracranial stimulator of the present invention implanted bilaterally in a human patient. In the embodiment illustrated in  FIG. 1 , two neurological control systems  999  are shown implanted bilaterally. Each system  999  includes a stimulating and recording unit  26  and one or more intracranial components described below. As described in this illustrative embodiment, the intracranial components preferably include a stimulating electrode array  37 . However, it should become apparent to those of ordinary skill in the relevant art after reading the present disclosure that the stimulating electrodes may also be extracranial; that is, attached to a peripheral nerve in addition to or in place of being located within the cranium. As shown in  FIG. 1 , stimulating and recording unit  26  of each neurological control system  999  is preferably implanted contralateral to the intracranial components of the device. 
     As one skilled in the relevant art would find apparent from the following description, the configuration illustrated in  FIG. 1  is just one example of the present invention. Many other configurations are contemplated. For example, in alternative embodiments of the present invention, the stimulating and recording unit  26  is implanted ipsilateral or bilateral to the intracranial components. It should also be understood that the stimulating and recording unit  26  can receive ipsilateral, contralateral or bilateral inputs from sensors and deliver ipsilateral, contralateral, or bilateral outputs to a single or a plurality of intracranial stimulating electrode arrays  37 . Preferably, these inputs are direct or preamplified signals from at least one of EMG electrode array  50 , EEG electrode array  51 , Accelerometer Array  52 , Acoustic Transducer Array  53 , Peripheral Nerve Electrode Array  54 , and Intracranial Recording Electrode Array  38 . The signals input from these sensors will be referred to herein as “sensory input modalities”  247 . The outputs include but are not limited to one or more stimulating current signals or stimulating voltage signals to Intracranial Stimulating Electrode Array  37 . 
     In the embodiment illustrated in  FIG. 1 , the two unilateral systems  26  are shown to receive sensory inputs from the side contralateral as well as the intracranial stimulating electrode arrays  37 . In the illustrative embodiment, systems  26  also receive sensory inputs from intracranial recording electrode arrays  38 . As will become apparent from the following description, intracranial recording electrode arrays  38  may provide valuable feedback information. 
     It should be understood that this depiction is for simplicity only, and that any combination of ipsilateral, contralateral or bilateral combination of each of the multiple sensory input modalities and multiple stimulation output channels may be employed. In addition, stimulating and recording units  26  may be a single device, two communicating devices, or two independent devices. Accordingly, these and other configurations are considered to be within the scope of the present invention. It is anticipated that stimulating and recording units  26 , if implemented as distinct units, would likely be implanted in separate procedures (soon after clinical introduction) to minimize the likelihood of drastic neurological complications. 
     In the exemplary embodiment illustrated in  FIG. 1 , the intracranial stimulating electrode array  37  includes a plurality of intracranial stimulating electrodes  1 ,  2 ,  3  and  4 . Array  37  may, of course, have more or fewer electrodes than that depicted in  FIG. 1 . These intracranial stimulating electrodes  1 - 4  may be used to provide stimulation to a predetermined nervous system component. The electrical stimulation provided by the intracranial stimulating electrodes  1 - 4  may be excitatory or inhibitory, and this may vary in a manner which is preprogrammed, varied in real-time, computed in advance using a predictive algorithm, or determined using another technique now or latter developed. 
     The intracranial recording electrode arrays  38  includes intracranial recording electrodes  5  and  6 . In accordance with one embodiment of the present invention, the intracranial recording electrodes  5 ,  6  are used to record cortical activity as a measure of response to treatment and as a predictor of impeding treatment magnitude requirements. In the illustrative embodiment, intracranial recording electrodes  5  and  6  are depicted in a location superficial to the intracranial stimulating electrodes  1 - 4 . However, this positioning may be reversed or the intracranial stimulating electrodes  1 - 4  and intracranial recording electrodes  5  and  6  may be interspersed in alternative embodiments. For example, these electrodes may be placed in at least one of motor cortex, premotor cortex, supplementary motor cortex, other motor cortical areas, somatosensory cortex, other sensory cortical areas, Wemicke&#39;s area, Broca&#39;s area, other cortical region, other intracranial region, and other extracranial region. 
     In the illustrative embodiment, an intracranial catheter  7  is provided to mechanically support and facilitate electrical connection between intracranial and extracranial structures. In this embodiment, intracranial catheter  7  contains one or more wires connecting extracranial stimulating and recording circuit  26  to the intracranial electrodes, including but not limited to, intracranial stimulating electrodes  1 - 4  and intracranial recording electrodes  5 ,  6 . The wires contained within intracranial catheter  7  transmit stimulating electrode output signal (SEOS) to intracranial stimulating electrode array  37 . Such wires additionally transmit stimulating electrode input signal (SEIS) and recording electrode input signal (REIS), from intracranial stimulating electrode array  37  and intracranial recording electrode array  38  respectively, to stimulating and recording circuit  26 . 
     Stimulating and recording circuit  26  is protected within a circuit enclosure  44 . Circuit enclosure  44  and contained components, including stimulating and recording circuit  26  comprise stimulating and recording unit  43 . It should be understood that more or fewer of either type of electrode as well as additional electrode types and locations may be incorporated or substituted without departing from the spirit of the present invention. Furthermore, stimulating and recording circuit  26  can be placed extra cranially in a subclavian pocket as shown in  FIG. 1 , or it may be placed in other extracranial or intracranial locations. 
     Connecting cable  8  generally provides electrical connection between intracranial or intracranial locations. A set of electrical wires provides the means for communication between the intracranial and extracranial components; however, it should be understood that alternate systems and techniques such as radiofrequency links, optical (including infrared) links with transcranial optical windows, magnetic links, and electrical links using the body components as conductors, may be used without departing from the present invention. Specifically, in the illustrative embodiment, connecting cable  8  provides electrical connection between intracranial components  246  and stimulating and recording circuit  26 . In embodiments wherein stimulating and recording circuit  26  has an intracranial location, connecting cable  8  would likely be entirely intracranial. Alternatively, connecting in embodiments wherein stimulating and recording circuit  26  is implanted under scalp  10  or within or attached to calvarium  9 , connecting cable  8  may be confined entirely to subcutaneous region under the scalp  10 . 
     A catheter anchor  29  provides mechanical connection between intracranial catheter  7  and caldarium  9 . Catheter anchor  29  is preferably deep to the overlying scalp  10 . Such a subcutaneous connecting cable  8  provides electrical connection between intracranial electrodes  246  and stimulating and recording circuit  26 . Cable  8  may also connect any other sensors, including but not limited to any of sensory input modalities  247 , or other stimulating electrodes, medication dispensers, or actuators with stimulating and recording circuit  26 . 
     Sensory feedback is provided to recording and stimulating unit  26  from a multiplicity of sensors, collectively referred to as sensory input modalities  247 . Intracranial recording electrode array  38 , previously described, is intracranial in location. Additional sensors, most of which are located extracranially in the preferred embodiment, comprise the remainder of sensory input modalities  247 . Sensory input modalities  247  provide information to stimulating and recording unit  26 . As will be described in greater detail below, such information is processed by stimulating and recording unit  26  to deduce the disease state and progression and its response to therapy. 
     In one embodiment of the invention, a head-mounted acoustic sensor  11  is used to monitor any number of vibratory characteristics such as high frequency head vibration, muscle vibration, and/or speech production. Head-mounted acoustic sensor  11  is connected to stimulating and recording circuit  26  with an acoustic sensor connecting cable  30 . 
     A head-mounted accelerometer  12  is implemented in certain embodiments of the present invention to monitor head movement and position with respect to gravity. Head-mounted accelerometer  12  may be mounted to any structure or structures that enables it to accurately sense a desired movement. Such structures include, for example, the skull base, caldarium, clavicle, mandible, extraocular structures, soft tissues and vertebrae. Head-mounted accelerometer  12  is connected to stimulating and recording circuit  26  with an accelerometer connecting cable  31 . 
     A proximal electromyography (EMG) electrode array  45  is also included in certain preferred embodiments of the invention. Proximal EMG electrode array  45  includes a positive proximal EMG electrode  13 , a reference proximal EMG electrode  14 , and a negative proximal EMO electrode  15 . As one skilled in the relevant art would find apparent, proximal EMO electrode array  45  may include any number of type of electrodes. Proximal EMO electrode array  45  is implanted in or adjacent to muscle tissue. In the embodiment illustrated in  FIG. 1 , proximal EMO electrode array  45  is shown implanted within the neck of the human patient. However, it should be understood that this location is illustrative only and that proximal EMO electrode array  45  may be implanted in or adjacent to any muscle without departing from the spirit of the present invention. 
     A proximal acoustic sensor  27  may also be implemented in the present invention. Proximal acoustic sensor  27  senses muscle vibration and may be used to augment, supplement or replace EMG recording. Also, a proximal accelerometer  28  may be used to sense movement, including tremor and voluntary activity, and orientation with respect to gravity. Proximal connecting cable  16  provides electrical connection from the proximal EMG electrodes  14  and  15 , proximal acoustic sensor  27 , and proximal accelerometer  28  to stimulating and recording circuit  26 . In the illustrative embodiment, these sensors are shown connected to a common proximal connecting cable  16 . However, in alternative embodiments, this configuration may include the use of multiple connecting cables or implement other types of communication media without departing from the present invention. It should also be understood from the preceding description that the number of each type of sensor may also be increased or decreased, some sensor types may be eliminated, and other sensor types may be included without departing from the spirit of the present invention. 
     A distal EMG electrode array  47  may also be included in certain embodiments of the present invention. In such embodiments, distal EMG electrode array  47  typically includes a positive distal EMG electrode  17 , a reference distal EMG electrode  42 , and a negative distal EMG electrode  18 . Positive distal EMG electrode  17  is connected to stimulating and recording circuit  26  by positive distal EMG connecting cable  20 . Negative distal EMG electrode  18  is connected to stimulating and recording circuit  26  by negative distal EMG connecting cable  21 . Reference distal EMG electrode  42  is connected to stimulating and recording circuit  26  by reference distal EMG connecting cable  48 . 
     In other embodiments, a distal acoustic sensor  19  is connected to stimulating and recording circuit  26  by distal acoustic connecting cable  22 . Distal accelerometer  33  is connected to stimulating and recording circuit  26  by distal accelerometer connecting cable  34 . Distal accelerometer  33  is connected to stimulating and recording circuit  26  by distal accelerometer connecting cable  34 . 
     In the embodiment illustrated in  FIG. 1 , distal EMG electrode array  47 , distal acoustic sensor  19 , and distal accelerometer  33  are shown located in the shoulder region. However, the distal EMG electrode array  47  may be located in other locations, including, for example, the masseter, temporalis, stemocleidomastoid, other portion of the head and neck, pectoralis, torso, abdomen, upper extremities, lower extremities, and other locations. The number of each type of sensor may be increased or decreased, some sensor types may be eliminated, and other sensor types may be included without departing from the spirit of the present invention. 
     An enclosure-mounted EMG electrode array  46  is illustrated in  FIG. 1 . Enclosure-mounted EMG electrode array  46  includes enclosure-mounted positive EMG electrode  23 , enclosure-mounted negative EMG electrode  24  and enclosure-mounted reference EMG electrode  25 , all of which are attached to the circuit enclosure  44  that encloses stimulating and recording unit  26 . The circuit enclosure  44  is preferably included to provide robustness against potential lead entanglement and fracture. In one particular embodiment, circuit enclosure  44  is constructed of titanium and epoxy, or other single or combination of bio-compatible materials. Enclosure-mounted acoustic sensor  35  and enclosure-mounted accelerometer  36  are mounted to stimulating and recording unit  43 . The number of each type of sensor may be increased or decreased, their locations changed, some sensor types eliminated, and other sensor types included without departing from the spirit of the present invention. 
     In the embodiment illustrated in  FIG. 1 , EEG electrodes  39 ,  40 ,  41  are provided. The EEG electrodes may be mounted directly to connecting cable  8  or may be connected via intermediate cables. Any one of the numerous standard and new electrode configurations, or montages, may be employed in EEG electrodes  39 - 41  without departing from the present invention. 
     In one embodiment, a proximal peripheral nerve electrode array  98  is connected to stimulating and recording circuit  26  by proximal peripheral nerve electrode array connecting cable  100 . Proximal peripheral nerve electrode array  98  is shown located in the neck region. In this location proximal peripheral nerve electrode array  98  can interface with the vagus nerve, spinal accessory nerve, or nerve arising from cervical roots. 
     A distal peripheral nerve electrode array  99  is connected to stimulating and recording circuit  26  by distal peripheral nerve electrode array connecting cable  32 . Distal peripheral nerve electrode array  99  is shown located by the proximal arm, in position to interface with the brachia) plexus or proximal arm nerve. One or more of these peripheral nerve electrode arrays may be implanted in these or other locations, including but not limited to the head, cranial nerves, neck, torso, abdomen, upper extremities, and lower extremities, without departing from the present invention. 
     In one preferred embodiment, the peripheral nerve electrode arrays are each comprised of three epineural platinum-iridium ring electrodes, each in with an internal diameter approximately 30% larger than that of the epineurium, longitudinally spaced along the nerve. Electrodes of differing dimensions and geometries and constructed from different materials may alternatively be used without departing from the present invention. Alternative electrode configurations include but are not limited to epineural, intrafascicular, or other intraneural electrodes; and materials include but are not limited to platinum, gold, stainless steel, carbon, and other element or alloy. 
       FIG. 2  is an architectural block diagram of one embodiment of the neurological control system  999  of the present invention for modulating the activity of at least one nervous system component in a patient. As used herein, a nervous system component includes any component or structure comprising an entirety or portion of the nervous system, or any structure interfaced thereto. In one preferred embodiment, the nervous system component that is controlled by the present invention includes the globus pallidus internus. In another preferred embodiment, the controlled nervous system component is the subthalamic nucleus. 
     The neurological control system  999  includes one or more implantable components  249  including a plurality of sensors each configured to sense a particular characteristic indicative of a neurological or psychiatric condition. One or more intracranial (IC) stimulating electrodes in an IC stimulating electrode array  37  delivers a neural modulation signal to the same or other nervous system component as that being monitored by the system  26 . One or more sensors  38 ,  51 ,  52 ,  53 , and  54  sense the occurrence of neural responses to the neural modulation signals. Stimulating and recording unit  26  generates the neural modulation signal based on the neural response sensed by the sensors. 
     The neurological control system  999  preferably also includes a patient interface module  55  and a supervisory module  56 . A control circuit  72  (described below) is communicably coupled to the patient interface module  55  and receives signal inputs from and provides signal outputs to patient interface module  55  and supervisory module  56 . In one preferred embodiment, patient interface module  55  and supervisory module  56  remain external to the body of the patient. However either of these devices may be connected via percutaneous leads or be partially or totally implanted without departing from the present invention. 
     Patient interface module  55  and supervisory module  56  facilitate adjustment of control parameters, monitoring of disease state, monitoring of response to therapy, monitoring of stimulating and recording circuit  26 , monitoring of impedance and other characteristics of intracranial stimulating electrode array  37 , monitoring of physiologic parameters, monitoring of vital signs, monitoring of any other characteristic or function of components of the present invention, including but not limited to the stimulating and recording circuit  26 , stimulating and recording unit  43 , circuit enclosure  44 , EMG electrode array  50 , EEG electrode array  51 , accelerometer array  52 , acoustic transducer array  53 , peripheral nerve electrode array  54 , and intracranial recording electrode array  38 . Such monitoring and adjustment is accomplished through the use of any well known bi-directional communication between control circuit  72  and supervisory module  56 . In one preferred embodiment, a radio frequency link is employed. In alternative embodiments, other communication technologies, including but not limited to optical, percutaneous, or electromagnetic, may be used. 
     In one preferred embodiment, patient interface module  55  and supervisory module  56  are placed adjacent to the patients garments overlying the implanted stimulating and recording unit  43 . When neurological control system  999  is turned on in this position, a communications handshaking protocol is executed. Communication handshaking routines are known to those or ordinary skill in the art, and they enable establishment of a communication rate and protocol and facilitate mutual identification of devices. Patient interface module  55  automatically downloads parameters from stimulating and recording circuit  26  and stores values of such parameters in a memory. When the transfer of these parameter values is complete, patient interface module  55  emits a audible signal such as a series of beeps, and the patient turns off patient interface module  55  and removes it from its position overlying the implanted stimulating and recording unit  43 . Parameter values may then be retrieved by the patient by a routine including but not limited to a menu driven interface, and the values may be transmitted via telephone conversation or other communication method to a health care professional. Supervisory module  56  operates in the same manner with one addition; a step is provided during which the health care professional may upload parameters to stimulating and recording circuit  26  to alter its function including by means of changing parameters including but not limited to control laws gains and thresholds, filter parameters, signal processing parameters, stimulation waveform modes (including at least one of current regulated, voltage regulated, frequency regulated, or pulse width regulated), and stimulation waveform parameters. 
     Control laws, well known to those of ordinary skill in the field of control theory, are defined by a set of parameters specific to the particular control law. Common parameters include preset gains, threshold levels, saturation amplitudes, sampling rates, and others. Adaptive controllers change in response to the behavior of the system being controlled; as such, in addition to preset parameters, adaptive controllers possess a set of varying parameters. These varying parameters contain information indicative of the behavior of the system being controlled; downloading of these parameters provides one set of measures of the disease state and its response to therapy. 
     Such monitoring includes observation ohime history of disease state, stimulation parameters, response to therapy, and control law parameters, including time-varying adaptive controller parameters. Such adjustments includes modification of actual stimulation parameters and allowable ranges thereof, including but not limited to pulse width, pulse amplitude, interpulse interval, pulse frequency, number of pulses per burst frequency. Adjustments can further include modification of actual control law parameters and allowable ranges thereof, including but not limited to gains, thresholds and sampling rates of said stimulation waveforms. Signal processor  71  contains signal processor modules for each of the sensory input modalities  247 . Signal processing algorithms for each of the said sensory input modalities  247  may be independent. Additionally, signal processing algorithms the said sensory input modalities  247  may be coupled, such that the processing of one of the sensory input modalities  247  is dependent on another of the sensory input modalities  247 . Adjustments may additionally include modification of actual signal processor parameters and allowable ranges thereof, including but not limited to gains, filter cutoff frequencies, filter time constants, thresholds, and sampling rates. In a preferred embodiment, the stimulation and control law parameters are stored in at least one of random access memory and central processing unit registers (not shown). 
     It is anticipated that patient interface module  55  is to be used by the patient, a family member or associate, or home health care personnel to monitor the functions and performance of neurological control system  999 . In such an embodiment, the use of the patient interface module  55  is restricted to monitoring operations; adjustment of stimulation and control parameters is not enabled. However, adjustment of all or a subset of stimulation and control parameters (described below) may be facilitated by patient interface module  55  without departing from the present invention. Supervisory module  56 , on the other hand, is used by a physician or other health care personnel to monitor function and performance of neurological control system  999  and to adjust stimulation and control parameters. Control parameters controlled by patient interface module  55  and supervisory module  56  include allowable stimulation magnitude range, such as maximum combination of stimulation voltage, current, pulse width, pulse frequency, train frequency, pulse train count, pulse train duration. Control parameters may also include variables and constants used to define control laws implemented in control circuit  72 . Such control parameters include, but are not limited to, control law gains  197 - 203 , and other parameters for control laws, including but not limited to proportional controller  230 , differential controller  204 , integral controller  205 , nonlinear controller  206 , adaptive controller  207 , sliding controller  208 , model reference controller  209 , and other controllers. In addition, amplitudes for other controller parameters, including but not limited to amplitudes for controller weights  210 - 216  may be set by supervisory module  56 . Additionally, the parameters specifying the maximum amplitudes, or saturation values, may be set by supervisory module  56 . Control circuit  72  ( FIG. 12 ) will be described in detail below. 
     The majority of the computation accomplished by stimulating and recording circuit  26  is performed in signal conditioning circuit  76 , signal processor  71 , and control circuit  72 ; the algorithms and behavior of which are determined by corresponding sets of control parameters, of which some may be set by the supervisory module  56  and a typically more restricted set by patient interface module  55 . In one embodiment, control parameters further includes signal conditioning parameters. Signal conditioning parameters may include, for example, amplifier gains, filter gains and bandwidths, threshold values, and other parameters. In certain embodiments, control parameters additionally include signal processing parameters, including envelope determinator gains and time constants, filter passbands, filter gains, threshold values, integrator gains, analyzer parameters, disease state estimator parameters, artifact rejecter thresholds, envelope determinator time constants, rectifier parameters, spectral analyzer parameters and timer parameters. 
     In the illustrative embodiment described herein, control parameters further include spike detector  188  ( FIG. 9 ) parameters, spike characterizer  189  ( FIG. 9 ) parameters, spike analyzer  190  ( FIG. 9 ) parameters, spectral energy characterizer  192  ( FIG. 9 ) parameters, spectral energy analyzer  193  ( FIG. 9 ) parameters, aggregate disease state estimator  195  ( FIG. 10 ) parameters. 
     In accordance with the present invention, tremor are quantified and monitored by any sensors over time as indicators of disease state. Such sensors include but are not limited to EMG electrode array  50 , EEG electrode array  51 , accelerometer array  52 , acoustic transducer array  53 , peripheral nerve electrode array  54 , intracranial recording electrode array  38 , and intracranial stimulating electrode array  37 . In one particular embodiment, the sensed tremor characteristics include, but are not limited to, magnitude, frequency, duration and frequency of occurrence of tremors. Changes in these and other parameters are compared to current levels of, and changes in, treatment parameters. These changes are then used by aggregate disease state estimator  195  to estimate the response to therapy as functions of various electrical stimulation treatment parameters. Electrical stimulation treatment parameters are adjusted by control circuit  72  in real-time to provide optimal control of disease state. 
     Modulation parameters are optimized to achieve at least one of minimization of disease state, minimization of symptoms of disease, minimization of stimulation magnitude, minimization of side effects, and any constant or time-varying weighted combination of these. Patient interface module  55  and supervisory module  56  also preferably monitor the function and operation of other components of neurological control system  999 , including stimulating and recording unit  26  and implanted components  249 . 
     Stimulating and recording unit  26  receives and processes signals generated by implanted components  249  to provide conditioned signals  78 - 84  to a signal processor  71 . For each type of implanted components  249  coupled to stimulating and recording unit  26 , signal conditioning circuit  76  preferably includes an associated amplifier and filter. Each amplifier and associated filter is configured to receive and process the signal generated by the associated one of the set of sensors  38 ,  51 ,  52 ,  53 , and  54 . 
     In the illustrative embodiment, implanted components  249  include an electromyography (EMG) electrode array  50  which generate EMG signals. Preferably, EMC electrode array  50  comprises of all EMG electrodes implemented in the particular embodiment of the present invention. These include, in the exemplary embodiment illustrated in  FIG. 1 , proximal EMG electrode array  45 , enclosure-mounted EMG electrode array  46  and distal EMG electrode array  47 . Array  50  may also include, for example, EMG electrodes implanted in the head or other location, and surface EMG electrodes. 
     Implanted components  249  also include an electroencephalography (EEG) electrode array  51  which generate EEG signals and accelerometer array  52  which generates acceleration signals. EEG electrodes  39 ,  40 ,  41  illustrated in  FIG. 1  are representative of EEG electrode array  51 . EEG electrodes  39 - 41  may be mounted directly to connecting cable  8  or connected via intermediate cables. EEG electrode array  51  may include more or fewer elements than EEG electrodes  39 - 41  depicted; and any of numerous standard and new electrode configurations, or montages, may be employed without departing from the present invention. 
     Accelerometer array  52 , which produces well-known acceleration signals, preferably includes all accelerometers implemented in the patient associated with the present invention. For example, in the embodiment illustrated in  FIG. 1 , accelerometer array  52  includes head-mounted accelerometer  12 , proximal accelerometer  28 , enclosure-mounted accelerometer  36  and distal accelerometer  33 . Accelerometer array  52  may include more or fewer accelerometers than these accelerometers, and accelerometers of any types and locations may be employed without departing from the present invention. 
     Acoustic transducer array  53  includes all acoustic sensors utilized by the present invention. In the exemplary embodiment illustrated in  FIG. 1 , acoustic transducer array  53 , includes head-mounted acoustic sensor  11 , proximal acoustic sensor  27 , enclosure-mounted acoustic sensor  35  and distal acoustic sensor  19 . It should be understood that acoustic transducer array  53  may include more or fewer elements than said acoustic sensors listed above; and any of numerous acoustic sensor types and locations may be employed without departing from the present invention. 
     Peripheral nerve electrode array  54  generates peripheral neural signals, including but not limited to efferent and afferent axonal signals. Preferably, peripheral nerve electrode array  54  includes all peripheral nerve electrodes implemented in present invention. For example, in the illustrative embodiment illustrated in  FIG. 1 , peripheral nerve electrode array  54  includes proximal peripheral nerve electrode array  98  and distal peripheral nerve electrode array  99 . The single or plurality of individual peripheral nerve electrode arrays which comprise peripheral nerve electrode array  54  may be implanted in the illustrated or other locations, as noted above. 
     Intracranial (IC) recording electrode array  38  generates central neural signals, including but not limited to cortical, white matter, and deep brain nuclear signals. Neural activity to be sensed includes but is not limited to that found in the primary motor cortex, premotor cortex, supplementary motor cortex, somatosensory cortex, white matter tracts associated with these cortical areas, the globus pallidus internal segment, the globus pallidus external segment, the caudate, the putamen, and other cortical and subcortical areas. As one of ordinary skill in the relevant art will find apparent, the present invention may include additional or different types of sensors that sense neural responses for the type and particular patient. Such sensors generate sensed signals that may be conditioned to generate conditioned signals, as described below. One example of the placement of these electrodes is described above with reference to the embodiment illustrated in  FIG. 1 . Many others are contemplated by the present invention. 
     As noted, for each of the different types of sensors included in implanted components  249 , signal conditioning circuit  76  includes an associated amplifier and filter in the illustrative embodiment. Accordingly, signal conditioning circuit  76  includes an EMG amplifier  59  and filter  66 , each constructed and arranged to amplify and filter, respectively, the EMG signals received from EMG electrode array  50 . Similarly, signal conditioning circuit  76  also includes an EEG amplifier  60  and filter  67 , accelerometer (ACC) amplifier  61  and filter  68 , acoustic (ACO) amplifier  62  and filter  69 , peripheral nerve electrode (PNE) amplifier  63  and filter  70  and intracranial (IC) recording electrode (ICRE) amplifier  58  and filter  65 . 
     Simplifiers  57 - 63  may be single or multi-channel amplifiers depending upon the number of electrodes with which it interfaces. In one preferred embodiment, amplifiers  57 - 63  are physically located in the same enclosure as filters  64 - 70 ; that is, in a single signal conditioning circuit  76 . Preferably, signal conditioning circuit  76  is physically contained within stimulating and recording unit  102 . However, amplifiers  57 - 63  may be located separately from stimulating recording unit  102 . For example, amplifiers  57 - 63  may be affixed to or situated proximate to their associated electrode arrays  38 ,  50 - 54 . This arrangement facilitates the preamplification of the associated signals generated by the associated electrode arrays  38 ,  50 - 54 , increasing the signal-to-noise ratio of the signals. Amplifiers  57 - 63  may be any known voltage amplifier now or later developed suitable for amplifying the particular signals generated by their associated electrodes. 
     As noted, the amplified signals are passed to their associated filters  64 - 70  as shown in  FIG. 2 . As with amplifiers  57 - 59 , filters  64 - 70  may be physically separate from or incorporated into signal conditioning circuit  76  and stimulating and recording unit  26 . In one preferred embodiment, filters  64 - 70  are low pass filters having a cut-off frequency of, for example, 3,000 Hz. In alternative embodiments, filters  64 - 70  may include a notch filter to remove, for example, 60 Hz noise, or other types of filters appropriate for the type of signals generated by the associated sensors  38 ,  51 ,  52 ,  53 , and  54 . Selection of the appropriate frequencies for the cut-off and notch filter frequencies is considered to be well known in the relevant art and within the scope of the present invention. Filters  66 - 70 ,  65  and  64  generate conditioned sensed signals  84 ,  83  and  78 - 82 , respectively. 
     Signal processor  71  processes the conditioned sensed neural response signals  78 - 84  generated by signal conditioning circuit  76  in accordance with the present invention to determine neural system states. Signal processor  71  generally performs well known filtering operations in the time and frequency domains. In one preferred embodiment, the neural system states include one or more physiologic or disease states. Signal processor  71 , which can be implemented in a fast microprocessor, a DSP (digital signal processor) chip, or as analog circuitry, for example, is described in detail below. 
     Control circuit  72 , responsive to the signal processor  71 , patient interface module  55  and supervisory module  56 , adjusts the magnitude of a neural modulation signal in response to the sensed neural response. Signal processor  71  extracts relevant information from the sensed condition signals, and control circuit  72  uses this extracted information in the calculation of an output neuromodulation signal (NMS)  998 . Neuromodulation signal  998  subsequently travels along stimulator output path  111  to IC stimulating electrode array  37 . In one embodiment, control circuit  72  is a state machine, utilizing current and past system behavior in the calculation of a control signal. In an alternative embodiment, control circuit  72  includes an embedded microprocessor to process nonlinear control laws. Alternative embodiments of the control circuit  72  appropriate for the particular application may be also be used. 
     Control circuit  72  receives control law selection information, control law parameter information, stimulation waveform parameter range information, stimulation modulation mode, output stage regulation mode, and medication dose and timing information from patient interface module  55  and supervisory module  56 . The waveform parameter or parameters which are modulated by control law output signal U  997  are determined by the stimulation modulation mode; these parameters include but are not limited to pulse amplitude, pulse width, pulse frequency, pulses per burst, and burst frequency. Selection between regulation of pulse voltage or pulse current as the regulated pulse amplitude is determined by the output stage regulation mode. 
     Control circuit  72  provides stimulation waveform parameter history information, disease state history information, control law state variable history information, control law error history information, control law input variable history information, control law output variable history information, stimulating electrode impedance history information, sensory input history information, battery voltage history information, and power consumption history information to patient interface module  55  and supervisory module  56 . 
     Provision of stimulating electrode impedance history information allows monitoring of stimulating electrode performance and functionality. If an electrode is determined to be fractured, shorted, or encapsulated by fibrotic tissue, any of various control law parameters, output stage parameters, and waveform range parameters may be adjusted to allow compensation for these changes. Additionally, the Neuromodulation Signal (NMS)  998  may be delivered to different sets of electrodes to insure that it reaches neural tissue  250 . Sensory input history information allows evaluation of validity of any given sensory input. This is useful in determining the functionality of a given sensor and serves as an indicator for sensor replacement or adjustment of the signal processing parameters or algorithm or the control law parameters or algorithm to continue to generate reliable disease state estimate signals X and control law outputs U despite the loss of any particular individual or set of sensory signals. 
     Signal processor  71  receives amplifier gain setting information, filter parameter information, weighting information, and disease state estimator parameter and algorithm information from patient interface module  55  and supervisory module  56 . The function and operation of patient interface module  55  and supervisory module  56  are described above. As noted, patient interface module  55  may be used by the patient or home health care personnel to monitor disease state, stimulation parameters, and response to therapy. Limited adjustment of stimulation parameters and ranges is facilitated. Patient interface module  55  may be used by the patient or home health care personnel to provide information to the physician, avoiding the need for an office visit for the obtainment of said information. 
     Patient information module  55  queries signal processor  71  for present and time histories of monitored values. Time histories of selected variables in signal processor  71  and control circuit  72  are stored in memory module  240  for subsequent retrieval by patient interface module  55  and supervisory module  56 . Selected variables include but are not limited to disease state, tremor frequency, tremor magnitude, EMO magnitude, EMO frequency spectra (EMO magnitude within frequency ranges), and acceleration of limb, head, mandible, or torso. Selected variables may also include disease state, frequency spectra of limb, torso, and head movements, as determined by EMG and accelerometer signals. 
     Stimulating and recording unit  26  also includes an output stage circuit  77 . Output stage circuit  77  takes for an input the control law output signal U, which may be comprised of a single or multiplicity of channels or signals, from control circuit  72 . This control law output signal U  997  modulates the magnitude of the sequence of waveforms comprising the desired output neuromodulation signal (NMS.sub.D) which is produced by output stage circuit  77  and delivered via intracranial stimulating electrode array  37  to neural tissue  250 . 
     Output stage circuit  77  generates a neuromodulation signal (NMS.sub.D)  998  with a magnitude specified by control law output signal U  997  received from control circuit  72 . In one preferred embodiment, the waveform parameter of the desired output neuromodulation signal (NMS.sub.D) which is modulated by control law output signal U is the stimulation current magnitude. The capability to specifically modulate the stimulation current confers efficacy resistance to perturbations or changes in electrode impedance. Presently implanted systems suffer from a decline in efficacy which results from an increase in electrode impedance which accompanies the normal tissue response to a foreign body, that is fibrotic encapsulation of the electrode. In this design taught in the present invention, a the magnitude of the current delivered to the neural tissue  250  will not vary as the electrode becomes encapsulated with fibrotic tissue or its impedance otherwise changes over time. A further advantage conferred by current modulation is the ability to monitor electrode impedance. If a current-modulated waveform, preferably a sinusoid, is delivered to the electrodes, and the resultant voltage potential waveform is concurrently monitored, the relative magnitudes and phase shifts of these waveforms may be computed. From these magnitudes and phases, the complex impedance and hence the resistive and capacitive components of the electrode impedance may be calculated. 
     In an alternative embodiment, the waveform parameter of the desired output neuromodulation signal (NMS.sub.D) which is modulated by control law output signal U  997  is the stimulation voltage magnitude. This design would not enjoy the independence of the stimulation current and efficacy from impedance variation enjoyed by the embodiment described above. If fibrosis was uneven around the surface of the electrode, this embodiment would avoid potentially undesirably large current densities along narrow tracts of remaining low resistance unfibrosed regions of neural tissue  250 . 
     Alternatively, regulation of stimulus pulse width may be desired. In certain circuit implementations, the available resolution or bits for specifying the magnitude of pulse width may be greater than that for specifying the pulse voltage or current. In such a case, if finer control of the magnitude of Neuromodulation signal (NMS)  998  is desired than is provided by the control of pulse current or pulse voltage, then it may be desirable to modulate the pulse width. Furthermore, the spatial neuron recruitment characteristics of a pulse width modulated neuromodulation signal (NMS)  998  may provide a more linear, predictable, or controllable response than that obtained with current or voltage modulation. Selection between regulation of pulse voltage, pulse current, or pulse width as the regulated pulse amplitude parameter is determined by the output stage regulation mode, which may be set using supervisory module  56 . In alternative embodiments, the modulation of pulse frequency and the modulation of the number of pulses per burst are regulated. As one of ordinary skill in the relevant art would find apparent. Other such characteristics may be regulated in addition to or instead of the ones noted above. 
     Output stage circuit  77  includes a pulse generator  73 , an output amplifier  74  and a multiplexor  75 . Pulse generator  73  generates one or more stimulus waveforms, each of which is characterized by several parameters, including but not limited to pulse amplitude, pulse width, pulse frequency, number of pulses per burst, and burst frequency. As noted above, pulse amplitude may comprise pulse voltage or pulse current. Preferably, each of these parameters may be independently varied, as specified by control law output signal U  997  generated by control circuit  72 . As noted, the stimulus waveforms comprising the neuromodulation signal (NMS) generated by output stage circuit  77  are applied to patient through intracranial (IC) stimulating electrode array  37 . Pulse generator  73  generates a single waveform when single channel stimulation is to be used, and a plurality of waveforms when multiple channel stimulation is to be used. It may generate monophasic or biphasic waveforms. 
     In one preferred embodiment, charge balanced biphasic waveforms are produced. Those skilled in the art are aware that the net charge contained in a given pulse is given by the time integral of the stimulus current over the duration of the pulse. In a biphasic configuration, a pair of pulses of opposite polarity is generated, and the pulse current amplitude and pulse width are chosen such that the charge amplitude is equal in magnitude and opposite in polarity. In some cases, it is desirable for the pulses comprising the biphasic pulse pair to have different amplitudes; in this case, the pulse widths are chosen to insure equal and opposite charges so the pulse par introduces zero net charge to the neural tissue  250 . The capability to deliver pulse pairs with balanced charges is yet a further advantage conferred by the current regulation mode described above. 
     Even though the waveform parameters of the pulse pairs are calculated to deliver a zero net charge, in practice, noise and precision limitations in computation and resolution limitations and nonlinearities in the digital to analog conversion and amplification stages may result in slight imbalances in the pulse pair charges. Over time, this can result in the delivery of a substantial accumulated net charge to the neural tissue. To eliminate this potential for net charge delivery to neural tissue, a direct current (DC) blocking capacitor is employed. This is a technique that is well known to those or ordinary skill in the art. In one preferred embodiment, a DC blocking capacitor is included within multiplexor  75  in series with stimulator output path  111 . 
     Typically, multi-channel stimulation is used in the case of bilateral stimulation. Since the disease progression is typically asymmetrical, and the normal motor control systems governing movement on the left and right side of the body are also highly independent of each other, the delivery of treatment to the left and right sides of the body should be controlled separately. This represents one need for a multiple channel neuromodulation signal (NMS)  998 . Multichannel stimulation is also expected to be beneficial in treating patients with variable involvement of different limbs. For example, the magnitude neuromodulation of a portion of the globus pallidus required to achieve optimal controls of arm tremor may be different from the optimal level of neuromodulation of separate portion of the globus pallidus to achieve optimal control of leg tremor. In this case, separate electrodes or electrode pairs are required to deliver optimal levels of neuromodulation to control tremor in these two regions of the body. Correspondingly, these separate electrodes or electrode pairs will be driven by separate neuromodulation signal (NMS) channels, necessitating a multichannel system. 
     A further need for multichannel neuromodulation signal (NMS) is the control of multiple symptoms of the movement disorder and the side effects arising from pharmacologic treatment. Optimal control of tremor, dyskinesias, and rigidity are not achieved by modulation of the same site at the same intensity. For this reason, multiple and separately controlled channels of neuromodulation are required to simultaneously achieve optimal control of these multiple symptoms and side effects. Each of these symptoms and side effects may be considered to comprise one or more element in a multivariable disease state. A multivariable control system will be required to optimally drive each of these disease state elements to its desired value, ideally toward a target minimum level and thus achieve optimal control of this multiplicity of disease states. This multivariable control system may be implemented as multiple independent control laws each with separate though potentially overlapping sensory inputs or as a multivariable control law matrix. 
     Stimulation via each of the multiple channels comprising the neuromodulation signal (NMS)  998  is characterized by separate though possibly overlapping sets of one or more of the following parameters: stimulation voltage, stimulation current stimulation frequency of pulses within the same burst, frequency of bursts, pulse width, pulses per burst, duration of burst, and interpulse interval. The stimulus waveforms are amplified by output amplifier  74  to generate an amplified stimulus waveform. Specifically, pulse generator  73  transfers information to output amplifier  74  which includes information that uniquely specifies the desired stimulation waveform. In a preferred embodiment, the information is in the form of an analog signal which represents a scaled version of the voltage or current waveform to be delivered to the tissue. It should be understood that other forms of the signal generated by pulse generator  73  may be used, including combinations of at least one of analog and digital representations. Output amplifier  74  performs amplification and regulation of the received stimulus waveform generated by the pulse generator  73 . This may be regulation of electrical current to achieve desired voltage or regulation of electrical voltage to achieve desired current, depending on whether a voltage or current waveform is to be delivered to the nervous system component. 
     As one skilled in the relevant art would find apparent, voltage regulation is simpler to implement, and is a technique which is commonly used by many conventional stimulators. Current regulation, on the other hand, is more complex but allows for more precise control of the applied stimulation. Current regulation insures that a specified amount of current is delivered, regardless of the impedance of the electrode. Current regulation is advantageous in that it allows for precise control of stimulation level despite changes in electrode impedance which invariably occur over time. Since electrode impedances often change, typically increasing as they become encapsulated by fibrosis, current regulation is preferred to avoid the decrease in current which would occur if voltage regulation were to be used in such circumstances. 
     The amplified stimulus waveform generated by output amplifier  74  is conducted along stimulator amplifier output path  112  to multiplexor  75 . Multiplexor  75  allows for delivery of a stimulating electrode output signal (SEOS) to the intracranial stimulating electrode array  37 , multiplexed with sensing of a stimulating electrode input signal (SEIS). Specifically, multiplexor  75  serves to alternately connect intracranial stimulating electrode (ICSE) array  37  to output amplifier  74  and intracranial stimulating electrode amplifier  57 . Connection of intracranial stimulating electrode (ICSE) array  37  to output amplifier  74  facilitates delivery of neural modulation signal to neural tissue, while connection of intracranial stimulating electrode (ICSE) array  37  to intracranial stimulating electrode amplifier  57  facilitates monitoring of neural activity in the region being stimulated. 
     Multiplexor  75  allows delivery of neural modulation signals to neural tissue concurrent with monitoring of activity of same neural tissue; this facilitates real-time monitoring of disease state and response to treatment. Stimulating electrode output signal (SEOS) from output amplifier  74  is conducted along stimulator amplifier output path  112  to multiplexor  75 . Multiplexor  75  conducts output from output amplifier  74  to stimulator output path  111  which conducts the stimulating electrode output signal to intracranial stimulating electrode array  37 . To facilitate periodic sampling of neural activity in tissue being stimulated, multiplexor  75  alternatively conducts signal arising from stimulated tissue via intracranial stimulating electrode array (ICSE)  37  and stimulator output path  111  to multiplexed stimulator recording input path  113  and intracranial stimulating electrode amplifier  57 . 
     Multiplexor  75  selectively conducts the signal on multiplexed stimulator recording input path  113  to amplifier  57 . Multiplexor  75  may alternate conduction between path  111  and path  112  or path  113  using temporal multiplexing, frequency multiplexing or other techniques to allow concurrent access to the intracranial stimulating electrode (ICSE) array  37  for modulation of tissue activity and monitoring of tissue activity. Temporal multiplexing is a well known technique and frequency multiplexing of stimulation and recording signals in known to those skilled in the art. In this embodiment, temporal multiplexing is accomplished by alternately connecting stimulator output path  111  to stimulator amplifier output path  112  and multiplexed stimulator recording input path  113 . In one embodiment, frequency multiplexing is accomplished by passing a band-limited portion of stimulating electrode output signal SEOS via the stimulator output path  111  to intracranial stimulating electrode array  37  while simultaneously monitoring activity on intracranial stimulating electrode array  37  within a separate frequency band, thereby generating a stimulating electrode input signal SEIS. Thus, stimulating electrode input signal SEIS is conducted from the intracranial stimulating electrode array  37  to stimulator output path  111  to multiplexor  75  and via multiplexed stimulator recording input path  113  to intracranial stimulating electrode array amplifier  57 . 
     Multiplexor  75  facilitates conduction between stimulator amplifier output path  112  and multiplexed stimulator recording input path  113  to allow automated calibration. In this mode, a calibration signal of known amplitude is generated by pulse generator  73  and amplified by output amplifier  74  which, for calibration purposes, delivers a voltage regulated signal via stimulator amplifier output path  112  to multiplexor  75 . Multiplexor  75  conducts amplified calibration signal to multiplexed stimulator recording input path  113  which conducts signal to intracranial stimulating electrode amplifier  57 . 
     Although not included in the illustrative embodiment, multiplexed or intermittent connection of stimulator amplifier output path  112  to the inputs of at least on of the other amplifiers, including EMG amplifier  59 , EEG amplifier  60 , accelerometer amplifier  61 , acoustic amplifier  62 , peripheral nerve electrode amplifier  63 , and intracranial recording electrode amplifier  58 , may be implemented without departing from the present invention. The same multiplexed connections may be used to calibrate the pulse generator  73  and output amplifier  74 . 
     Referring to  FIG. 15 , an analog switch may be used to connect one or an opposing polarity pair of Zener diodes across the noninverting and inverting inputs of intracranial recording electrode amplifier  58 . In this configuration, the Zener diodes would limit the maximal amplitude of the calibration signal in one or both polarities to known values, allowing for accurate calibration of intracranial recording electrode amplifier  58 . The analog switch may then be deactivated, removing the cathode of the single or pair of Zener diodes from the input of intracranial recording electrode amplifier  58  to allow measurement of stimulating electrode output signal (SEOS) for calibration of pulse generator  73  and output amplifier  74 . This is described in greater detail below. 
     Multiplexor  75  also facilitates conduction between stimulator amplifier output path  112 , multiplexed stimulator recording input path  113 , and stimulator output path  111  to allow measurement of impedances of components of intracranial stimulating electrode array  37 . In this electrode impedance measurement mode, a three way connection between stimulator amplifier output path  112 , multiplexed stimulator recording input path  113 , and stimulator output path  111  is created. When output amplifier  74  is operated in current regulated mode, it delivers an SEOS of known current via stimulator output path  111  to intracranial stimulating electrode array  37 . The voltages generated across the elements of intracranial stimulating electrode array  37  generally are the products of the electrode impedances and the known stimulating currents. These voltages are sensed as the stimulating electrode input signal SEIS by the intracranial stimulating electrical amplifier  57 . 
     Reference module  1116  contains memory registers in which control law reference values are stored. Such reference values include but are not limited to target disease state levels, target symptom levels, including target tremor level, and threshold levels. Threshold levels include but are not limited to disease and symptom levels, including tremor threshold levels. Neural modulation amplitude may be increased when at least one of disease state and symptom level exceed the corresponding threshold. Similarly neural modulation amplitude may be decreased or reduced to zero when either the disease state or symptom level falls below the corresponding threshold. 
     Reference module  116  is connected to patient interface module  55 , facilitating both monitoring and adjustment of reference values by patient. Reference module  116  is also connected to supervisory module  56 , facilitating both monitoring and adjustment of reference values by physician or other health care provider. Supervisory module  56  may be used by the neurologist, neurosurgeon, or other health care professional, to adjust disease state reference R values for the one or more control laws implemented in control circuit  72 . The disease state reference R values specify the target level at which the corresponding disease states are to be maintained, as quantified by the disease state estimate X values, providing reference values for control laws implemented in control law circuit block  231  ( FIG. 11 ; discussed below) and contained within control circuit  72 . Reference module  116  may also receive input from control circuit  72 , facilitating the dynamic adjustment of reference disease state “r” (discussed below). Reference module  116  may additionally receive input from disease state estimator module array (DSEMA)  229  ( FIG. 11 ; discussed below) and aggregate disease state estimator  195  ( FIG. 1  discussed below) and components of signal processor  71 , for use in dynamically determining reference disease state “r”. 
       FIG. 10  is a schematic diagram of signal processor  71 . In this illustrative embodiment, signal processor  71  includes a disease state estimator module array  229  that includes one or more signal processor modules that generate a quantitative estimate of at least one disease state or parameter thereof based upon its respective input. For example, magnitude of tremor in the 3 to 5 Hz range represents one possible representation of a disease state. This could be an absolute or normalized quantification of limb acceleration in meters per second squared. This component of the disease state would be calculated almost exclusively from sensory feedback from accelerometer array  52 . Another possible disease state is the frequency of occurrence of episodes of tremor activity per hour. This element of the disease state may be estimated from any of several of the sensory feedback signals. In this case, the most accurate representation of this disease state element is obtained by applying a filter such as a Kalman filter to calculate this parameter based upon a weighted combination of the sensory feedback signals. Such weighting coefficients are calculated from quantified measures of the accuracy of and noise present upon each sensory feedback channel. 
     In the illustrative embodiment, disease state estimator module array  229  includes an EMG signal processor  233 , EEG signal processor  234 , accelerometer signal processor  235 , acoustic signal processor  236 , peripheral nerve electrode (PNE) signal processor  237 , intracranial recording electrode (ICRE) signal processor  238 , and intracranial stimulating electrode (ICSE) signal processor  239 . It should be understood that other signal processors may also be included in the array  229 . Inputs to these modules include conditioned EMG signal path  78 , conditioned EEG signal path  79 , conditioned accelerometer signal path  80 , conditioned acoustic signal path  81 , conditioned peripheral nerve electrode (PNE) signal path  82 , conditioned intracranial recording electrode (ICRE) signal path  83 , and conditioned intracranial stimulating electrode (ICSE) signal path  84 , respectively. Communication between these modules is facilitated. The output(s) of each of the modules is connected to an aggregate disease state estimator  195 . Aggregate disease state estimator  195  generates a single or plurality of disease state estimates “X” indicative of state of disease and response to treatment. 
     In the preferred embodiment, the acceleration of at least one of the affected limb and the head, each of which is sensed as a sensory feedback channel by an element of the accelerometer array  52 , serves as respective elements in the disease state estimate X. These elements of disease state estimate X are inputs to respective control laws implemented in control circuit  72 . of input to the control law. A control law governing the function of a proportional controller using acceleration as its single sensory feedback channel is given by equation (1): 
         u   1 =0.3166(V*S 2 /m)* ACC   (1)
 
       and if 
         u   2 =0.6333(V*S 2 /m)* ACC   (2)
 
     where u 1  and u 1  are the stimulation voltage given in volts; and ACC is the limb, mandible, or head acceleration given in meters per second squared (m/s 2 ). 
     In equation (1), the stimulation site is the ventroposterolateral pallidum, the output stage mode is voltage regulated, the waveform is a continuous train of square waves, the amplitude u  1  is given in volts (typically approximately 1 volt), and the remaining stimulation parameters include a pulse width of 210 microseconds, and a stimulation frequency of 130 Hz. In equation (2), the stimulation site is the ventral intermediate thalamic nucleus (Vim), the output stage mode is voltage regulated, the waveform is an intermittent train of square waves with an on time of 5 minutes and an off time of 45 seconds, the amplitude u 2  is given in volts (typically approximately 3 volts), and the remaining stimulation parameters include a pulse width of 60 microseconds, and a stimulation frequency of 130 Hz. 
     In one preferred embodiment, the ACC signal represents the average acceleration over a finite time window, typically 15 to 60 seconds. This effective lowpass filtering provides a stable sensory feedback signal for which a proportional control law is appropriate. If stability and performance requirements dictate, as is familiar to those practiced in the art of feedback control, other components, including an integrator and a differentiator may be added to the control law to produce a proportional-integral-differential (PIO) controller, as needed. 
     One preferred embodiment also includes electromyographic (EMG) signals as sensory feedback in the calculation of at least one element of the disease state estimate X which is an input to the control law. As discussed in the section describing EMG signal processor  233 , the EMG signals are rectified by full wave rectifier  123 , passed through envelope determiner  124 , passed through several bandpass filters  125 ,  127 ,  129 ,  131 ,  133  and associated threshold discriminators  126 ,  128 ,  130 ,  132 ,  134  and then passed in parallel to each of integrator  135  and counter  136 . Integrator  135  generates an output which is a weighted function of it inputs and represents the average magnitude of tremor activity over a given time window −w/2 to +w/2. A simplified representation of this is given by equation (3): 
         u   3 =∫ −w/2   w/2   X   EMG   dt   (3)
 
     over a given time window −w/2 to +w/2. A simplified representation of this is given by the equation: 
     As is familiar to those skilled in the art of control theory, an integral controller is marginally stable. To confer stability to this control law, the equivalent of a finite leak of the output magnitude u.sub.4 to zero is added to maintain stability. A more general form of this equation is given by equation (4): 
       − C   1 ∂ u     4     /dt+C   2   *u   4   =B   1   *∂X   EMG   /dt+B   2   *X   EMG   (4)
 
     Shown as a system function, the control law output U is given as the product of a transfer function H(s) and the disease estimate X, the input to the control law: 
         u ( s )( C   1   ∘s+C   2 )= X   EMG ( s )( B   1   ∘s+B   2 )  (5)
 
         u ( s )/ X   EMG ( s )=(81° s+ 82)/( C 1° s+C 2)  (6)
 
         H ( s )= u ( s )/ X   EMG ( s )=( B   1   ∘s+B   2 )/( C   1   ∘s+C   2 )  (7)
 
     One such control law with an appropriate time response is given by: ° 
         H ( s )= u ( s )/ X   EMG ( s )= G   V/EMG (0.1∘ s+ 1)/(2∘ s+ 1)  (8)
 
     where G V/EMG  is the gain in neuromodulation signal (NMS) (volts per volt of EMG signal). 
     For intramuscular EMG electrodes, signal amplitudes are on the order of 100 microvolts. For neuromodulation signal (NMS) parameters of 2 volts amplitude, 60 microseconds pulse width, 130 Hz stimulation frequency, the appropriate overall gain G′ V/EMG  is 20,000 volts NMS /volts EMG . Since the preamplifier stage performs amplification, 1000, in the preferred embodiment, the actual value for G v/EMG  as implemented in the control law is 20 volts NMS /volts PREAMPL EMG . 
     Disease state estimator  195  determines estimates of disease state including but not limited to long-term, or baseline, components, circadian components, postprandial components, medication induced alleviation of components, medication induced components, and future predicted behavior of said components. Output of disease state estimator  195  includes output of observer  228 , depicted in  FIG. 11 , which makes use of an adaptive model of disease behavior to estimate disease states which are not directly detectable from sensors. Such sensors provide input to the adaptive model to correct state estimates and model parameters. Each of the signal processor modules in disease state estimator module array  229  are described below. 
       FIG. 3  is a block diagram of intracranial recording electrode (ICRE) signal processor  238  and intracranial stimulating electrode (ICSE) signal processor  239 , each of which are included within signal processor  71  in the illustrative embodiment illustrated in  FIGS. 2 and 10 . ICRE signal processor module  238  and ICSE signal processor module  239  process signals from one or more intracranial electrodes, including but not limited to those comprising intracranial recording electrode array  38  and intracranial stimulating electrode array  37 . As noted, intracranial stimulating electrode array  37  is comprised of one or more intracranial stimulating electrodes while intracranial recording electrode array  38  is comprised of one or more intracranial recording electrodes. 
     Input to ICRE signal processor  238  is conditioned intracranial recording electrode (ICRE) signal path  83  noted above. This input is connected to a spike detector  85  which identifies action potentials. Spike detection techniques are well known to those skilled in the art and generally employ low and high amplitude thresholds. Waveforms having amplitudes greater than the low threshold and lower than the high threshold are determined to be action potentials. These thresholds may be predetermined or adjusted manually using supervisory module  56  or may be adapted in real-time by an algorithm which sweeps the threshold through a range of values to search for values at which action potential spikes are consistently recorded. The low amplitude threshold is set above the amplitude of background noise and that of nearby cells not of interest, and the high amplitude threshold is set above the amplitude of the desired action potentials to allow their passage while eliminating higher amplitude noise spikes, such as artifacts arising from electrical stimulation currents. Bandpass, notch, and other filtering techniques may also be used to improve signal to noise ratio and the sensitivity and specificity of spike detectors. Individual neuron action potentials are usually recorded using fine point high-impedance electrodes, with impedances typically ranging from 1 to 5 megohms. Alternatively, larger lower-impedance electrodes may be used for recording, in which case the signals obtained typically represent aggregate activity of populations of neurons rather than action potentials from individual neurons. Spike detector  85  passes the waveform(s) to a spike characterizer  86 . Spike characterizer  86  determines firing patterns of individual neurons. The patterns include, for example, tonic activity, episodic activity, and burst firing. Spike characterizer  86  calculates parameters that characterize the behavior of the individual and groups of neurons, the activity of which is sensed by intracranial recording electrode array  38 . In one embodiment, the characterization includes parameterization of recorded action potentials, also referred to as spikes, bursts of spikes, and overall neural activity patterns. This parameterization includes, but is not limited to, calculation of frequencies of spikes, frequencies of bursts of spikes, inter-spike intervals, spike amplitudes, peak-to-valley times, valley-to-peak times, spectral composition, positive phase amplitudes, negative phase amplitudes, and positive-negative phase differential amplitudes. These parameters are depicted in  FIG. 14  and are discussed below. Based on these parameterization, spike characterizer  86  discriminates individual spikes and bursts originating from different neurons. This discrimination facilitates serial monitoring of activity of individual and groups of neurons and the assessment and quantification of activity change, reflective of change in disease state and of response to therapy. 
     A spike analyzer  87  receives as input the parameters from spike characterizer  86 . Spike analyzer  87  extracts higher level information, including but not limited to average spike frequencies, average interspike intervals, average amplitudes, standard deviations thereof, trends, and temporal patterning. By comparing current spike frequency rates to historical spike frequency data, spike analyzer  87  additionally calculates the rates of change of spike parameters. Prior trends and current rates of change may then be used to predict future behaviors. Rates of change of the parameters include but are not limited to autocorrelation and digital filtering. 
     Spike analyzer  87  may receive additional input from accelerometers, including but not limited to at least one of head mounted accelerometer  12 , proximal accelerometer  28 , enclosure mounted accelerometer- 36 , and distal accelerometer  33 . Spike analyzer  87  may receive indirect input from accelerometers, such as from conditioned or processed signals arising therefrom. This may include, for example, the signal transmitted by conditioned accelerometer signal path  80 . 
     Spike analyzer  87  may also receive additional input from EMG arrays  50 , such as a proximal EMG electrode array  45 , enclosure-mounted EMG electrode array  46 , or distal EMG electrode array  47 . Spike analyzer  87  may receive indirect input from such EMG electrode arrays  50 , such as from conditioned or processed signals arising therefrom, including but not limited to the signal transmitted by conditioned EMG signal path  78 . 
     These additional inputs from accelerometers and EMG arrays facilitates the characterization of neuronal firing patterns relative to activity of muscle groups and movement of joints, including but not limited to characterization of neuronal spike amplitudes and tuning of firing to movement, including but not limited to movement velocity and direction. The characterization may be used to assess functioning of the sensorimotor system, including but not limited to motor response time, and to measure the disease state and response to therapy. 
     Intracranial recording electrode (ICRE) single unit-based (SU) disease state estimator  88  receives input from spike characterizer  86  and/or spike analyzer  87 . Spike analyzer  87  provides higher level information, including but not limited to average spike frequencies, average interspike intervals, average amplitudes, standard deviations thereof, trends, and temporal patterning to disease state estimator  88 . These inputs are representative of the current neuronal activity in the tissue from which the intracranial recording electrodes (ICRE) are recording. ICRE SU disease state estimator  88  may also receive input representative of one or more signals, including desired neuronal activity, from control circuit  72 . The ICRE SU disease state estimate X ICRE   _   SU  calculated by ICRE SU disease state estimator  88 , may be comprised of a single or a plurality of signals, consistent with a representation of the disease state by a single or a multitude of state variables, respectively. The ICRE MU disease state estimate X ICRE   _   MU  calculated by ICRE MU disease state estimator  88 , may be comprised of a single or a plurality of signals, each representative of multiunit neurophysiological signals, i.e. reflective of concurrent activity of numerous neurons. Both ICRE SU disease state estimate X ICRE   _   SU  and ICRE MU disease state estimate X ICRE   _   MU  are output to aggregate disease state estimator  195 . 
     Referring to  FIG. 3 , conditioned intracranial recording electrode (ICRE) signal path  83  additionally connects to filter  101 . Filter  101  is preferably of the bandpass type filter. In one embodiment, the bandpass filter  101  has a passband of 0.1 to 100 Hz, although other ranges may be used. Output of filter  101  connects to spectral energy characterizer  102 , which may be implemented in any of several hardware or software forms. For example, in one embodiment, the spectral energy characterizer  102  is implemented using real-time fast Fourier transform (FFT) techniques. Alternatively, other digital or analog techniques may also be used. 
     It should be understood that inputs and outputs from spike detector  85 , spike characterizer  86 , spike analyzer  87 , disease state estimator  88 , filter  101 , spectral energy characterizer  102 , spectral energy analyzer  103 , and disease state estimator  104  may be comprised of individual signals or a plurality of signals. Further, spike detector  85 , spike characterizer  86 , spike analyzer  87 , disease state estimator  88 , filter  101 , spectral energy characterizer  102 , spectral energy analyzer  103 , and disease state estimator  104  may each have different parameters and signal processing characteristics for each of the multiple signals processed. Because baseline neuronal firing rates differ among various anatomical and functional regions of the brain, and their involvement in disease states and susceptibility to change in firing patterns varies, the respective signal processing circuitry and logic will vary correspondingly. For example, baseline firing rates among neurons in the globus pallidus externus are approximately 43 Hz and those in the globus pallidus internus are 59 Hz. 
     The input to intracranial stimulating electrode ICSE signal processor  239 , referred to above as conditioned intracranial stimulating electrode (ICSE) signal path  84 , connects to spike detector  89 . Spike detector  89  identifies action potentials in a manner similar to that described above with reference to spike detector  85 . Intracranial stimulating electrode ICSE signal processor  239  performs a similar set of functions as intracranial recording electrode ICRE signal processor  238  on a different set of sensory feedback signals. As noted above, spike detection techniques are well known to those skilled in the art. 
     Spike detector  89  passes waveforms to spike characterizer  90 , which uses well known techniques to calculate parameters than characterize the behavior of the individual and groups of neurons, the activity of which is sensed by intracranial stimulating electrode array  37 . As noted above with respect to spike characterizer  86 , this characterization may include parameterization of spikes, bursts of spikes, and overall neural activity patterns. Similarly, the parameterization may include calculation of spike frequencies, burst frequencies, inter-spike intervals, amplitudes, peak-to-valley times, valley-to-peak times, spectral composition, positive phase amplitudes, negative phase amplitudes, and positive-negative phase differential amplitudes. Such characterization of neural spikes is known to those skilled in the art of neurophysiology. Based on this parameterization, spike characterizer  90  discriminates individual spikes and bursts originating from different neurons. As noted, such discrimination facilitates serial monitoring of activity of individual and groups of neurons and the assessment and quantification of activity change, reflective of change in disease state and of response to therapy. 
     Spike analyzer  91  receives the parameters from spike characterizer  90 , and extracts higher level information, including average spike frequencies, average interspike intervals, average amplitudes, standard deviations thereof, trends, and temporal patterning. The function and operation of spike analyzer  91  is similar to that described herein with reference to spike analyzer  87 . Similarly, spike analyzer  91  may receive additional input directly or indirectly from accelerometers and/or EMG arrays to facilitate the characterization of neuronal firing patterns relative to activity of muscle groups and movement of joints. This may include, for example, characterization of neuronal spike amplitudes and tuning of firing to movement, including but not limited to movement velocity and direction. Such characterization may be used to asses functioning of the sensorimotor system, including but not limited to motor response time, and to measure the disease state and response to therapy. 
     Intracranial stimulating electrode (ICSE) single unit-based (SU) disease state estimator  92  receives input from either or both spike characterizer  90  and spike analyzer  91 . ICSE SU disease state estimator  92  receives input representative of the current neuronal activity from spike characterizer  90 . ICSE SU disease state estimator  92  may receive input representative of at least one of several signals, including desired neuronal activity, actual neuronal activity, and the difference between these quantities. The ICSE SU disease state estimate, calculated by ICSE SU disease state estimator  92 , may be comprised of a single or a plurality of signals, consistent with a representation of the disease state by a single or a multitude of state variables, respectively. 
     As with intracranial recording electrode signal processor  238 , inputs and outputs from spike detector  89 , spike characterizer  90 , spike analyzer  91 , disease state estimator  92 , filter  106 , spectral energy characterizer  107 , spectral energy analyzer  108 , and disease state estimator  109  may include individual or a plurality of signals, and each may have different parameters and signal processing characteristics for each of the multiple signals processed. Because baseline neuronal firing rates differ among various anatomical and functional regions of the brain, and their involvement in disease states and susceptibility to change in firing patters varies, the respective signal processing circuitry and logic varies correspondingly. 
       FIG. 4  is a schematic diagram of a globus pallidus  119  implanted with stimulating and recording electrodes. Intracranial catheter  7  is shown in place with electrode of the intracranial stimulating electrode array  37  located within the globus pallidus internus (Gpi)  120 , including globus pallidus intemus internal segment (GPi,i)  94  and globus pallidus internus external segment (GPi,e)  95 , and globus pallidus extemus (GPe)  96 . 
     Intracranial stimulating electrodes  1  and  2  are shown implanted in the globus pallidus internus internal segment (GPi,i)  94 ; and intracranial stimulating electrodes  3  and  4  are shown implanted in the globus pallidus internus external segment (GPi,e)  95  and globus pallidus externus (GPe)  96 , respectively. It should be understood that this arrangement is illustrative of one preferred embodiment, and other stimulating and recording electrode configurations may be employed without departing from the present invention. 
     The optic tract  97  is shown in its close anatomical relationship to the globus pallidus internus (Gpi)  120 . The risk inherent in treatment modalities involving irreversible tissue ablation should be apparent; stereotactic errors of only one to several millimeters during lesioning of the globus pallidus intemus (Gpi)  120  may result in irreversible damage or complete destruction of the optic tract  97 . Furthermore, the advantage of a system which dynamically adjusts the amplitude of inhibitory electrical stimulus to the globus pallidus  119  to minimize said amplitude offers the potential advantage of minimization of side effects including interference with visual signals of the optic tract  97  and prevention of overtreatment. 
     Intracranial stimulating electrodes  1 , 2 , 3 , 4  are shown implanted in the GPi,i  94 , GPi,e  95 , GPe  96 , respectively. This is one preferred embodiment. Numerous permutations of electrode stimulation site configuration may be employed, including more or fewer electrodes in each of these said regions, without departing from the present invention. Electrodes may be implanted within or adjacent to other regions in addition to or instead of those listed above without departing from the present invention, said other reasons including but not limited to the ventral medial Vim thalamic nucleus, other portion of the thalamus, subthalamic nucleus (STN), caudate, putamen, other basal ganglia components, cingulate gyms, other subcortical nuclei, nucleus locus ceruleus, pedunculopontine nuclei of the reticular formation, red nucleus, substantia nigra, other brainstem structure, cerebellum, internal capsule, external capsule, corticospinal tract, pyramidal tract, ansa lenticularis, white matter tracts, motor cortex, premotor cortex, supplementary motor cortex, other motor cortical regions, somatosensory cortex, other sensory cortical regions, Broca&#39;s area, Wemickie&#39;s area, other cortical regions, other central nervous system structure, other peripheral nervous system structure, other neural structure, sensory organs, muscle tissue, or other non-neural structure. 
     Referring to  FIGS. 3 and 4 , a small percentage of cells in the globus pallidus internus internal segment  94  and globus pallidus internus external segment  95  exhibit tremor-synchronous discharges. As noted, at least one of single unit recordings from individual cells and multiple unit recordings from a plurality of cells are processed by signal processor  71 . The single and multiple unit recordings may be derived from signals arising from intracranial stimulating electrode array  37 , intracranial recording electrode array  38 , or other sources. The output from signal processor  71  is connected to control circuit  72  and the output may represent at least one of disease state, magnitude of symptomatology, response to therapy, other parameter, and combination thereof. 
     Individual electrodes comprising intracranial stimulating electrode array  37  and intracranial recording electrode array  38  may each be of the microelectrode type for single unit recordings, macroelectrode type for multiple unit recordings, other electrode type, or a combination thereof, without departing from the spirit of the present invention. In one preferred embodiment, intracranial stimulating electrode array  37  consists of macroelectrodes. The macroelectrodes facilitate delivery of stimulation current at a lower charge density (coulombs per unit of electrode surface area) than microelectrodes of the same chemistry and surface treatment. The dimensions of intracranial stimulating electrodes  1 - 4  are selected such that the current density, or electrical current divided by electrode surface area, is below the threshold of reversible charge injection for the given electrode material. 
     Standard single cell recording technique, using an electrode with an impedance of typically 1-2 Megohms, involves bandpass filtering with −6 decibel (dB) points at 300 and 10,000 Hertz. This filtering, or a modification thereof, may be accomplished by ICRE filter  65  and ICSE filter  64 ; alternatively, it may be performed in spike detector  85  and spike detector  89 , respectively, or other portion of stimulating and recording circuit  26 . 
       FIG. 5  is a block diagram of one embodiment of an EMO signal processor  233  which is included in a preferred embodiment of signal processor  71 . EMG signal processor  233  processes signals from EMG electrode array  50 , performing functions including but not limited to full wave rectification, envelope determination, bandpass filtering, threshold discrimination, and others described in more detail below, to produce signals indicative of the overall magnitude of tremor as well as the frequency at which tremor episodes occur. As noted, EMO electrode array  50  includes, but is not limited to, proximal EMG electrode array  45 , enclosure-mounted EMG electrode array  46 , and distal EMG electrode array  47 . EMG electrodes may be located in any implanted or external location without departing from the present invention. For example, electrodes may be located within or in proximity to the hand, forearm, arm foot, calf, leg, abdomen, torso, neck, head, haw, lip, eyelid, larynx, vocal cords, and tongue. 
     Conditioned EMG signal path  78  is also connected to a well-known full wave rectifier  123  now or later developed. Output from the full wave rectifier  123  is coupled to an input of an envelope determiner  124 . Determination of the envelope of a modulated signal is well known to those skilled in the art of electronics; this may be readily implemented in analog or digital hardware or in software. Output of envelope determiner  124  is connected to inputs of filters  125 ,  127 ,  129 ,  131  and  133 . In one embodiment, filters  125 ,  127 ,  129 ,  131 ,  133  implement passbands of approximately 0.1-2 Hz, 2-3 Hz, 3-5 Hz, 7-8 Hz, and 8-13 Hz, respectively. Outputs of filters  125 ,  127 ,  129 ,  131  and  133  are connected to threshold discriminators  126 ,  128 ,  130 ,  132 ,  134 , respectively. 
     Threshold discriminators  126 ,  128 ,  130 ,  132 , and  134  generate outputs representing episodes of normal voluntary movement (Mv), low frequency intention tremor (Til) resting tremor (Tr), high frequency intention tremor (Tih), and physiologic tremor (Tp), respectively. These outputs are each connected to both of integrator  135  and counter  136 . Integrator  135  generates outputs representative of the total activity of each of the above types of movement over at least one period of time. One such time period may be, for example, time since implantation, time since last visit to physician or health care provider, month internal, week interval, day interval, interval since last medication dose, interval since last change in stimulation parameters, weighted average of multiple time windows, and convolution of said activity with arbitrary time window function. 
     Counter  136  generates outputs representative of the number of episodes of each of the above types of movement over at least one period of time. Such period of time may be, for example, time since implantation, time since last visit to physician or health care provider, month interval, week internal, day interval, interval since last medication dose, interval since last change in stimulation parameters, and weighted average of said number of episodes over multiple time windows. Outputs from integrator  135  and counter  136  are connect to EMG analyzer  137 . EMG analyzer  137  performs a number of functions including, for example, calculation of proportions of tremor activity which are of the rest and the intention type, ratios of different types of tremor activity, the level of suppression of resting tremor activity with voluntary movement, assessment of temporal patterns of EMG activity. EMG disease state estimator  138  receives inputs from EMG analyzer  137  and generates output representative of disease state based upon said input. In one preferred embodiment, two disease states are calculated, including a signal representative of the overall magnitude of tremor activity and a signal representative of the frequency of occurrence of tremor events. It should be understood that all signals paths may transmit one or more signals without departing from the present invention. 
     EMG signals may be sensed from any individual or group of muscles and processed in a manner including but not limited to the determination of severity and frequency of occurrence of various tremor types. Normal or physiologic tremor includes movement in the 8-13 Hz range and may be used as a normalization for the other types of sensed tremor. The predominant pathological form of tremor exhibited in Parkinson&#39;s disease patients is the classical “resting” tremor which includes movements in the 3-5 Hz range which are present at rest and suppressed in the presence of voluntary movement. In the present invention, quantification of this tremor type serves as a heavily weighted sensory input in the assessment of disease state and response to therapy. Parkinson&#39;s disease patients may also exhibit intention tremor, of which there are two types. The first type of intention tremor is referred to as “low frequency intention tremor” (Til in the present invention) and consists of movements in the 2-3 Hz range. A second type of intention tremor is referred to as “high frequency intention tremor” Tih in the present invention and consists of irregular movements in the 7-8 Hz range which persist throughout voluntary movement. Other types of tremor having associated movement in other ranges may be sensed and represented by the EMG signals. 
     EMG signals from at least one of orbicularis oculi (effecting eye closure), Jevator paipebrae (effecting eye opening), and other muscles contributing to eyelid movement, may be sensed and processed to determine frequency of eye blinking. Patients with Parkinson&#39;s disease exhibit a reduction in eye blinking frequency from the normal of 20 per minute to 5 to 10 per minute, and this parameter is sensed as a measure of disease severity and response to treatment. Additionally, said EMG signals may be sensed and processed for detection and quantification of blepharoclonus, or rhythmic fluttering of the eyelids, and used as a measure of disease state and response to therapy. EMG signals, including baseline levels thereof, may be used to quantify rigidity and hypertonus as measures of disease state and response to therapy. Discharge patterns of individual motor units, including but not limited to synchronization of multiple units and distribution of intervals preceding and following discharge, may be used as measures of disease state and response to therapy. 
       FIG. 6  is a block diagram of one embodiment of an EEG signal processor module  234  which is included in embodiments of signal processor  71 . The EEG signal processor module  234  processes signals from EEG electrode array  51 . Conditioned EEG signal path  79  connects to an input of artifact rejecter  139  which rejects signals with amplitudes above a threshold. In one embodiment, this threshold is 0.1 mV. An output from artifact rejecter  139  connects to an input of each of supplementary motor area signal extractor  140  and filters  143 ,  146 ,  149 ,  152 ,  219 . Filters  143 ,  146 ,  149 ,  152 , and  219  are preferably of the bandpass type with passbands of 13-30 Hz, 8-13 Hz, 4-7 Hz, 0.1-4 Hz, and 0.1-0.3 Hz, respectively. Each filter output is connected to an input of an associated full wave rectifier  141 ,  144 ,  147 ,  150 ,  153 ,  220 . Each full wave rectifier  141 ,  144 ,  147 ,  150 ,  153 ,  220  is connected to an input of an associated envelope determiner  142 ,  145 ,  148 ,  151 ,  154 , and  221 , respectively. The envelope determiners generate a signal representative of the envelope of the input signal, typically performed by lowpass filtering with a time constant of 5 seconds. Finally, outputs of envelope determiners  142 ,  145 ,  148 ,  151 ,  154 , and  221  are connected to EEG disease state estimator  155 . 
     Signal SMA generated by supplementary motor area signal extractor  140  represents activity in the supplementary motor area ipsilateral to the intracranial stimulating electrode array (ISEA)  37 . Supplementary motor area signal extractor  140  amplifies signals which are unique to elements of the EEG electrode array  51  which overlie the supplementary motor area. The supplementary motor area receives neural signals via neural projections from the basal ganglia and exhibits decreased activity in patients with Parkinson disease. The SMA is essential for sequential movements, which are often impaired in Parkinson&#39;s disease patients. The SMA signal provides a quantitative measure of disease state and response to therapy. The SMA signal is extracted from the anterior EEC leads, predominantly from those in the vicinity of the frontal cortex, and provides a quantitative measure of disease state and response to therapy. Signals beta, alpha, theta, and delta consist of 13-30 Hz, 8-13 Hz, 4-7 Hz, and 0.1-4 Hz activity, respectively. 
     Signal “resp” consists of 0.1-0.3 Hz activity and reflects respiration. Parkinson&#39;s disease patients exhibit irregular respiratory patterns characterized by pauses and by abnormally deep breathing while at rest and preceding speech. Assessment of respiratory irregularity as well as other parameters derived from such resp signal serve as quantitative measures of disease state and response to therapy. 
     Anterior EEG electrodes are also used to sense EMG signals, and the EMG signals are processed to determine activity of muscles including but not limited to those related to eye blinking activity. Processing of the EMG signals is included in the  FIG. 6  circuit block diagram which contains the EEC signal processing component of signal processor  71 . However, the processing could be incorporated into EMG signal processing component of signal processor  71  without departing from scope of the present invention. Conditioned EEG signal path  79  is additionally connected to input of full wave rectifier  222 , the output of which is connected to the input of an envelope determiner  223 . Envelope determiner  223  includes an output connected to input of filter  224 . Filter  224  is preferably of the bandpass type with a passband range of 0.1 to 20 Hz. Filter  224  has an output connected to input of threshold discriminator  225 , the output of which is connected to EEG disease state estimator  155 . 
     Preferably, EMG signals arising from activity of at least one of orbicularis oculi (effecting eye closure), levator palpebrae (effecting eye opening), and other muscles the activity of which is associated with eyelid movement are sensed by anterior EEG electrodes. These EMG signals are processed to determine eye blink events, and the rates and regularity of eye blinking activity are calculated. Frequency and irregularity of eye blinking as well as blepharoclonus, or rhythmic fluttering of the eyelids, are quantified as measures of disease state and response to therapy. 
       FIG. 7  is a block diagram of one embodiment of an accelerometer signal processor  235  which is incorporated into certain embodiments of signal processor  71 . The accelerometer signal processor  235  processes signals from accelerometer array  52 . Conditioned accelerometer signal path  80  is connected to an input of each of a plurality of filters  156 ,  160 ,  164 ,  168 ,  172 . The filters are preferably of the bandpass type with passbands of 0.1-2 Hz, 2-3 Hz, 3-5 Hz, 7-8 Hz, and 8-13 Hz, respectively. Other passband frequency ranges may also be used. The output of each filter  156 ,  160 ,  164 ,  168 ,  172  is connected to an associated full wave rectifiers  157 ,  161 ,  165 ,  169 , and  173 , respectively. The output of each rectifier  157 ,  161 ,  165 ,  169 , and  173  is connected to an associated envelope determiners  158 ,  162 ,  166 ,  170 , and  174 , respectively. Outputs of envelope determiners  158 ,  162 ,  166 ,  170 , and  174  are connected to inputs of an associated threshold discriminators  159 ,  163 ,  167 ,  171 , and  175 , respectively. 
     Outputs of threshold discriminators  159 ,  163 ,  167 ,  171 ,  175  represent episodes of normal voluntary movement (Mv), low frequency intention tremor (Til), resting tremor (Tr), high frequency intention tremor (Tih), and physiologic tremor (Tp), respectively. These outputs are each connected to an integrator  176  and a counter  177 . Integrator  176  generates outputs representative of the total activity of each of the above types of movement over at least one period of time. As noted, such a time period may be, for example, time since implementation, time since last visit to physician or health care provider, or some other time interval, weighted average of multiple time windows, or convolution of selected activities with an arbitrary time window function. 
     Counter  177  generates outputs representative of the number of episodes of each of the above types of movements over at least one such period of time. Outputs from integrator  176  and counter  177  are connect to an acceleration analyzer  178 . Acceleration analyzer  178  calculates proportions of tremor types, such as the rest and intention types, ratios of different types of tremor activity, the level of suppression of resting tremor activity with voluntary movement, and assessment of temporal patterns of movement and acceleration. Acceleration analyzer  178  may perform some or all of these calculations, as well as other calculations, on alternative embodiments of the present invention. Acceleration-based disease state estimator  179  receives input from acceleration analyzer  178  and generates output representative of disease state based upon such input. 
     It should be understood that accelerometer signals may be sensed from any individual or group of body components. For example, such signals may be sensed from joints, bones, and muscles. Furthermore, such signals may be processed in any well known manner, including the determination of severity and frequency of occurrence of various tremor types. The types of tremor have been described above with respect to  FIG. 5 . 
       FIG. 8  is a block diagram of one embodiment of an acoustic signal processor  236  which is included in certain embodiments of signal processor  71 . Acoustic signal processor  236  processes signals from acoustic transducer array  53 . Conditioned acoustic signal path  81  is connected to a full wave rectifier  180  and a spectral analyzer  185 . The output of full wave rectifier  180  is connected to an input of an envelope determiner  181 , an output of which is connected to an input of a low threshold discriminator  182  and a high threshold discriminator  183 . Low threshold discriminator  182  and high threshold discriminator  183  each have an output connected to an input of timer  184 . Timer  184  generates an output signal representing latency (Lat) and is connected to acoustic analyzer  186 . An output of acoustic analyzer  186  is connected to an input of acoustic-based disease state estimator  187 . Latency (Lat) represents the latency between initiation of vocal utterance and the subsequent achievement of a threshold level of vocal amplitude. Such a vocal amplitude level is set by high threshold discriminator  183  and may represent steady state vocal amplitude or a preset or dynamically varying threshold. Latency from voice onset to achievement of steady state volume may be delayed in patients with Parkinson&#39;s disease and is calculated as a measure of disease state and response to therapy. 
     Acoustic analyzer  186  receives input from spectral analyzer  185 . The respiratory pattern is determined from rhythmic modulation of voice and breathing sounds, sensed by elements of the acoustic transducer array  53 . Irregularity and pauses in respiration as well as abnormally deep breathing patterns at rest and preceding speech are exhibited in Parkinson&#39;s disease patients. Such parameters are quantified and used as estimates of disease state and response to therapy. Respiration durations are quantified; abnormally deep respiration both during rest and preceding speech are identified and used as indicators of disease state and response to therapy. Pauses in speech and decline in speech amplitude, or fading, are additionally monitored as indicators of disease state and response to therapy. Spectral composition of speech is monitored and the change in spectral composition, reflective of changes of pharyngeal and laryngeal geometry, are quantified. Additionally, the fundamental vocal frequency; that is, the frequency at which the epiglottis vibrates, is extracted an that standard deviation of the fundamental vocal frequency is calculated over various time intervals as a quantified measure of the monotonic quality of speech characteristic of Parkinson&#39;s disease. This serves as yet another indicator of disease state and response to therapy. 
       FIG. 9  is block diagram of one embodiment of a peripheral nerve electrode (PNE) signal processor  237  which is implemented in certain embodiments of signal processor  71 . PNE signal processor  237  processes signals from peripheral nerve electrode array  54 . These signals provided by peripheral nerve electrode array  54  are provided to PNE signal processor  237  via conditioned PNE signal path  82 . Conditioned PNE signal path  82  is connected to an input of a spike detector  188  and a filter  191 . 
     Spike detector  188  identifies action potentials. As noted, spike detection techniques are well known to those skilled in the art, and generally employ low and high amplitude thresholds. Waveforms with amplitudes greater than the low threshold and lower than the high threshold are determined to be action potentials. These thresholds may be adjusted in real-time, and the low amplitude threshold is set above the amplitude of background noise and that of nearby cells not of interest, and the high amplitude threshold is set above the amplitude of the desired action potentials to allow their passage while eliminating higher amplitude noise spikes, such as artifacts arising from electrical stimulation currents. It should be understood that bandpass, notch, and other filtering techniques may also used to improve signal to noise ratio and the sensitivity and specific of spike detectors. Individual neuron action potentials are usually recorded using fine point high-impedance electrodes, with impedances typically ranging from 1 to 5 megohms. Alternatively, larger lower-impedance electrodes may be used for recording, in which case the signals obtained typically represent aggregate activity of populations of neurons rather than action potentials from individual neurons. As noted above, peripheral nerve electrode array  54  may include such electrodes as single unit recording microelectrodes, multiple unit recording microelectrodes, intrafascicular electrodes, other intraneural electrodes, epineural electrodes, and any combination thereof. 
     A spike characterizer  189  determines firing patterns of individual neurons, including, for example, tonic activity, episodic activity and burst firing. Spike characterizer  189  receives the signals passed by spike detector  188  and calculates parameters that characterize the behavior of the individual and groups of neurons, the activity of which is sensed by peripheral nerve electrode array  54 . Such characterization includes but is not limited to parameterization of spikes, bursts of spikes, and overall neural activity patterns. Parameterization includes but is not limited to calculation of frequencies of spikes, frequencies of bursts of spikes, inter-spike intervals, spike amplitudes, peak-to-valley times, valley-to-peak times, spectral composition, positive phase amplitudes, negative phase amplitudes, and positive-negative phase differential amplitudes. These parameters are described in further detail below with reference to  FIG. 14 . Based on this parameterization, spike characterizer  189  discriminates individual spikes and bursts originating from different neurons. The discrimination facilitates aerial monitoring of activity of individual and groups of neurons and the assessment and quantification of activity change, reflective of change in disease state and of response to therapy. 
     A spike analyzer  190  receives as input the parameters from spike characterizer  189 , and extracts higher level information, including but not limited to average spike frequencies, average frequencies o bursts of spikes, average interspike intervals, average spike amplitudes, standard deviations thereof, trends, and temporal patterning. 
     Preferably, spike analyzer  190  additionally calculates the rates of change of spike parameters. From prior and current rates of change, future behaviors may be predicted. Rates of change of the parameters include but are not limited to first, second, and third time derivatives. In alternative embodiments, spike analyzer  190  additionally calculates weighted combinations of spike characteristics and performs convolutions of spike waveforms with other spike waveforms, and other preset and varying waveforms. Such operations may be performed, for example, for purposes including but not limited to autocorrelation and digital filtering. 
     Spike analyzer  190  may receive additional input from accelerometers, such as those described above, including head mounted accelerometer  12 , proximal accelerometer  28 , enclosure mounted accelerometer  36 , and distal accelerometer  33 . Spike analyzer  190  may receive indirect input from these or other accelerometers, as well as from conditioned or processed signals arising therefrom. Such conditioned or processed signals include, for example, the signal transmitted by conditioned accelerometer signal path  80  ( FIG. 7 ). 
     Spike analyzer  190  may receive additional input from EMG arrays. As noted, such EMG arrays may include, for example, proximal EMG electrode array  4 S, enclosure-mounted EMG electrode array  46 , and distal EMG electrode array  47 . Spike analyzer  190  may also receive indirect input from these or other EMG electrode arrays, as well as from conditioned or processed signals arising therefrom. Such conditioned or processed signals include but are not limited to the signal transmitted by conditioned EMG signal path  78  ( FIG. 5 ). These additional inputs from accelerometers and EMG arrays facilitates the characterization of neuronal firing patterns relative to activity of muscle groups and movement of joints. Such characterization may include, for example, characterization of neuronal spike amplitudes and tuning of neuronal spike frequencies to movement, including but not limited to the signal transmitted by conditioned EMG signal path  78 . 
     The additional input from accelerometers and EMG arrays also facilitates the characterization of neuronal firing patterns relative to activity of muscle groups and movement of joints, including but not limited to characterization of neuronal spike amplitudes and tuning of neuronal spike frequencies to movement, including but not limited to movement velocity and direction. These characterizations may be used to assess functioning of the sensorimotor system, including but not limited to motor response time, and to measure the disease state and response to therapy. 
     Peripheral nerve electrode (PNE)-based single unit (SU) disease state estimator  194  receives an input representative of the current neuronal activity from spike characterizer  189 . PNE-based single unit disease state estimator  194  may receive input representative of at least one of several signals, including desired neuronal activity, actual neuronal activity, and the difference between these quantities. The output from estimator  194  may carry a single or a plurality of signals, consistent with a representation of the disease state by a single or a multitude of state variables, respectively. 
     Filter  191  has an output connected to an input of spectral energy characterizer  192 . Spectral energy characterizer  192  calculates the spectral composition of the signals sensed by the peripheral nerve electrode array  54 . Spectral energy characterizer  192  provides outputs to each of spectral energy analyzer  193  and peripheral nerve electrode (PNE)-based multiple unit disease state estimator  232 . Output of spectral energy analyzer  193  is connected to an input of PNE-based multiple unit (MU) disease state estimator  232 . PNE SU disease state estimator  194  both receives input from and provides output to PNE MU disease state estimator  232 . 
     PNE MU disease state estimator  232  receives as an input signals representative of the current neuronal activity from spectral energy characterizer  192 . PNE MU disease state estimator  232  may receive input representative of at least one of several signals, including desired neuronal activity, actual neuronal activity, and the difference between these quantities. The output from PNE MU disease state estimator  232  may carry a single or a plurality of signals, consistent with a representation of the disease state by a single or a multitude of state variables, respectively. 
     It should be understood that inputs and outputs from each spike detector  188 , spike characterizer  189 , spike analyzer  190 , filter  191 , spectral energy characterizer  192 , spectral energy analyzer  193 , and PNE-based single unit disease state estimator  194 , and PNE-based multiple unit disease state estimator  232  may each be comprised of individual signals or a plurality of signals. It should also be understood that each of these the units, spike detector  188 , spike characterizer  189 , spike analyzer  190 , filter  191 , spectral energy characterizer  192 , spectral energy analyzer  193 , and PNE-based single unit disease state estimator  194 , and PNE MU disease state estimator  232  may each have different parameters and signal processing characteristics for each of the multiple signals processed. Modifications of this processing circuitry may be made to accommodate various combinations of intraneural electrodes, used for single and multiple unit recordings, and epineural electrodes, used for compound action potential recordings, without departing from the present invention. 
       FIG. 11  is a schematic diagram of one embodiment of a patient-neural modulator system  999  illustrated in  FIG. 2  with feedback control. Patient-neural modulator system  999  primarily includes an observer  228  and a controller  229 . An observer is a component of a control system that is known to those or ordinary skill in the art of control systems. An observer is a functional block in which variables, typically represented in software as parameter values or in hardware as electrical signal amplitudes, represent states of the controlled system. Such a component is used in controlling systems in which one or more of the state variables are not directly observable from the sensed signals. An observer essentially includes a simulated version of the controlled system. Its input are the same control law output signals delivered to the controlled system, and its outputs are desired to match those sensed outputs of the controlled system. The difference between the outputs of the observer and the measured outputs of the controlled system, that is, the outputs of a motor control portion of the patient&#39;s nervous system in this case, are used to calculate an observer error signal which may then be used to correct the observer error. Since the observer is implemented in software or hardware, all of its signals, including all state variables, are accessible. In a system such as the complex neural circuitry of the patient, one or more of the state variables may not be “observable”, that is directly measurable or calculatable based on measured values. In such a case, the state variables present in the observer may be used as “estimates” of the actual state variables and included in the control law. The general use of “observers” for estimation of “unobservable” state variables is known to those skilled in the art of control theory. The use of observers for the estimation of neural state variables, disease states, and responses to therapy is one of the teachings of the present invention. 
     Observer  228  includes signal conditioning circuit  76  ( FIG. 2 ) and signal processor  71  ( FIGS. 2, 10 ). Signal processor  71 , as noted, includes disease state estimator module array (DSEMA)  229  and aggregate disease state estimator  195 . Observer  228  receives patient output “y” from patient  227 . Patient output “y” is comprised of one or more signals arising from patient  227 . In one preferred embodiment patient output “y” includes one or more signals from EMG electrode array  50 , EEG electrode array  51 , accelerometer array  52 , acoustic transducer array  53 , peripheral nerve electrode array  54 , intracranial recording electrode array  38 , and intracranial stimulating electrode array  37 . It should be understood that additional signals f the same or different type may also be included. 
     Control circuit  72  ( FIG. 2 ) includes summator  226  which receives an input from reference module  116 , and a control law circuit block  231 . Controller  229  includes the control law circuit lock  231  and output stage circuit  77 . Controller  229  generates a neural modulation waveforms “u”, described in detail below with reference to  FIG. 13 . The function and operation of each of these modules is described in detail below. 
     Reference disease state “r”, generated by reference module  116 , is a non-inverting input to summator  226 , providing disease state and target reference values for the single or plurality of control laws implemented in control law circuit block  231  introduced above with reference to  FIG. 2 . Reference module  116  may also receive input from control circuit  72 , facilitating the dynamic adjustment of reference values. Reference disease state “r” may comprise a single or plurality of signals, each of which may be zero, constant, or time-varying independent of the other. Disease state error “e” is output from summator  226  and input to controller  229 . Disease state error “e”, which may comprise a single or plurality of signals, represents a difference between a desired disease state (represented by reference disease state “r”) and an actual disease state (represented by disease state estimate “x”). Other methods of calculating disease state estimate “x”, including but not limited to linear or nonlinear combinations of reference disease state “r” and disease state estimate “x”, may be employed without departing from the present invention. Controller  229  is comprised of control law circuit block  231  and output stage circuit  77 . 
     Disease state error “e” is input to control law circuit block  231  which generates a control circuit output “uc.” Control law circuit block  231  is connected to an input of output stage circuit  77 . The output of the controller  229 , which is generated by the output stage circuit  77 , “u”, is delivered to patient  227  in the form of neural modulation waveforms, described in detail below with reference to  FIG. 13 . 
     Patient output “y” is input to signal conditioning circuit  76 , the output of which is connected to the input of DSEMA  229 . The output of DSEMA  229  is provided to an aggregate disease state estimator  195 , the output of which is the disease state estimate x. Disease state estimate x, which may be comprised of a single or plurality of signals, is an inverting input to summator  226 . 
     Control law circuit block  231  receives disease state estimate x as an additional input, for use in nonlinear, adaptive and other control laws. Reference module  116  receives input from DSEMA  229  and aggregate disease state estimator  195  for use in dynamically determining reference disease state r. Other modifications, including substitutions, additions, and deletions, may be made to the control loop without departing from the present invention. 
     Control law circuit block  231  has an autocalibration mode in which multivariable sweeps through stimulation parameters and stimulating electrode configurations are performed to automate and expedite parameter and configuration optimization. This autocalibration feature enables rapid optimization of treatment, eliminating months of iterations of trial and error in optimizing stimulation parameters and electrode configuration necessitated by the prior technique of constant parameter stimulation. Additionally, this autocalibration feature permits real-time adjustment and optimization of stimulation parameters and electrode configuration. This is particularly useful to overcome increases in electrode impedance which result from the body&#39;s normal response to implanted foreign bodies in which a fibrotic capsule is commonly formed around the electrodes. Effects of shifts in electrode position relative to a target structures may be minimized by said autocalibration feature. Detection of changes in electrode impedance and position are facilitated by autocalibration feature. The autocalibration feature facilitates detection of changes in electrode impedance and position. Notification of patient and health care provider allows proactive action, including automated or manual adjustment of treatment parameters and advance knowledge of impending electrode replacement needs. 
       FIG. 12  is a schematic diagram of control circuit  72 . As noted, control circuit  72  comprises control laws circuit block  231  and summator  226 . Disease state error “e” is input to gain stages of control laws, including but not limited to at least one of proportional gain  197 , differential gain  198 , integral gain  199 , nonlinear gain  200 , adaptive gain  201 , sliding gain  202 , and model reference gain  203 . 
     An output of each of these gain stages is connected to what is referred to herein as control law stages. In the illustrative embodiment, control law stages includes proportional controller  230 , differential controller  204 , integral controller  205 , nonlinear controller  206 , adaptive controller  207 , sliding controller  208 , and model reference controller  209 , respectively. 
     Outputs of these control law stages are connected to weight stages, including proportional controller weight  210 , differential controller weight  211 , integral controller weight  212 , nonlinear controller weight  213 , adaptive controller weight  214 , sliding controller weight  215 , and model reference controller weight  216 . Outputs of the weight stages are noninverting inputs to summator  217 , the output of which is control circuit output “uc”. The weight stages may be any combination of at least one of constant, time varying, and nonlinear without departing from the present invention. 
     Disease state estimate x is input to nonlinear controller  206 , adaptive controller  207 , sliding controller  208 , and model reference controller  209 . The control laws depicted are representative of one possible implementation; numerous variations, including substitutions, additions, and deletions, may be made without departing from the present invention. 
     The present invention optimizes the efficiency of energy used in the treatment given to the patient by minimizing to a satisfactory level the stimulation intensity to provide the level of treatment magnitude necessary to control disease symptoms without extending additional energy delivering unnecessary overtreatment. In the definition of the control law, a command input or reference input (denoted as r in  FIGS. 11 and 12 ) specifies the target disease state. In the preferred embodiment, r specifies the target amplitude of tremor. The control law generates an electrical stimulation magnitude just sufficient to reduce the patient&#39;s tremor to the target value. With this apparatus and method, the precise amount of electrical energy required is delivered, and overstimulation is avoided. In present stimulation systems, a constant level of stimulation is delivered, resulting in either of two undesirable scenarios when disease state and symptoms fluctuate: (1) undertreatment, i.e. tremor amplitude exceeds desirable level or (2) overtreatment or excess stimulation, in which more electrical energy is delivered than is actually needed. In the overtreatment case, battery life is unnecessarily reduced. The energy delivered to the tissue in the form of a stimulation signal represents a substantial portion of the energy consumed by the implanted device; minimization of this energy substantially extends battery life, with a consequent extension of time in between reoperations to replace expended batteries. 
       FIG. 13  is a schematic diagram of electrical stimulation waveforms for neural modulation. The illustrated ideal stimulus waveform is a charge balanced biphasic current controlled electrical pulse train. Two cycles of this waveform are depicted, each of which is made of a smaller cathodic phase followed, after a short delay, by a larger anodic phase. In one preferred embodiment, a current controlled stimulus is delivered; and the “Stimulus Amplitude” represents stimulation current. A voltage controlled or other stimulus may be used without departing from the present invention. Similarly, other waveforms, including an anodic phase preceding a cathodic phase, a monophasic pulse, a triphasic pulse, or the waveform may be used without departing from the present invention. 
     The amplitude of the first phase, depicted here as cathodic, is given by pulse amplitude  1  PA 1 ; the amplitude of the second phase, depicted here as anodic, is given by pulse amplitude  2  PA 2 . The durations of the first and second phases are pulse width  1  PW 1  and pulse width  1  PW 2 , respectively. Phase  1  and phase  2  are separated by a brief delay d. Waveforms repeat with a stimulation period T, defining the stimulation frequency as f=1/T. 
     The area under the curve for each phase represents the charge Q transferred, and in the preferred embodiment, these quantities are equal and opposite for the cathodic (Q 1 ) and anodic (Q 2 ) pulses, i.e. Q=Q 1 =Q 2 . For rectangular pulses, the charge transferred per pulse is given by Q 1 =PA  1 *PW 1  and Q 2 =PA 2 *PW 2 . The charge balancing constraint given by −Q 1 =Q 2  imposes the relation PA 1 *PW 1 =−PA 2 *PW 2 . Departure from the charge balancing constraint, as is desired for optimal function of certain electrode materials, in included in the present invention. 
     The stimulus amplitudes PA 1  and PA 2 , durations PW 1  and PW 2 , frequency f, or a combination thereof may be varied to modulate the intensity of the said stimulus. A series of stimulus waveforms may be delivered as a burst, in which case the number of stimuli per burst, the frequency of waveforms within the said burst, the frequency at which the bursts are repeated, or a combination thereof may additionally be varied to modulate the stimulus intensity. 
     Typical values for stimulation parameters include f=100-300 Hz, PA 1  and PA 2  range from 10 microamps to 10 milliamps, PW 1  and PW 2  range from 50 microseconds to 100 milliseconds. These values are representative, and departure from these ranges is included in the apparatus and method of the present invention. 
       FIG. 14  is a schematic diagram of one example of the recorded waveforms. This represents an individual action potential from a single cell recording, typically recorded from intracranial microelectrodes. Aggregates of multiple such waveforms are recorded from larger intracranial electrodes. The action potentials may be characterized according t a set of parameters including but not limited to time to valley  1  TV 1 , time to peak  1  TP 1 , time to valley  2  TV 2 , amplitude of valley  1  AV 1 , amplitude of peak  1  AP  1 , amplitude of valley  2  AV 2 , and algebraic combinations and polarity reversals thereof. 
     When recording activity from more than one cell, said characterization facilitates discrimination of waveforms by individual recorded cell. The discrimination allows activity of a plurality of cells to be individually followed over time. The parameterization may be performed separately on signals recorded from different electrodes. Alternatively, said parameterization may be performed on signals pooled from multiple electrodes. 
     Following is a description of a general form for representing disease state. 
     Disease State DS is a vector of individual disease states, including intrinsic disease states DS I  and extrinsic disease states DS E : 
       DS=[DS I DS E ] 
     Intrinsic disease states and extrinsic disease states are, themselves vectors of individual disease states: 
       DS I =[DS I1 DS I2 DS I3  . . . DS IN ] 
       DS E =[DS E1 DS E2 DS E3  . . . DS EM ] 
     Intrinsic Disease States include those disease states which characterize the state of disease at a given point in time. Extrinsic Disease States include variations of intrinsic disease states, including but not limited to cyclical variations in Intrinsic Disease States, variations in Intrinsic Disease States which occur in response to external events, and variations in Intrinsic Disease States which occur in response to levels of and changes in levels of electrical stimulation. Said external events include but are not limited to pharmacologic dosing, consumption of meals, awakening, falling asleep, transitioning from Parkinsonian “on” state to Parkinsonian “off” state, transitioning from Parkinsonian “off” state to Parkinsonian “on” state. 
     Each of Intrinsic Disease States and Extrinsic Disease States include but are not limited to those defined herein; additional disease states and definitions thereof may be added without departing from the present invention. 
     The first intrinsic disease state DS 11  represents the level of resting tremor 
       DS I1 RT N    
     Where Normalized Resting Tremor Magnitude RT.sub.N is given by: 
       RT N   =T   A,3-5   *W   TA,3-5   +T   E,3-5   *W   TE,3-5   +T   P,3-5   *W   PE,3-5   +T   C,3-5   +W   TC,3-5   +T   N,3-5   *W   TN,3-5   +T   S,3-5   *W   TS,3-5   +T   E,3-5   *W   TE,3-5    
     Where the factors from which the Resting Tremor Magnitude RTN is determined, representing estimates of the magnitude of 3-5 Hertz movement of selected body segments, including but not limited to limbs, torso, and head are: 
     T A,3-5 =Tremor level determined by acceleration monitoring
 
W TA,3-5 =Weighting factor for tremor T A,3-5  
 
T E,3-5 =Tremor level determined by electromyographic (EMG) monitoring
 
W TE,3-5 =Weighting factor for tremor T E,3-5  
 
T P,3-5 =Tremor level determined by peripheral nerve electrode monitoring
 
W TP,3-5 =Weighting factor for tremor T P,3-5  
 
T C,3-5 =Tremor level determined by cortical electrode monitoring
 
W TC,3-5 =Weighting factor for tremor T C,3-5  
 
T N,3-5 =Tremor level determined by neural monitoring, including subcortical nuclei, white matter tracts, and spinal cord neurons
 
W TN,3-5 =Weighting factor for tremor T N,3-5  
 
T S,3-5 =Tremor level determined by acoustic sensor monitoring
 
W TS,3-5 =Weighting factor for tremor T S,3-5  
 
     Weighting factors are adjusted after implantation to achieve normalization of RTN and to allow for selective weighting of tremor levels as determined from signals arising from various sensors, including but not limited to those listed. 
     These calculations may be implemented in analog hardware, digital hardware, software, or other form. In the preferred embodiment, values are implemented as 16-bit variables; ranges for said weighting factors and tremor levels are 0 to 65535. These ranges may be changed or implemented in analog form without departing from the present invention. 
     The second intrinsic disease state DS I2  represents the level of dyskinesia: 
       DS I2   =D   N    
     Where Normalized Dyskinesia Magnitude D N  is given by: 
     
       
      
       D 
       N 
       =D 
       A 
       *W 
       DA 
       +T 
       E 
       *W 
       TE 
       +T 
       P 
       *W 
       PE 
       +T 
       C 
       +W 
       TC 
       +T 
       N 
       *W 
       TN 
       +T 
       S 
       *W 
       TS 
       T 
       E 
       *W 
       TE  
      
     
     Where 
     D A,3-5 =Dyskinesia level determined by acceleration monitoring
 
W DA,3-5 =Weighting factor for Dyskinesia D A,3-5  
 
D E,3-5 =Dyskinesia level determined by electromyographic (EMG) monitoring
 
W DE,3-5 =Weighting factor for Dyskinesia D E,3-5  
 
D P,3-5 =Dyskinesia level determined by peripheral nerve electrode monitoring
 
W DP,3-5 =Weighting factor for Dyskinesia D P,3-5  
 
D C,3-5 =Dyskinesia level determined by cortical electrode monitoring
 
W DC,3-5 =Weighting factor for Dyskinesia D C,3-5  
 
D N,3-5 =Dyskinesia level determined by neural monitoring, including subcortical nuclei, white matter tracts, and spinal cord neurons
 
W DN,3-5 =Weighting factor for Dyskinesia D N,3-5  
 
D S,3-5 =Dyskinesia level determined by acoustic sensor monitoring
 
W DS,3-5 =Weighting factor for Dyskinesia D S,3-5  
 
     The third intrinsic disease state DS I3  represents the level of rigidity. 
       DS I3   =R   N    
     Where Normalized Rigidity Magnitude R N  is given by: 
     
       
      
       R 
       N 
       =R 
       A 
       *W 
       RA 
       +R 
       E 
       *W 
       RE 
       +R 
       P 
       *W 
       RE 
       +R 
       C 
       W 
       RC 
       R 
       N 
       *W 
       RN 
       +R 
       S 
       *W 
       RS 
       +R 
       E 
       *W 
       RE  
      
     
     Where 
     R A,3-5 =Rigidity level determined by acceleration monitoring
 
W RA,3-5 =Weighting factor for Rigidity R A,3-5  
 
R E,3-5 =Rigidity level determined by electromyographic (EMG) monitoring
 
W RE,3-5 =Weighting factor for Rigidity R E,3-5  
 
R P,3-5 =Rigidity level determined by peripheral nerve electrode monitoring
 
W RP,3-5 =Weighting factor for Rigidity R P,3-5  
 
R C,3-5 =Rigidity level determined by cortical electrode monitoring
 
W RC,3-5 =Weighting factor for Rigidity R C,3-5  
 
R N,3-5 =Rigidity level determined by neural monitoring, including subcortical nuclei, white matter tracts, and spinal cord neurons
 
W RN,3-5 =Weighting factor for Rigidity R N,3-5  
 
R S,3-5 =Rigidity level determined by acoustic sensor monitoring
 
W RS,3-5 =Weighting factor for Rigidity R S,3-5  
 
     The fourth intrinsic disease state DS I4  represents the level of bradykinesia. 
       DS I4   =B   N    
     Where Normalized Bradykinesia Magnitude B N  is given by: 
     
       
      
       B 
       N 
       =B 
       A 
       *W 
       BA 
       +B 
       E 
       *W 
       BE 
       +B 
       P 
       *W 
       PE 
       +B 
       C 
       +W 
       BC 
       +B 
       N 
       *W 
       BN 
       +B 
       S 
       *W 
       BS 
       +B 
       E 
       *W 
       BE  
      
     
     Where 
     R A =Bradykinesia level determined by acceleration monitoring
 
W RA =Weighting factor for Bradykinesia R A  
 
R E =Bradykinesia level determined by electromyographic (EMG) monitoring
 
W RE =Weighting factor for Bradykinesia R E  
 
R P =Bradykinesia level determined by peripheral nerve electrode monitoring
 
W RP =Weighting factor for Bradykinesia R P  
 
R C =Bradykinesia level determined by cortical electrode monitoring
 
W RC =Weighting factor for Bradykinesia R C  
 
R N =Bradykinesia level determined by neural monitoring, including subcortical nuclei, white matter tracts, and spinal cord neurons
 
W RN =Weighting factor for Bradykinesia R N  
 
R S =Bradykinesia level determined by acoustic sensor monitoring
 
W RS =Weighting factor for Bradykinesia R S  
 
     The control law drives these disease states toward their reference values, nominally 0, according to a vector of weights, establishing a prioritization. 
     Side effects and other parameters, such as power consumption and current magnitude, are also quantified and minimized according to a cost function. 
     One advantage of the present invention is that it provides prediction of future symptomatology, cognitive and neuromotor functionality, and treatment magnitude requirements. Such predictions may be based on preset, learned and real-time sensed parameters as well as input from the patient, physician or other person or system. The prediction of future symptomatology is based upon any of several weighted combination of parameters. Based upon prior characterization of the circadian fluctuation in symptomatology (that is, tremor magnitude for deep brain stimulation or level of depression for stimulation of other sites including locus ceruleus), future fluctuations may be predicted. An estimate, or model, of fluctuation may be based upon a combination of preset, learned, and real-time sensed parameters. Preset parameters are derived from clinical studies designed specifically for the purpose of gathering such data, or from estimates extracted from data gleaned from published literature. Real-time sensed parameters are derived from the current states (and changes, i.e. derivatives and other processed signals, thereof) of sensed and processed signals. Learned parameters are based upon the time histories of previously sensed signals. For example, the circadian fluctuation in tremor amplitude may be sensed; a weighted average of this data collected over numerous prior days provides as estimate of the expected tremor amplitude as well as a standard deviation and other statistical parameters to characterize the anticipated tremor amplitude. Similarly, in the presence of closed-loop feedback, the level of stimulation required to reduce or eliminate tremor may be used as an estimate of the “amplitude” or state of the underlying disease. 
     Another advantage of the present invention is that it performs automated determination of the optimum magnitude of treatment—by sensing and quantifying the magnitude and frequency of tremor activity in the patient, a quantitative representation of the level or “state” of the disease is determined. The disease state is monitored as treatment parameters are automatically varied, and the local or absolute minimum in disease state is achieved as the optimal set of stimulation parameters is converged upon. The disease state may be represented as a single value or a vector or matrix of values; in the latter two cases, a multivariable optimization algorithm is employed with appropriate weighting factors. 
     Having now described several embodiments of the invention, it should be apparent to those skilled in the art that the foregoing is merely illustrative and not limiting, having been presented by way of example only. For example, all signal paths may transmit a single or plurality of signals without departing from the present invention. Numerous modifications and other embodiments are within the scope of one of ordinary skill in the art and are contemplated as falling within the scope of the invention as defined by the appended claims. 
     Current implanted neurostimulators suffer from limited battery life necessitating replacement. The original filing of this invention teaches a closed-loop technique which allows lower average power delivery, providing extended batter life. A further improvement is taught in the present invention. The present invention teaches a novel form of power delivery, in which electromagnetic power is provided externally and radiated transcutaneously through the skin  382  to the implanted circuit. 
     A multiplicity of electromagnetic coils is used, to provide electromagnetic fields of multiple non-collinear orientations. The purpose of this design is to overcome fluctuations and interruptions in power that otherwise occur when the implanted circuit moves along with the body parts relative to electromagnetic coils. 
     An additional improvement is taught in the application ohime multiplexing of signals delivered by said multiplicity of electromagnetic coils. Multiplexing signals in time from a multiplicity of coils allows electromagnetic energy to be delivered at a multiplicity of spatial orientations without mutual interference between fields. In one embodiment, three coils are positioned such that they are mutually orthogonal. Regardless of the orientation of the implanted circuit, mutual coupling between the implanted circuit and at least one element of the coil array will facilitate power transmission. 
     The present invention further includes an embodiment in which the coils are embedded in a flexible cloth assembly, including but not limited to a pillow, bandana, hat, or other accessory or piece of apparel. 
     Additionally, low profile mounting of the neurological control system  999  is taught, including a design in which the device is implanted entirely adjacent to the head, avoiding the subcutaneous cables plaguing current designs with high failure and fracture rates. Additionally, a low-profile design is taught in which the neurological control system is implanted beneath the skin and recessed in the caldarium. 
       FIG. 16  and  FIG. 18  show two coil and three coil embodiments, respectively, of power delivery unit  413 . Different numbers of coils may be used without departing from the present invention. Each of electromagnetic coil  372 ,  373 ,  374  radiates electromagnetic power, represented by magnetic flux  375 ,  376 ,  377 , which radiates through the skin  382  and is then converted to electrical energy by power conversion unit  378 . Power is then transmitted from power conversion unit  378  via power cable  379  to stimulating and recording circuit  26 , a component of the neurological control system  999 . 
     A single or multiplicity of coil holder  380  holds electromagnetic coil  372 ,  373 ,  374  in place. Coil holder  380  may be placed atop bedding  381 , which may include a pillow, blanket, or mattress. Alternatively, coil holder  380  may be placed below bedding  381 , which may include a pillow, blanket, mattress, or other element. 
       FIG. 17  shows this configuration at greater magnification, in which magnetic flux  375  is seen to penetrate skin  382 . 
       FIG. 19  depicts power delivery unit  413  with coil holder  380  shown holding electromagnetic coil  372 ,  373 ,  374  in place, as seen from above. In this design, all electromagnetic coils  372 ,  373 ,  374  are contained within coil holder  380 . For illustrative purposes, coil holder  380  is shown in this embodiment as a convex shape. Other shapes may be used without departing from the present invention. 
       FIG. 20  shows coil holder  380  with electromagnetic coil  372 ,  373 ,  374  in place, as seen from above. In this design, all electromagnetic coils  372 ,  373 ,  374  are contained within coil holder  380 . For illustrative purposes, coil holder  380  is shown in this embodiment as a multi-lobed shape, with electromagnetic coil  372 ,  373 ,  374  contained within coil pockets  383 ,  384 ,  385 , respectively. Other shapes may be used without departing from the present invention. 
     Electromagnetic coils  372 ,  373 ,  374  may be permanently affixed within coil holder  380 . Alternatively, electromagnetic coils  372 ,  373 ,  374  may be removable form coil holder  380  to facilitate cleaning or laundering of coil holder  380 . 
     Further, use of electromagnetic fields, as direct modulators of neural activity is taught in the present invention. [This also references provisional application filed Jul. 29, 2002.) 
     As shown in  FIG. 21 , electromagnetic coils  372 ,  373 ,  374  may be positioned by coil holder  380  to lie in close proximity to the head  398  of a patient. Magnetic flux  375 ,  376 ,  377 , which radiate through the skin  382  penetrate into underlying brain  251 , or other neural tissue  354 , and generate electrical currents. Said electrical currents serve to stimulate or inhibit neural activity. Other neural tissue includes but is not limited to brain  251 , brainstem, spinal cord, peripheral nerves, cranial nerves, neurosensory organs, and other structures. 
       FIG. 22  shows multiple coil embodiments. Different numbers of coils may be used without departing from the present invention.  FIG. 22  shows a distributed arrangement of coils, to facilitate strong signal coupling with a minimum of tissue heating or sensory stimulation. Brain  251  is shown underlying skin  382  in head  398 . Temporal cortex  255  and thalamus  270  are shown. 
     Headband coil holder  386  is in mechanical connection with each of electromagnetic coils  387 ,  388 ,  389 ,  390 ,  391 ,  392 ,  393 , which radiate electromagnetic power through the skin  382 . This is then converted to electrical energy by power conversion unit  378 , which is implanted in any of several locations, shown in  FIG. 17  and  FIG. 18 , omitted here for diagram clarity. As shown in  FIG. 17 , power is then transmitted from power conversion unit  378  via power cable  379  to stimulating and recording circuit  26 , a component of the neurological control system  999 . 
     Power source  397  provides power to power modulator  396 , which modulates power on a carrier frequency and is electrically connected via electromagnetic coil cable  394  to electromagnetic coils  387 ,  388 ,  389 ,  390  which radiate power through skin  382 . Power modulator  396  is also electrically connected via electromagnetic coil cable  395  to electromagnetic coils  391 ,  392 ,  393  which radiate power through skin  382 . Electromagnetic coils  387 ,  388 ,  389 ,  390 ,  391 ,  392 ,  393  can be arranged in a different geometrical or anatomical configuration, with the same, a higher or a lower number of electromagnetic coils, without departing from the present invention. Electromagnetic coils  387 ,  388 ,  389 ,  390 ,  391 ,  392 ,  393  may alternatively be affixed to a different form of apparel, clothing, or fixture without departing from the present invention. Such embodiments include a hat, cap, bandana, or other device. 
       FIG. 23  shows the paracranial design, with implanted components in close proximity to the head  398 . This design eliminates the pervasive problem of moving parts, inherent in all current technology devices. All cabling is in close approximation to the caldarium, a rigid structure; therefore all implanted parts remain stationary. Since no implanted parts are subjected to movement, they are at greatly reduced risk for mechanical fatigue, the single highest failure mode for current implanted neuromodulators. This innovation dramatically reduces the frequency and likelihood for re-operation to repair broken implanted components, a major concern in all patients and particularly the elderly, who are often poor operative candidates, and who are at highest risk for many of the neurological and neurodegenerative diseases amenable to treatment by neuromodulation. 
       FIG. 23  depicts an embodiment in which the head  398  of the patient lies atop coil holder  380 , placing the power conversion unit  378  within at least one of magnetic flux  375 ,  376 ,  377 . Power conversion unit  378  converts power transmitted as magnetic flux  375 ,  376 ,  377  into electrical energy, which is transmitted by power cable  379  to stimulating and recording circuit  26 , a component of the neurological control system  999 . Stimulating and recording circuit  26  generates neuromodulation signal that is transmitted via connecting cable  8  to intracranial stimulating electrode array  37 , depicted in more detail in  FIG. 1 , which deliver neuromodulation signal to brain  251 . or other neural tissue  354 . Connecting cable  8  furthermore provides bi-directional electrical connection between intracranial electrodes  246  and stimulating and recording circuit  26 , a component of neurological control system  999 . 
     Catheter anchor  29  is in mechanical communication with intracranial catheter  7 . and secures it to caldarium  9 . Intracranial electrodes  246  are mounted on or incorporated into intracranial catheter  7 . 
       FIG. 24  shows power conversion unit  378  includes electromagnetic coupling element  399 , such as a wire coil, circuit board tracing coil, or equivalent implementation. In one embodiment, power conversion unit  378  also includes power conversion circuit  400 , which converts electrical signal induced by any of magnetic flux  375 ,  376 ,  377 , on electromagnetic coupling element  399 , into at least one of electrical power and electrical signal, which are transmitted via power cable  379  to stimulating and recording circuit  26 , which generates neuromodulation signal  412 , which is transmitted on connecting cable  8  to intracranial catheter  7 . 
     Power conversion circuit  400  includes rectifier  401 , energy storage element  402 , regulator  403 , filter  404 , demodulator  405 , and amplifier  406 . Electromagnetic coupling element  399  is electrically connected via electromagnetic coupling element cable  407  to power conversion circuit  400 . 
     Either of magnetic flux  375 ,  376 ,  377  passing through electromagnetic coupling element  399  induces induced current  408 , which is transmitted via electromagnetic coupling element cable  407  to power conversion circuit  400 . Induced current  408  is rectified by rectifier  401  to a direct current form and is stored or lowpass filtered by energy storage element  402  and regulated by regulator  403  and then transmitted as regulated power  409  by power cable  379  to stimulating and recording circuit  26 , a component of neurological control system  999 . 
       FIG. 25  shows the communication circuit. This circuit also provides bi-directional communication with external devices, including power delivery unit  413  as well as with patient interface module  55  and supervisory module  56 , shown in  FIG. 1 . 
     Either of magnetic flux  375 ,  376 ,  377  passing through electromagnetic coupling element  399  induces induced current  408 , which is transmitted via electromagnetic coupling element cable  407  to power conversion circuit  400 . Induced current  408  is filtered by filter  404 , then demodulated by  405 , then amplified by amplifier  406 , then transmitted as incoming data stream  410  via power cable  379  to stimulating and recording circuit  26 . At least one of digital to analog or analog to digital conversion may also be performed without departing from the present invention. 
     Outgoing data stream  411  is transmitted from stimulating and recording circuit  26  via power cable  379  to power conversion circuit  400  where it is modulated by modulator  414  and then amplified by amplifier  415  and transmitted as inducing current  416  along electromagnetic coupling element cable  407  to electromagnetic coupling element  399  where it generated magnetic flux that is detected and decoded by external devices, including power delivery unit  413  as well as with patient interface module  55  and supervisory module  56 , shown in  FIG. 1 . At least one of digital to analog or analog to digital conversion may also be performed without departing from the present invention. 
       FIG. 26  depicts the overall system, with power conversion unit  378  connected via power cable  379  to power management unit  417 , a component of stimulating recording and power circuit  419 . Power management unit  417  is in electrical connection with energy storage unit  418  and in electrical connection with stimulating and recording circuit  26 . 
     In one preferred embodiment, energy storage unit  418  is implemented as a capacitor. Power management unit  417  converts regulated power  409  into charge, which is stored in energy storage unit  418 . 
     In another preferred embodiment, energy storage unit  418  is implemented as a rechargeable battery. Power management unit  417  converts regulated power  409  into current, which recharges energy storage unit  418 . 
     When regulated power  409  is insufficient to power stimulating and recording circuit  26 , power management unit  417  withdrawals energy from energy storage unit  418  and delivers energy to stimulating and recording circuit  26 . 
     An embodiment of this invention further includes novel geometrical features to enhance form and function of this neuromodulation system as well as any neural stimulation system. One invention taught is the refinement of the implanted case such that one side is concave. An additional invention taught is the refinement of the implanted case such that one side is convex. A further extension of this invention includes the use of at least one concave side and at least one convex side. Advantages of this design include the close positioning of the casing against the intact outer table of the skull of the patient. 
       FIG. 27  shows stimulating and recording unit  43  enclosed within system enclosure  434 , implanted beneath the scalp  10 , overlying the caldarium  9 . System enclosure  434  is designed with an enclosure inner surface  427  and enclosure outer surface  426 . In one preferred embodiment, enclosure inner surface  427  and enclosure outer surface  426  are concave and convex, respectively. 
     By designing enclosure inner surface  427  to be concave, system enclosure  434  may be placed tightly and securely against caldarium  9 . This represents a substantial innovation over flat machined enclosures, the designs currently implanted elsewhere and that are based upon established pacemaker technology. This innovation reduces or eliminates movement of the implant that would otherwise occur with existing devices. 
     By designing enclosure outer surface  426  to be convex, system enclosure  434  may be placed against calvarium  9  with a substantially improved cosmetic result and less displacement of scalp  10  or other skin  382 . This represents a substantial innovation over flat machined enclosures, the designs currently implanted elsewhere and that are based upon established pacemaker technology. This innovation reduces or eliminates movement of the implant that would otherwise occur with existing devices. 
     Furthermore, each of enclosure inner surface  427  and enclosure outer surface  426  serve to reduce the profile of system enclosure  434  and to improve the cosmetic appearance of the overlying skin  382  or scalp  10 . 
     In this embodiment, system enclosure  434  overlies the intact pericranium  420 . This facilitates attachment of System enclosure  434  to the pericranium  420  by means of mechanical attachment  424 , providing mechanical attachment between mechanical attachment mount  425  and at least one of pericranium  420 , skin  382 , scalp  10 , or other tissue of the patient. Mechanical attachment  424 , may be implemented as sutures, wires, clips, or other attachment means. In this embodiment, the caldarium outer table  421 , caldarium marrow layer  422 , and caldarium inner table  423  remain intact. 
     Alternatively, System enclosure  434  is implanted beneath the scalp  10  and beneath the pericranium  420 , directly overlying the caldarium  9 . In this embodiment, the pericranium  420  serves to secure System enclosure  434  in place against the caldarium  9 . 
       FIG. 28  depicts system enclosure  434  secured to the calvarium  9  using screws  429 ,  431  which are shown inserted through screw mounts  428 ,  430 , respectively, into calvarium  9 . Additional or fewer screws and screw mounts may be used without departing form the present invention. Screws  429 ,  431  may penetrate in depth to the level of the calvarium outer table  421 , calvarium marrow layer  422 , or calvarium inner table  423 . 
       FIG. 29  depicts a lower profile design in which the system enclosure  434  is partially recessed into the calvarium  9 . Protruding component  432  lies above the outer surface of calvarium  9 , and recessed component  433  lies below the outer surface of calvarium  9 . Recessed component  433  is shown occupying volume previously occupied by calvarium outer table  421 . Recessed component  433  may also occupying volume previously occupied by calvarium marrow layer without departing from the present invention. 
       FIG. 30  depicts a lower profile design in which the system enclosure  434  is partially recessed into the calvarium  9 . Protruding component  432  lies above the outer surface of calvarium  9 , and recessed component  433  lies below the outer surface of calvarium  9 . Recessed component  433  is shown occupying volume previously occupied by calvarium outer table  421 , calvarium marrow  422 , and calvarium inner table  423 . System enclosure  434  is shown secured to the calvarium outer table  421  portion of calvarium  9  using screws  429 ,  431  which are shown inserted through screw mounts  428 ,  430 , respectively. Additional or fewer screws and screw mounts may be used without departing form the present invention. Screws  429 ,  431  may penetrate in depth to the level of the calvarium outer table  421 , calvarium marrow layer  422 , or calvarium inner table  423 . 
       FIG. 31  depicts a lower profile design in which the system enclosure  434  is fully recessed into the calvarium  9 . Recessed component  433  lies below the outer surface of calvarium  9 . Recessed component  433  is shown occupying volume previously occupied by calvarium outer table  421 , calvarium marrow  422 , and calvarium inner table  423 . System enclosure  434  is shown secured to the calvarium outer table  421  portion of calvarium  9  using screws  429 ,  431  which are shown inserted through screw mounts  428 ,  430 , respectively. Additional or fewer screws and screw mounts may be used without departing form the present invention. Screws  429 ,  431  may penetrate in depth to the level of the calvarium outer table  421 , calvarium marrow layer  422 , or calvarium inner table  423 . 
       FIG. 32  depicts a lower profile design in which the system enclosure  434  is fully recessed into the calvarium  9 . Recessed component  433  lies below the outer surface of calvarium  9 . Recessed component  433  is shown occupying volume previously occupied by calvarium outer table  421 , calvarium marrow  422 , and calvarium inner table  423 . In one embodiment shown, enclosure inner surface  427  is shown extending below the level of calvarium inner table  423 . This design facilitates close proximity of stimulating and recording unit  43  to underlying neural tissue  354  and brain  251 , valuable in sensing neural activity and in delivering an output neuromodulation signal (NMS)  998  via intracranial stimulating electrode array  37 , shown in  FIG. 1  and  FIG. 2 . 
     System enclosure  434  is shown secured to the calvarium inner table  423  portion of calvarium  9  using screws  429 ,  431  which are shown inserted through screw mounts  428 ,  430 , respectively. Additional or fewer screws and screw mounts may be used without departing form the present invention. Alternate means of mechanically attaching system enclosure  434  to calvarium  9  may be employed without departing from the present invention. 
       FIG. 33  depicts the neurological control system  999  employing a lower profile design in which the system enclosure  434  is recessed into the calvarium  9 . Power delivery unit  413 , via generated magnetic flux, transmits power to power conversion unit  378 , which transmits power via power cable  379  to stimulating and recording circuit  26 , part of stimulating and recording unit  43 , contained within system enclosure  434 . Stimulating and recording circuit  26  is electrically connected via connecting cable  8  to intracranial catheter  7  which provides electrical connection to intracranial stimulating electrode array  37  and intracranial recording electrode array  38 . Intracranial catheter is mechanically secured via catheter anchor  29  to calvarium  9 . 
       FIG. 34  depicts the neurological control system  999  employing a lower profile and more compact design in which the system enclosure  434  is recessed into the calvarium  9 . Power delivery unit  413 , via generated magnetic flux, transmits power to power conversion unit  378 , which transmits power via power cable  379  to stimulating and recording circuit  26 , part of stimulating and recording unit  43 , contained within system enclosure  434 . 
     Stimulating and recording circuit  26  is in close proximity to intracranial catheter  7 , this includes but is not limited to a preferred embodiment in which stimulating and recording circuit  26  is recessed into calvarium at the site surrounding the entry point at which intracranial catheter  7  passes from outside the calvarium to inside the calvarium. Stimulating and recording circuit  26  is electrically connected directly or via connecting cable  8  to intracranial catheter  7  which provides electrical connection to intracranial stimulating electrode array  37  and intracranial recording electrode array  38 . Intracranial catheter is mechanically secured via catheter anchor  29  to calvarium  9 . 
     An advantage of the closed-loop system includes the natural variability of the output signal. The nervous system exhibits a natural tendency to attenuate responses to continuous stimuli, such as continuous background noises. A natural example includes imperceptibility of stationary visual images on frog retina. A related example is electrical stimulation of peripheral nerves, such as stimulation of the tibial or peroneal nerves for use in eliciting the flexion withdrawal reflex to facilitate gait restoration in paraplegics. With time, these reflexes attenuate or habituate, reducing their effectiveness. 
     The time-varying nature of a closed-loop signal, responding to environmental and system fluctuations and noise, the natural neural process of habituation to constant signals will be reduced. 
     Furthermore, by specifically and intentionally altering the output signal, such as intermittently reducing the amplitude of the stimulus, and allowing the closed-loop system to compensate in between these intentional amplitude restrictions, further reduction and prevention of habituation is achieved. 
     As shown previously, ventricular electrode catheter  1  is shown positioned within the lateral ventricle  2 , with ventricular electrode  17  in contact with the CSF fluid  18  in lateral ventricle  2 . Cortical electrode  3  is in contact with the CSF fluid overlying the cerebral cortex  19 . Cortical electrode  3  may be placed over any portion of the cerebral or cerebellar cortex without departing from the present invention. Hippocampal electrode  4  is shown underlying the hippocampal regions of the cerebrum. 
     The parenchyma of the nervous system is used as a dielectric to establish a voltage potential gradient between any of the CSF reservoirs, including the lateral ventricles, third ventricle, cerebral aqueduct, fourth ventricle, foramen of lushke, foramen of magende, basal cisterns, CSF overlying the cerebellar hemispheres, CSF overlying the cerebral hemispheres, central spinal canal, CSF overlying the spinal cord, and CSF overlying the spinal nerves and roots. 
     Patients currently implanted with neuromodulation devices must undergo repeat surgery to replace implanted pulse generators every 3-5 years to replace units when batteries fail. Tradeoffs between acceptable implanted device size and limited energy density with current battery technology are responsible for this pervasive problem. Many of these patients are older, and the risks of general anesthesia and surgery are not insignificant. Subjecting these patents to these large risks is a pervasive problem in the field of neuromodulation. The present invention overcomes these limitations with a clever design employing a novel external radiofrequency power delivery system. 
     The present embodiment of the invention includes an external coil use of Limited energy density because technology is limited. 
     The present embodiment of this invention teaches a device, method of implantation, and surgical tools for the rapid implantation of a neuromodulation system. Current devices suffer form the need to implant a pulse generator in the subclavicular pocket or other site remote form the site of electrode implantation. Because of this, subcutaneous cables must be implanted to connect the implanted circuit to the implanted stimulating electrode. The present invention teaches a device and method for implanting the circuit in close proximity to the site of stimulation. The design taught herein obviates the need for any subcutaneous cables. Additionally, it teaches a compact design that allows placement of the implanted circuit and the implantation of the intracranial electrode catheter through a single hole. This is a substantial improvement, facilitating much more rapid implantation and eliminating the need for subcutaneous cable implantation. As a result, surgical procedures are much faster and may be performed under local anesthesia, no longer requiring general anesthesia. This substantially increases the market size, allowing implantation in older and frail patients who might otherwise not benefit from neuromodulation technology because of their being poor surgical candidates, due to their inability to safely tolerate general anesthesia and long surgical procedures. 
       FIGS. 35 and 36  depict the neurological control system  999  employing a lower profile and more compact design in which the system enclosure  434  is recessed into the calvarium  9 , shown in the anteroposterior and lateral projections, respectively. Power delivery unit  413 , is included within system enclosure  434 ; however, power delivery unit  413  may also be external to system enclosure  434  without departing from the present invention. 
     Stimulating and recording circuit  26  is in close proximity to intracranial catheter  7 , this includes but is not limited to a preferred embodiment in which stimulating and recording circuit  26  is recessed into calvarium at the site surrounding the entry point at which intracranial catheter  7  passes from outside the calvarium to inside the calvarium. Stimulating and recording circuit  26  is electrically connected to intracranial catheter  7  which provides electrical connection to intracranial stimulating electrode array  37  and intracranial recording electrode array  38 . Intracranial catheter is mechanically secured to system enclosure  434 . System enclosure  434  is secured to calvarium  9  via mechanical attachment  424 , which is attached to system enclosure  434  via machine screw  439  or equivalent means and to calvarium  9  via bone screw  438  or equivalent means. Catheter recess  441  provides space for establishment of electrical and mechanical connection of stimulating and recording circuit  26  to intracranial catheter  7 . Catheter mount socket  437  and catheter mount ball  436 . 
       FIG. 37  shows an expanded view of the neurological control system  999  also shown in  FIGS. 35 and 36 . 
       FIGS. 38 and 39  depict a Caldarium drill bit is used to create a circular hole in the calvarium. The present invention teaches a major advance in the expeditiousness of the implantation procedure for creating a craniotomy, with particular relevance to the implantation of a neuromodulation device recessed in the calvarium. In a single pass, a drill bit creates a hole of a diameter similar to that of the implanted device. The outer diameter portion is created by calvarium bit outer diameter segment  443 . Attached to and deep to calvarium bit outer diameter segment  443  is the calvarium bit inner diameter segment  442 , which creates a hole in the calvarium of a smaller diameter. The resulting geometry, as seen in  FIG. 38  and  FIG. 39 , produces bone ledge  447 . Bone ledge  447  serves to provide mechanical support to system enclosure  434 , preventing system enclosure  434  from becoming displaced and impinging upon brain  251 . 
     Intracranial catheter  7  is secured to system enclosure  434  by means of catheter mount ball  436 . A compressible material is either adjacent to or comprises catheter mount ball  436 . 
       FIG. 40  depicts the path of the intracranial catheter  7  and its connection to the electrical elements of stimulating and recording circuit  26 , which is contained within system enclosure  434  and in close proximity to intracranial catheter  7 . Intracranial Catheter Proximal End  451  is largely contained within or at least has a component that occupies a portion of the catheter recess  441 . Intracranial Catheter Proximal Electrode  452  and Intracranial Catheter Proximal Electrode  453  are shown. Additional electrodes or alternate connection means may be employed without departing from the present invention. 
     In one embodiment, set screws are used to electrically connect electrodes on intracranial catheter  7  to contacts mounted on system enclosure  434 . Electrode contacts and set screw are recessed in catheter recess  441  in system enclosure  434 . 
     System enclosure is constructed from a castable material, such as potted epoxy, methylmethacrylate, or other plastic, silicone, or polymer. RF electrodes are included within the system enclosure. A titanium or stainless steel shield may be included into the center of the system enclosure  434 . The outer surface of system enclosure  434  may have a convex shape, to better approximate the shape of the calvarium removed and replaced by system enclosure  434 , and the convex shape of outer surface of system enclosure  434  minimized stress concentrations on the overlying scalp  10 . 
     Disease state is estimated using a measure or estimate of chaos of neural activity. Such measures include Lyupanov functions and other measures of chaos or system synchronicity or correlation. 
     Bottom surface of system enclosure  434  may include electrodes used for at least one of recording, stimulation, ground, or reference electrode functions. 
     An array of at least one EEG electrode is used for recording electroencephalographic signals. Said EEG electrode may be placed in at least one of a subgaleal location, subcutaneous location, epidural location, subdural location, intracerebral location, or other location enabling EEG electrode to sense neural activity. 
     An advancement in intracranial catheter design is taught in the present invention. Intracranial catheter  7  includes a microelectrode channel  448 . Microelectrode  449  is inserted through microelectrode channel  448 , with microelectrode tip  450  protruding beyond tip of intracranial catheter  7 . Microelectrode tip  450  is used to record single cell activity to identify neural structures during advancement of intracranial catheter  7  through brain  251 . At the completion of the insertion of intracranial catheter  7  through brain  251 , microelectrode  449  may be removed from intracranial catheter  7 . 
     This allows microelectrode recording and implantation of intracranial catheter  7  during a single pass, saving substantial time during the implantation procedure. 
     During and Following implantation, closed-loop optimization of electrical field shaping is performed by control circuit  72 . 
     Disease state is characterized by a measure of correlation between neural signals measured from neural tissue. Disease State DS is a vector of individual disease states, including intrinsic disease states DS I  and extrinsic disease states DS E : 
       DS=(DS I DS E ] 
     Intrinsic disease states and extrinsic disease states are, themselves vectors of individual disease states: 
       DS I =[DS I1 DS I2 DS I3  . . . DS IN ] 
       DS E =[DS E1 DS E2 DS E3  . . . DS EM ] 
     Intrinsic Disease States include those disease states which characterize the state of disease at a given point in time. Extrinsic Disease States include variations of intrinsic disease states, including but not limited to cyclical variations in Intrinsic Disease States, variations in Intrinsic The fifth intrinsic disease state DS 1 s represents the level of correlation between neural activity in multiple areas of the nervous system. 
       DS I5   =C   N    
     Where Normalized Correlation Magnitude matrix CN is given by: 
     
       
         
           
             CN 
             = 
             
               [ 
               
                 
                   
                     
                       C 
                       
                         1 
                         , 
                         1 
                       
                     
                   
                   
                     
                       C 
                       
                         1 
                         , 
                         2 
                       
                     
                   
                   
                     
                       C 
                       
                         1 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         1 
                         , 
                         M 
                       
                     
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     
                       C 
                       
                         2 
                         , 
                         1 
                       
                     
                   
                   
                     
                       C 
                       
                         2 
                         , 
                         2 
                       
                     
                   
                   
                     
                       C 
                       
                         2 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         2 
                         , 
                         M 
                       
                     
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     
                       C 
                       
                         3 
                         , 
                         1 
                       
                     
                   
                   
                     
                       C 
                       
                         3 
                         , 
                         2 
                       
                     
                   
                   
                     
                       C 
                       
                         3 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         3 
                         , 
                         M 
                       
                     
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     … 
                   
                   
                     … 
                   
                   
                     … 
                   
                   
                     … 
                   
                   
                     … 
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     
                       C 
                       
                         M 
                         , 
                         1 
                       
                     
                   
                   
                     
                       C 
                       
                         M 
                         , 
                         2 
                       
                     
                   
                   
                     
                       C 
                       
                         M 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         M 
                         , 
                         M 
                       
                     
                   
                   
                     
                         
                     
                   
                 
               
               ] 
             
           
         
       
     
     Which becomes: 
     
       
         
           
             CN 
             = 
             
               [ 
               
                 
                   
                     1 
                   
                   
                     
                       C 
                       
                         1 
                         , 
                         2 
                       
                     
                   
                   
                     
                       C 
                       
                         1 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         1 
                         , 
                         M 
                       
                     
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     
                       C 
                       
                         2 
                         , 
                         1 
                       
                     
                   
                   
                     1 
                   
                   
                     
                       C 
                       
                         2 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         2 
                         , 
                         M 
                       
                     
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     
                       C 
                       
                         3 
                         , 
                         1 
                       
                     
                   
                   
                     
                       C 
                       
                         3 
                         , 
                         2 
                       
                     
                   
                   
                     1 
                   
                   
                     … 
                   
                   
                     
                       C 
                       
                         3 
                         , 
                         M 
                       
                     
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     … 
                   
                   
                     … 
                   
                   
                     … 
                   
                   
                     … 
                   
                   
                     … 
                   
                   
                     ⋮ 
                   
                 
                 
                   
                     
                       C 
                       
                         M 
                         , 
                         1 
                       
                     
                   
                   
                     
                       C 
                       
                         M 
                         , 
                         2 
                       
                     
                   
                   
                     
                       C 
                       
                         M 
                         , 
                         3 
                       
                     
                   
                   
                     … 
                   
                   
                     1 
                   
                   
                     
                         
                     
                   
                 
               
               ] 
             
           
         
       
     
     This matrix or a weighted sum of its components represents an additional measure of disease state. This has broad applications in neurological disease quantification and also has particular relevance to measurement of tremor and assessment of seizure activity as well as prediction of likelihood and onset of seizure activity. 
     
       
         
           
             CN 
             = 
             
               
                 
                   C 
                   
                     1 
                     , 
                     2 
                   
                 
                 · 
                 
                   W 
                   
                     1 
                     , 
                     2 
                   
                 
               
               + 
               
                 
                   C 
                   
                     1 
                     , 
                     3 
                   
                 
                 · 
                 
                   W 
                   
                     1 
                     , 
                     3 
                   
                 
               
               + 
               … 
               + 
               
                 
                   C 
                   
                     1 
                     , 
                     M 
                   
                 
                 · 
                 
                   W 
                   
                     1 
                     , 
                     M 
                   
                 
               
               + 
               
                 
                   C 
                   
                     2 
                     , 
                     1 
                   
                 
                 · 
                 
                   W 
                   
                     2 
                     , 
                     1 
                   
                 
               
               + 
               
                 
                   C 
                   
                     2 
                     , 
                     3 
                   
                 
                 · 
                 
                   W 
                   
                     2 
                     , 
                     3 
                   
                 
               
               + 
               … 
               + 
               
                 
                   C 
                   
                     2 
                     , 
                     M 
                   
                 
                 · 
                 
                   W 
                   
                     2 
                     , 
                     M 
                   
                 
               
               + 
               
                 
                   C 
                   
                     3 
                     , 
                     1 
                   
                 
                 · 
                 
                   W 
                   
                     3 
                     , 
                     1 
                   
                 
               
               + 
               
                 
                   C 
                   
                     3 
                     , 
                     2 
                   
                 
                 · 
                 
                   W 
                   
                     3 
                     , 
                     2 
                   
                 
               
               + 
               … 
               + 
               
                 
                   C 
                   
                     3 
                     , 
                     M 
                   
                 
                 · 
                 
                   W 
                   
                     3 
                     , 
                     M 
                   
                 
               
               + 
               
                 … 
                  
                 
                     
                 
                  
                 
                   
                     C 
                     
                       M 
                       , 
                       1 
                     
                   
                   · 
                   
                     W 
                     
                       M 
                       , 
                       1 
                     
                   
                 
               
               + 
               
                 
                   C 
                   
                     M 
                     , 
                     2 
                   
                 
                 · 
                 
                   W 
                   
                     M 
                     , 
                     2 
                   
                 
               
               + 
               
                 
                   C 
                   
                     M 
                     , 
                     3 
                   
                 
                 · 
                 
                   W 
                   
                     M 
                     , 
                     3 
                   
                 
               
               + 
               … 
             
           
         
       
     
     Where 
     C I,J =Correlation between signal I and signal J
 
W I,J =Weighting factor for Correlation between signal I and signal J
 
     The sixth intrinsic disease state DS I6  represents the level of chaos in neural activity in a single or multiplicity of areas of the nervous system. This may be implemented as any measure of entropy or chaos, including variance, standard deviation, Lyupanov exponent values, maximum Lyupanov exponent value, or other measure of entropy or chaos without departing from the present invention. 
       DS I6   =SN    
       DS I6   =S   I   ·W   SI   +S   2   ·W   S2   +S   3   ·W   S3   + . . . +S   N   ·W   SN    
     Where 
     S I =Entropy measure for signal I, which may be implemented in any of several ways outlined above, some of which are detailed below. 
     W SI =Weighting factor for Entropy measure for signal 
     Chaos measurement implemented and quantified as Entropy: 
     
       
         
           
             
               
                 
                   
                     S 
                     I 
                   
                   = 
                     
                    
                   
                     Entropy 
                      
                     
                         
                     
                      
                     measure 
                      
                     
                         
                     
                      
                     for 
                      
                     
                         
                     
                      
                     signal 
                      
                     
                         
                     
                      
                     I 
                   
                 
               
             
             
               
                 
                   = 
                     
                    
                   
                     
                       - 
                       k 
                     
                      
                     
                       ∫ 
                       
                         
                           [ 
                           
                              
                             X 
                           
                           ] 
                         
                          
                         
                           P 
                            
                           
                             [ 
                             
                               X 
                               I 
                             
                             ] 
                           
                         
                          
                         log 
                          
                         
                             
                         
                          
                         
                           P 
                            
                           
                             [ 
                             
                               X 
                               I 
                             
                             ] 
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
     Where 
     k=a constant, i.e. Boltzmann&#39;s constant as in thermodynamics, or by convention a dimensionless constant in information theory; 
     X I =Signal I, such as EEG voltage signal I or implanted electrode signal I or microelectrode voltage signal 
     P[X I ]=Probability distribution of Signal I 
     dX=integration variable 
     In a typical implementation in digital hardware, based upon a base 2 digitization scheme, chaos measurement implemented and quantified as Entropy becomes: 
         S   I   =ΣP   i  log 2  pi bits 
     Where 
     S I =Entropy measure for signal I 
     P i =probability of outcome i, for example of sensed voltage being within a particular discretization bin window or value range. 
     log 2  pi=Log base 2 of pi, alternative bases could be used, but 2 is typically chosen to be consistent with digital hardware, which is implemented using the binary (base 2) system. 
     Chaos measurement implemented and quantified as Lyupanov exponent, which is defined as: 
         S   I   =L =(1/Σ k=1   M   Dt   k )*Σ k=1   M   L   k  bits per second
 
     Where 
     L=Lyupanov Exponent 
     L k =Satisfies the condition: L(t k )=L(t k - I )*2 Lk * Dtk    
     Dt k =t k -t k - I  is the evolution time of L(t k ) 
     L(t k ) is the distance between two close points in phase space at time tk 
     S I =Entropy measure for signal I, calculated with Lypoanov Exponent 
     Other measures of chaos, including variations of these and other measures or estimates for chaos, may be used without departing from the present invention. 
       FIGS. 37 and 40  show system enclosure in cross section, in which intracranial catheter  7  traverses catheter mount ball  436 , which rotates within catheter mount socket  437 , providing a swivel mechanism to facilitate the selection of a continuum of potential intracranial target sites for intracranial electrode array and intracranial catheter using a single mounted position for system enclosure  434  on caldarium  9 . 
     The reader is requested to note the following labels on  FIG. 37 : 
     Catheter mount ball  436   
     Catheter mount socket  437   
     stimulating and recording unit  43   
       FIG. 41  depicts one design for the system enclosure for implantation in the calvarium. Mechanical attachment  424  is shown in plurality, facilitating mechanical attachment of system enclosure  434  to calvarium  9 , including calvarium outer table  421  or calvarium inner table  423 , calvarium stabilization lip  501 , or other portion of calvarium without departing from the present invention. Alternatively, other attachment means may be fashioned to perform the equivalent function of attaching system enclosure  434  to calvarium  9  without departing from the present invention. A single or plurality of screw  429  is used to perform the attachment of mechanical attachment  424  to calvarium  9  and to system enclosure  434 . Mechanical attachment may be implemented as a cranial plate, craniofacial plate, or other form well known to neurosurgeons, or it may be implemented in another equivalent fashion without departing form the present invention. 
     Electrode  452 ,  453 ,  454 ,  455 ,  456 ,  457 ,  458 ,  459 , are shown on the end of intracranial catheter  7 , said electrode facilitate contact between intracranial electrodes  246 , and circuitry enclosed within system enclosure  434 , including but not limited to output stage, signal conditioning circuit, signal processing circuit, control circuit, and other circuit components. 
       FIGS. 42 and 43  depict a dual intracranial catheter design, shown from above and shown in cross section profile implanted in a patient, respectively. Mechanical attachment  424  are as described in  FIG. 41 . Intracranial catheter  7  and Electrode  452 ,  453 ,  454 ,  455 ,  456 ,  457 ,  458 ,  459  are as described in  FIG. 41 . Electrode  476 ,  477 ,  478 ,  479 ,  480 ,  481 ,  482 ,  483  are shown on the end of intracranial catheter  500 . A multiplicity of intracranial catheter y or intracranial catheter  500  may be employed without departing from the spirit of the present invention. In  FIG. 43 , a plurality of bone screw  438  is shown facilitating mechanical attachment of mechanical attachment  424  to caldarium  9 . In  FIG. 43 , a plurality of machine screw  439  is shown facilitating mechanical attachment of mechanical attachment  424  to system enclosure  434 . In  FIG. 43 , catheter recess  441  is seen in cross section, providing space for intracranial catheter  7  and intracranial catheter  500  to minimize the profile or height of system enclosure and avoid surface protuberances that could cause ulceration of overlying portions of scalp  10 . 
       FIG. 44  is a schematic diagram of one embodiment of the intracranial stimulator of the present invention implanted bilaterally in a human patient. In the embodiment illustrated in  FIG. 44 , two neurological control systems  999  are shown implanted bilaterally. Each system  999  includes a stimulating and recording unit  26  and one or more intracranial components described below. As described in this illustrative embodiment, the intracranial components preferably include a stimulating electrode array  37 . However, it should become apparent to those of ordinary skill in the relevant art after reading the present disclosure that the stimulating electrodes may also be extracranial; that is, attached to a peripheral nerve in addition to or in place of being located within the cranium. 
       FIG. 45  is a schematic diagram of one embodiment of the neurological control system  999  implanted unilaterally in a human patient. A multiplicity of components or multiplicity of entire systems may be implanted unilaterally or bilaterally without departing from the present invention. In the embodiment illustrated in  FIG. 45 , one neurological control systems  999  is shown implanted unilaterally in the temporal lobe, shown in an orientation intersecting the hippocampus  277 . Each system  999  includes a stimulating and recording unit  26  and one or more intracranial components described in detail above in parent cases. As described in this illustrative embodiment, the intracranial components preferably include an intracranial catheter  7  with at least one of a sensor array  560  or neuromodulator array  561 . Sensor array  560  may be implemented as a intracranial recording electrode array  38 , EEG electrode array  51 , or other form of sensory input including sensory input modalities  247  shown in  FIG. 1  or other form, including optical, metabolic, chemical, or other sensor, without departing form the present invention. Neuromodulator array  561 , shown on intracranial catheter  7 , serve to stimulate, inhibit, or both, neural activity. Neuromodulator array  561  may be on the same intracranial catheter  7  as sensor array  560  or on a second intracranial catheter  500 , as shown in  FIG. 46 , on a multiplicity of intracranial catheter  7 , or on other structure, without departing from the present invention. There may be partial or complete overlap in elements that comprise neuromodulator array  561  and sensor array  560 . For example, neural interface array  562  may be implemented as electrode array  563 , some or all of elements of intracranial recording electrode array  38  and elements of intracranial stimulating electrode array  37  may be shared. Various forms of multiplexing, in various dimensions, including time, space, frequency, phase, or other dimension, may be employed to facilitate dual or multiple function of elements of sensor array  560  and neuromodulator array  561 , to one skilled in the art, without departing form the present invention. It should become apparent to those of ordinary skill in the relevant art after reading the present disclosure that the stimulating electrodes may also be extracranial; that is, attached to a peripheral nerve or other location such as on the surface of the skin in addition to or in place of being located within the cranium. 
       FIG. 46  is a schematic diagram of one embodiment of the neurological control system  999  implanted unilaterally in a human patient. A multiplicity of components or multiplicity of entire systems may be implanted unilaterally or bilaterally without departing from the present invention. In the embodiment illustrated in the sagittal view diagram in  FIG. 46 , one neurological control systems  999  is shown implanted unilaterally, with two intracranial catheters, intracranial catheter  7  and intracranial catheter  500 , in the right thalamus  335  and the right hippocampus  564 , respectively. The same or a multiplicity of neurological control systems  999  may be placed in contralateral structures, including left thalamus  336  and left hippocampus  565 , and other structures including the right anterior nucleus of the thalamus, left anterior nucleus of the thalamus, right subthalamic nucleus, left subthalamic nucleus, right substantia nigra, left substantia nigra, other single structures, or other multiplicity of structures. 
     Modulation of right hippocampus  564  is also accomplished via hippocampal modulator  534  and hippocampal modulator  535 . Modulation of left hippocampus  565  is also accomplished via hippocampal modulator  536  and hippocampal modulator  357 . Cortex  252  is modulated via cortical modulator  530 , cortical modulator  531 , cortical modulator  532 , and cortical modulator  533 . 
       FIG. 46  also shows cortical modulator  530 , cortical modulator  531 , cortical modulator  532 , cortical modulator  533 , hippocampal modulator  534 , hippocampal modulator  535 , hippocampal modulator  536 , and hippocampal modulator  537 , which facilitate modulation of multiple regions of cortex  252 , right hippocampus  564 , and left hippocampus  565 . 
       FIG. 46  and  FIG. 47  are sagittal and lateral view diagrams, respectively, depicting a multiplicity of neural interfacing nodes. Neural interfacing is accomplished with olfactory nerve  525  via at least one of olfactory nerve modulator  527  and with trigeminal nerve  368  via at least one of trigeminal nerve modulator  528 . 
       FIG. 47  depicts neural interfacing with vagus nerve  369  via at least one of vagus nerve modulator  529 , with baroreceptor  370  via baroreceptor modulator  566 , with sympathetic ganglion  371  via sympathetic modulator  567 . Modulation of these structures is used for control of neurological state in the treatment of neurological disease as well as hypertension and hypotension. 
       FIG. 47  also depicts several noninvasive techniques for the treatment of neurological disease. Orbitofrontal modulator  279 , prefrontal modulator  280 , precentral modulator  281 , postcentral modulator  282 , parietal modulator  283 , parietooccipital modulator  284 , occipital modulator  285 , cerebellar modulator  286  are shown overlying the scalp  10 . These may be implemented as electromagnetic coils or as electrodes, to produce electromagnetic waves for the induction of current or to directly produce currents, respectively. There may also be implemented optically or using other modality for the modulation of neurological activity. These neuromodulators may be located at any anatomical depth, including superficial to the scalp, implanted at any level including but not limited to within the scalp, under the scalp, recessed in the caldarium, deep to the caldarium, in the epidural space, in the subdural space, overlying the cortex, within the cortex, or deep to the cortex, without departing from the present invention. Neuromodulators may take the form of macroelectrodes, macroelectrode arrays, microelectrodes, microelectrode arrays, nerve cuffs, other designs described in the present invention, other electrode designs known in the art, and other neural interfaces to be developed, without departing form the present invention. 
     Neural chaos is modulated by the controlled introduction of energy into a single or multiplicity of neural structures including but not limited to olfactory nerve  525 , trigeminal nerve  368 , vagus nerve  369 , cutaneous nerves, other cranial nerves, subcutaneous nerve endings, cortex  252 , cerebral cortex, cerebellar cortex  257 , deep brain structures  349 , thalamus  121 , subthalamic nucleus  122 , basal ganglia, locus ceruleus, any portion or portions of Papez&#39; circuit, hippocampus, amygdala, fomix, subthalamic nucleus, anterior nucleus of the thalamus, prepyriform cortex, solitary nucleus, dorsal column nucleus, cerebellar nuclei, caudate, putamen, corpus callosum, other nuclei, other tracts, other nerves, other nuclei, other neural structures, and other non-neural structures. 
       FIG. 48  shows dermatomal zones, revealing specific areas of innervation of skin  382  by underlying nerves. Dermatomal zone  538 , dermatomal zone  539 , and dermatomal zone  540  are innervated by trigeminal nerve branches  1 ,  2 , and  3 , respectively. Dermatomal zone  541 , dermatomal zone  542 , and dermatomal zone  543  are innervated by cervical nerve roots C 2 , C 2 , and C 3 , respectively. 
       FIG. 49  depicts two implementations of noninvasive versions of neurological control system  999 ; these are shown as neurological control system  994  which is preferably mounted on the had and neurological control system  995  which is preferably mounted on the neck. 
     Neurological control system  994  comprises head band  568 , recording and stimulating unit  555 , connecting cable  556 , connecting cable  557 , and neuromodulator  545 , neuromodulator  546 , neuromodulator  547 , neuromodulator  548 , neuromodulator  549 , neuromodulator  550 , neuromodulator  551 , neuromodulator  552 , and an additional set, which may be symmetrical or asymmetrical, on the contralateral side. 
     Neurological control system  995  comprises head band  569 , recording and stimulating unit  570 , connecting cable  558 , connecting cable  559 , and neuromodulator  553 , neuromodulator  554 , and an additional set, which may be symmetrical or asymmetrical, on the contralateral side. 
     Any single or plurality of said dermatomal zone  538 ,  539 ,  540 ,  541 ,  542 ,  543  may be modulated using implanted nerve cuff electrode disclosed in pending patent application Ser. No. 10/198,871 (GISTIM) and the cited provisional Appln. No. 60/307,124. Other techniques for modulating innervation to these regions may be employed without departing from the present invention. These include surface electrical stimulation, magnetic stimulation, transcranial magnetic stimulation, vibrotactile stimulation, thermal stimulation, pressure stimulation, optical stimulation, or other stimulation modality. 
       FIG. 50  depicts a functional block diagram of the neurological control system  999 , connected to intracranial catheter  7 , shown implanted in the temporal lobe  571 , intersecting the hippocampus  277 . In this diagram, signal from sensor array  560  is conditioned, conducted along intracranial recording electrode (ICRE) signal path  83  to signal processor  71 , which generates control input (Y)  996 , which is conducted to control circuit  72 , which generates control output (U)  997 , which is conducted to Output stage circuit  77 , which generates neuromodulating signal (NMS)  998 , which is conducted along stimulator output path  111  to neuromodulator array  561 . Neuromodulator array  561  and sensor array  560  are implemented as intracranial recording electrode array (ICREA)  38  and intracranial stimulating electrode array (ICSEA)  37 , in one preferred embodiment. In the present invention, control input (Y)  996  is a function of at least one of neural chaos, T-index, neural signal correlation, neural signal cross-correlation, and neural synchronization. Control output (U)  997  and neuromodulatory signal (NMS)  998  are selected to modulate at least one of neural chaos, neural signal correlation, neural signal cross-correlation, and neural synchronization. 
       FIG. 51  depicts the same functional block diagram as in  FIG. 50  with the addition of a second intracranial catheter  500  positioned to intersect at least one of the thalamus  121 , subthalamic nucleus  122 , or other anatomical target, including at least one of the centromedian nucleus, substantia nigra, locus ceruleus, reticular activating center, nucleus solitarus, or other neural structure or non-neural structure. 
       FIG. 52  depicts the same functional block diagram as in  FIG. 51  with the addition of a time diagram of the control input (Y)  996  and control output (U)  997  shown versus time. The control input (Y)  996  is shown to vary with time and to remain within the bounds delimited by threshold  572  and threshold  575 . Control output (U)  997  is shown to vary with time in a manner designed to maintain control input (Y)  996  within the range specified, below threshold  572  and above threshold  575 . Any of the control laws specified in this patent as well as any others may be used without departing form the present invention. In  FIG. 52 , signals form a nonlinear control law is shown; as the disease state varies and approaches threshold  575  toward the center of the diagram, control output (U)  997  is seen to increase and drive control input (Y)  996  back toward the center of the range delimited by threshold  572  and threshold  575 . 
       FIG. 53  depicts the same functional block diagram and time diagram as in  FIG. 52  with the addition of a threshold  573 , threshold  574 , and target value  576 . In this time diagram, control output (U)  997  is shown to vary in accordance with a more complex nonlinear control law. The variation is representative of a piecewise linear control law, which response differently when the control input (Y) is in different regions as defined by threshold  572 , threshold  573 , threshold  574 , threshold  575 , and target value  576 . In this embodiment, as control input (Y)  996  diverges farther from target value  576 , the absolute value of the magnitude of control output (U)  997  becomes larger. The vertical center of the time diagram for control output (U) may be interpreted as being zero, though other offsets and baseline values may be used without departing form the present invention. As disease state enters the range delimited by threshold  572  and threshold  573 , the gain of control law is seen to increase, as reflected by the incremental increase in magnitude of control output (U)  997 . As disease state enters the range delimited by threshold  574  and threshold  575 , the gain of control law is seen to increase, as reflected by the incremental increase, in the opposite polarity, in magnitude of control output (U)  997 . This behavior demonstrates a control law designed to maintain control input (Y)  996  within the operating range defined by threshold  573  and threshold  574 , shown as target range  577  and target range  578 , which are above and below target value  576 , respectively. As is shown subsequently, state (X)  991  is a scalar or vector of value representative of neurological state and disease state. Components of state (X)  991  may be identical to or functions of control input (Y). Furthermore, the same, similar, or different control laws and systems may be employed to control the behavior of state (X)  991 . f control input (Y)  996  diverges outside this range and into critical range  579  or critical range  580 , a different control scheme is used. This may be implemented as the same control law with different gains or as a different control law. 
     In  FIG. 53 , the signal path is shown along with the system block diagram, detailed in  FIG. 2 , which is from the original filing of the parent case of this patent. Neural recording signal (NRS)  993  is sensed by sensor array  560 , and is transmitted to signal conditioning circuit  76 . Feedback signal (F)  992  is generated by signal conditioning circuit  76  from neural recording signal (NRS)  993  and transmitted to signal processor  71 . Feedback signal (F) is shown transmitted on Conditioned Intracranial recording electrode (ICRE) signal path  83 , though for a non-implanted neurological control system  999 , a different transmission means would be used, without departing form the present invention. Signal conditioning circuit  76 , may have a unity gain, and equivalently be omitted from neurological control system  999 , without departing from the present or parent case invention. 
     In  FIG. 54 , a time diagram of the control input (Y)  996  and control output (U)  997  shown versus time. In addition, a time diagram of state vector (X)  991 , with components state  1  (X 1 )  991 - 1 , state  2  (X 2 )  991 - 2 , state  3  (X 3 )  991 - 3 , state  4  (X 4 )  991 - 4 , and state  5  (X 5 )  991 - 5 , are shown. More or fewer states, comprising the range of state  1  (X 1 )  991 - 1  to state N (XN)  991 -N, as shown in  FIG. 56 , may be defined without departing from the present invention. 
     In state time diagram shown, state  1  (X 1 )  991 - 1  is identical to control input (Y)  996 . In the application of seizure control, one embodiment for such a state as represented by state  1  (X 1 )  991 - 1  is a function of neural chaos. This may be used to facilitate a proportional control law component in the control of neural chaos. In one preferred embodiment the function of neural chaos includes the disentrainment of neural chaos, inversely related to the synchronization of neural chaos among at least 1 recording electrode or other neural signal transducer. 
     There are a multiplicity of methods and designs in which this can be implemented in the present invention. In one such preferred embodiment, one state may be allocated to represent the measure of neural chaos for each element of neural recording signal (NRS)  993 , as recorded from each recording electrode or signal transducer, including at least one of intracranial recording electrode  5 , intracranial recording electrode  6 , elements of intracranial recording electrode array  38 , elements of neural interface array  562  (shown in  FIG. 45 ), or other neural signal transducer. At least one additional state is defined as a function of these states, specifically the disentrainment of neural chaos, inversely related to the correlation or synchronization of neural chaos, which is a measure of the correlation between these measures of neural chaos derived from elements of neural recording signal (NRS)  993 . These measures of neural chaos and the correlation between neural chaos measurements may be alternatively calculated within signal processor  71  as a neural state or disease state estimate, without departing from the present invention. 
     In state time diagram shown, state  2  (X 2 )  991 - 2  is a time derivative of control input (Y)  996 . In the application of seizure control, one embodiment for such a state as represented by state  2  (X 2 )  991 - 2  is a measure of the first derivative with respect to time of neural chaos or function thereof, which is used to facilitate a differential control law component in the control of said function of neural chaos. 
     In state time diagram shown, state  3  (X 3 )  991 - 3  is a time integral of control Input (Y)  996 . In the application of seizure control, one embodiment for such a state as represented by state  3  (X 3 )  991 - 3  is a measure of the first integral with respect to time of neural chaos or function thereof, which is used to facilitate an integral control law component in the control of said function of neural chaos. 
     In state time diagram shown, state  4  (X 4 )  991 - 4  is a nonlinear function of control input (Y)  996 . In the application of seizure control, one embodiment for such a state as represented by state  4  (X 4 )  991 - 3  is a measure of a nonlinear function of neural chaos, which is used to facilitate a nonlinear control law component in the control of neural chaos or function thereof, including disentrainment of neural chaos or of synchronization of neural chaos. As shown, state  4  (X 4 )  991 - 4  depicts a nonlinear function which is approximately zero during the condition in which control input (Y)  996  is within target range  577  and target range  578  and increases in magnitude as control input (Y) deviates from the region defined by the union of target range  577  and target range  578  and migrates into at least one of critical range  579  and critical range  580 . State  4  (X 4 )  991 - 4  is shown to represent “target range deviation”, a continuous function which is shown as the amount by which state  1  (X 1 ) deviates outside the limits defined by the union of target range  577  and target range  578 . 
     In state time diagram shown, state  5  (X 5 )  991 - 5  is an error signal representing the difference between a constant or time varying reference value and control input (Y)  996 . As such it represents an error signal. It may also be processed to generate additional states, such as a combination of first, second, and higher order derivatives and integrals with respect to time, to produce control laws of arbitrary complexity to facilitate tracking of any or all components of state (X)  991  to a desired reference signal. In the application of seizure control, one embodiment for such a state as represented by state  5  (X 5 )  991 - 5  is a measure of the difference between the actual level of neural chaos and the desired level of neural chaos, such as target value  576  or other signal. 
     These components of state (X)  991 , and other signals and functions thereof, are used to drive the neural chaos to a desired level, such as within the normal level seen during inter-ictal periods, and to prevent the neural chaos form entering critical range  579  or critical range  580 . When neural chaos decreases to a value within critical range  580 , the probability of a seizure is increased. Generation of neuromodulatory signal (NMS)  998  is performed to drive neural chaos out of this region into either of target range  577  or target range  578 , to reduce the probability of a seizure and thereby prevent the occurrence of a seizure. 
     This control system is also used to prevent occurrence of other neurological events, including mania, depression, psychosis, rage, narcolepsy, desire for addicting agents (i.e. opiates, cocaine, nicotine, alcohol, or other drug), or other undesirable neurological state, condition, perception, or symptom. 
     Neuromodulatory signal (NMS)  998  is transmitted to neuromodulator array  561 . Neuromodulator array  561 , encompasses any single or plurality of neuromodulator elements, including neuromodulator  545 , neuromodulator  546 , neuromodulator  547 , neuromodulator  548 , neuromodulator  549 , neuromodulator  550 , neuromodulator  551 , neuromodulator  552 , neuromodulator  553 , neuromodulator  554 , intracranial stimulating electrode  1 , intracranial stimulating electrode  2 , intracranial stimulating electrode  3 , and intracranial stimulating electrode  4 . Any of said neuromodulator elements may be implemented as a singularity or combination of an electrode for the delivery of electrical energy, a drug delivery catheter for the delivery of drug or chemical agent, a microcatheter for the intraparenchymal delivery of drug or agent, an optical element for the delivery of optical energy, a coil or other transceiver for the delivery of electromagnetic energy, ultrasonic transducer for the delivery of ultrasound energy, thermal source for the introduction of thermal energy, thermal sink for the removal of thermal energy, microdialysis device for the introduction or control of chemical concentrations, or other device for the modulation of neural activity. 
     Neuromodulatory signal (NMS)  998  is transmitted to neuromodulator array  561 , according to closed-loop control Jaws described above, to control state (X)  991  in any of several manners or combinations thereof, including control laws to: (1) maintain state (X)  991  at or near a target value (scalar or vector), (2) maintain state (X)  991  within a single or plurality of target ranges, including but not limited to target range  577  and target range  578 , (3) prevent state (X)  991  from entering any critical range, including but not limited to critical range  579  and critical range  580 , (4) to maintain state (X)  991  at or near any constant or time-varying reference value, (scalar or vector), (5) maintain state (X)  991  within a single or plurality of constant or time-varying target ranges, (6) prevent state (X)  991  from entering any constant or time-varying critical range, and (7) other form of closed-loop control. 
     As is typical of control systems, the farther any single or plurality of component of state (X)  991  is from a critical range, such as critical range  579  and critical range  580 , the lower the probability of entering said critical range by said single or plurality of component of state (X)  991 . The larger the difference between a single or plurality of component of state (X)  991  from said critical range, the larger the system dynamic effect, external perturbation, or noise magnitude would be required to drive said single or plurality of component of state (X)  991  into said critical range. For a neurological condition that has an elevated probability of occurring when said single or plurality of component of state (X)  991  are in critical range, the probability of said neurological condition occurring can be minimized if said single or plurality of component of state (X)  991  is kept outside or as far in value as possible from of said critical range. 
     When the present invention is applied to the treatment of epilepsy, at least one component, i.e. state  1  (X 1 )  991 - 1 , of state (X)  991 , is decreases as the probability of a seizure occurring increases. Through the introduction of chaos into the nervous system, the neural chaos is increased, thereby reducing the probability of a seizure occurring. With sufficient control gain, the probability of a seizure occurring can be driven to small values approaching zero; resulting in the prevention of a seizure. 
     State (X)  991  comprises disease state estimate as shown in  FIG. 12 . As taught in the parent case, and is shown in  FIG. 12 , a control error (e) is calculated as the difference between the disease reference state (r) and the disease state estimate (X). Control circuit  72  operates to drive this control error (e) toward zero, such that the disease state estimate (X) follows the disease state reference (r). This same invention, taught in the parent case, is used to control state (X) as also shown in  FIG. 52 , to follow a desired disease reference state. This same invention, taught in the parent case, is also used to control state (X) as also shown in  FIG. 52 , to remain within a desired target range. This same invention, taught in the parent case, is also used to control state (X) as also shown in  FIG. 52 , to remain outside of a critical range. Control law circuit block  231  teaches a configuration for accomplishing each of these control schemes. 
     In the control of neurological state to prevent seizures, the control circuit  72  shown in the parent invention may be employed to perform at least one of (1) maintenance of neurological chaos or function thereof at a reference value, typically defined within the normal inter-ictal range; (2) maintenance of neurological chaos or function thereof within a target range, typically representative of normal inter-ictal chaos range; (3) maintenance of neurological chaos or function thereof outside of a critical range, typically outside of the normal inter-ictal chaos range, (4) maintenance of neurological chaos or function thereof as far as possible from a critical range which is associated with an increased probability of seizure; (5) maintenance of neurological chaos or function thereof at a constant value, (6), maintenance of neurological chaos at a time varying value. Functions of neurological chaos included in the present invention comprise disentrainment of neural chaos, entrainment of neural chaos, synchronization of neural chaos, correlation of neural chaos, differences between actual and reference values of neural chaos, and functions thereof. 
     In  FIG. 55 , a time diagram of the control input (Y)  996  and control output (U)  997  shown versus time. In addition, a time diagram of state vector (X)  991 , with components state  1  (X 1 )  991 - 1 , state  2  (X 2 )  991 - 2 , state  3  (X 3 )  991 - 3 , state  4  (X 4 )  991 - 4 , and state N (XN)  991 -N, are shown. More or fewer states may be defined without departing from the present invention. 
     In this figure, state  1  (X 1 )  991 - 1  is shown to be the same as control input  1  (Y- 1 )  996 - 1 , which represents disentrainment of neural chaos among elements of neural recording signal (NRS)  993 , calculated in this preferred embodiment as a disease state estimate by signal processor  71  and transmitted to control circuit  72 . The calculations may be allocated differently among signal processor  71  and control circuit  72  without departing form the present invention. State  2  (X 2 )  991 - 2  is shown as the first derivative with respect to time of state  1  (X 1 )  991 - 1 . State  3  (X 3 )  991 - 3  is shown as the first integral with respect to time of state  1  (X 1 )  991 - 1   
     As seen in  FIG. 55 , control input  1  (Y 1 )  996 - 1  remains within target range  577  and target range  578 . State  4  (X 4 )  991 - 4  represents a function of the amount by which state  1  (X 1 ) deviates outside the limits defined by the union of target range  577  and target range  578 . In state time diagram shown, state  4  (X 4 )  991 - 4  is a nonlinear function of control input (Y)  996 . In the application of seizure control, one embodiment for such a state as represented by state  4  (X 4 )  991 - 3  is a measure of a nonlinear function of the disentrainment of neural chaos. As seen in related  FIG. 54 , state  4  (X 4 )  991 - 3  depicts a nonlinear function which is approximately zero during the condition in which control input (Y)  996  is within target range  577  and target range  578  and increases in magnitude as control input (Y) deviates from the region defined by the union of target range  577  and target range  578  and migrates into at least one of critical range  579  and critical range  580 . State  4  (X 4 )  991 - 4  is shown to represent “target range deviation”, a continuous function which is shown as the amount by which state  1  (X 1 ) deviates outside the limits defined by the union of target range  577  and target range  578 . In  FIG. 55 , state  4  (X 4 )  991 - 4  remains approximately zero during the time span shown, since control input  1  (Y 1 )  996 - 1  remains within the union of union of target range  577  and target range  578 , suggesting satisfactory closed-loop control performance, specifically in the regulation of the disentrainment of neural chaos. 
     In  FIG. 56 , a time diagram of the unperturbed control input (Y′)  988  and perturbed control input (Y″)  989 , unperturbed control output (U′)  983  and perturbed control output (U″)  984 , unperturbed state (X′)  986 , and perturbed state (X″)  987  in response to perturbation (P)  990 . This figure shows the effect of perturbation (P)  990  on the neurophysiology of patient  227  and signals processed by neurological control system  999 . 
     Unperturbed control input  1  (Y- 1 ′)  988 , unperturbed control output  1  (U- 1 ′)  983 , and unperturbed state  1  (X- 1 ′)  986  represent the neurophysiology of patient  227  and signals processed by neurological control system  999  in the absence of perturbation (P)  990 . Additional elements of any values, including but not limited to unperturbed control input (Y′)  988 , unperturbed control output (U′)  983 , and unperturbed state (X′)  986  are included in the present invention, in a multivariable implementation of a preferred embodiment of the present invention. 
     Perturbed control input  1  (Y- 1 ″)  989 - 1 , perturbed control output  1  (U- 1 ″)  984 - 1 , and perturbed state  1  (X- 1 ″)  987 - 1  represent the neurophysiology of patient  227  and signals processed by neurological control system  999  in the presence of perturbation (P)  990 , such as the application of flashing lights. Additional elements of any values, including but not limited to perturbed control input (Y″)  989 , perturbed control output (U″)  984 , and perturbed state (X″)  987  are included in the present invention, in a multivariable implementation of a preferred embodiment of the present invention. 
     Independence of neural chaos and disentrainment of neural chaos are seen to decrease, reflective of an increase in synchronization of neural chaos, in response to perturbation (P)  990 ; and other state variables, including perturbed state  1  (X 1 ″)  991 - 1 , through perturbed state N (XN″)  991 -N, as described in detail in  FIG. 54  and  FIG. 55 , are shown to respond accordingly. 
     Perturbed control output  1  (U- 1 ″)  984 - 1  is seen to increase relative to unperturbed control output  1  (U- 1 ′)  983 - 1 , as determined by control law implemented in control circuit  72 , in response to the increase in perturbed state  1  (X 1 ″)  987 - 1 . This incremental increase in perturbed control output  1  (U- 1 ″)  984 - 1  causes a corresponding incremental increase in neuromodulating signal (NMS)  998 , which causes an incremental increase in the level of neural disentrainment to compensate for the effects of perturbation (P)  990 , driving the level of disentrainment of neural chaos back into the desired range, comprising the region defined by the union of target range  577  and target range  578 . 
     In  FIG. 57 , a time diagram of the EEG signal (E)  985 , unperturbed control input (Y′)  988  and perturbed control input (Y″)  989 , unperturbed control output (U′)  983  and perturbed control output (U″)  984 , unperturbed state (X′)  986  and perturbed state (X″)  987  in response to perturbation (P)  990 . This figure shows the effect of perturbation (P)  990  on the neurophysiology of patient  227  and signals processed by neurological control system  999 . 
     In addition to the values shown in  FIG. 56 ,  FIG. 57  further shows EEG signal  985 , including EEG signal  1  (E 1 )  985 - 1  and EEG signal  2  (E 2 )  985 - 2 , during which time span there are no EEG abnormalities  599 . Neurological control system  999  maintains perturbed control input  1  (Y 1 )  989 - 1  within the desired range, comprising the region defined by the union of target range  577  and target range  578 , preventing any neurological signs or symptoms and preventing any EEG abnormalities which may precede or be concurrent with such neurological signs or symptoms. 
     In  FIG. 58 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y)  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time in the normal unperturbed state. This diagram shows normal EEG signals, (E 1 )  985 - 1  and (E 2 )  985 - 2 , with no EEG abnormalities  599  nor neurological signs or symptoms  600  during this time span 
     In one preferred embodiment, control input  1  (Y 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . Additional states (X)  991  may be defined or used without departing from the present invention. In one embodiment, for the treatment of seizures, state  1  (X 1 )  991 - 1  represent the level of correlation of neural chaos between EEG signals (E 1  to E-N)  985 - 1  to  985 -N. 
     Control input  1  (Y 1 )  996 - 1  may occupy a range of values that are subdivided into specific ranges shown in  FIG. 58 . 
     Under normal baseline conditions, as shown for baseline nonperturbed nontreated period  592 , control input  1  (Y 1 )  996 - 1  is shown to vary within normal range  587 . This demonstrated variation of control input  1  (Y 1 )  996 - 1  occurs throughout the day under normal conditions and in the absence of any signs or symptoms of disease. This represents a normal range of disease state. In one preferred embodiment for the treatment of epilepsy, this represents the normal range of a metric of disentrainment of neural chaos and during which there are no signs and no symptoms of seizure. 
     Under normal conditions and during which time the present invention is in operation (“Closed-Loop Neuromodulator Control ON”), shown as nonperturbed treated period  593 , control input  1  (Y 1 )  996 - 1  is shown to vary within control range  586 . Control range  586  is a subset of normal range  587 . This demonstrates variation of control input  1  (Y 1 )  996 - 1  that occurs throughout the day under conditions in which the present invention is active (“Closed-Loop Neuromodulator Control ON”) and in the absence of any signs or symptoms of disease. This represents a normal range of disease state. In one preferred embodiment for the treatment of epilepsy, this represents the normal range of a metric of neural chaos during which there are no signs and no symptoms of seizure. During nonperturbed treated period  593 , the Closed-Loop Neuromodulator Control is ON and control output (U 1 )  997 - 1  is active. Due to the action of control output (U 1 )  997 - 1 , implemented as electrical current or voltage pulses, flow of pharmacological or chemical agent, emission of light, delivery of vibratory or ultrasound energy, production of electromagnetic energy, application of pressure or other neuromodulating energy form, control input  1  (Y 1 )  996 - 1  is maintained within control range  586 . 
     Under normal conditions and during which time the present invention is inactive (“Closed-Loop Neuromodulator Control OFF”), shown as nonperturbed nontreated period  594 , control input  1  (Y 1 )  996 - 1  is shown to vary within normal range  587 . This demonstrated variation of control input  1  (Y 1 )  996 - 1  occurs throughout the day under normal conditions and in the absence of any signs or symptoms of disease. This represents a normal range of disease state. In one preferred embodiment for the treatment of epilepsy, this represents the normal range of a metric of neural chaos during which there are no signs and no symptoms of seizure. There may be some aftereffects that persist following the deactivation of the present invention (Closed-Loop Neuromodulator Control OFF); these are anticipated in the present invention. The persistence of beneficial effects for some period following the use of the present invention allows the duty cycle of operation to be reduced, thereby minimizing tissue stimulation, drug or other agent delivery. 
     There are no EEG abnormalities  599  and no neurological signs or symptoms  600 , including seizures, during the time periods depicted in  FIG. 58 . As shown during nonperturbed nontreated period  594 , the action of control input  1  (Y 1 )  996 - 1  provides a continuous stabilizing influence on control input  1  (Y 1 )  996 - 1 , preventing it from deviating outside of control range  586 . At no point in time in  FIG. 58  does control input  1  (Y 1 )  996 - 1  indicate any adverse neurological conditions that would be reflective of a seizure, and aura, or associated event. Control input  1  (Y 1 )  996 - 1  does not enter borderline range  588 , borderline range  589 , critical range  590 , or critical range  591 . 
     In  FIG. 59 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time in the normal unperturbed state. This diagram shows normal EEG signals, (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, occupying interictal period  603 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , outside of normal range  587  and into borderline range  589  and critical range  591  without the occurrence of any EEG abnormalities  599  or signs or symptoms of neurological disease  600 . 
     In one preferred embodiment, control input  1  (Y 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . Additional states (X)  991 , including but not limited to the time derivative of state  1  (X 1 )  991 - 1 , the integral with respect to time of state  1  (X 1 )  991 - 1 , and other functions of state  1  (X 1 )  991 - 1 , other elements of state (X)  991  and functions thereof, other elements of control input (Y)  996  and functions thereof, and other values without departing from the present invention. 
     Control input  1  (Y 1 )  996 - 1  is shown to vary first within the normal range  587  and to have multiple excursions into borderline range  589  and critical range  591 . During this time, there are no episodes of abnormal EEG nor episodes of neurological signs or symptoms. Control input  1  (Y 1 )  996 - 1 , as well as corresponding elements of state (X)  991 , exhibit excursions outside of normal range  587 , and corresponding ranges likewise for elements of state (X)  991 , and return to normal range  587  without event. 
     In  FIG. 60 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time in the unperturbed state in which the present invention is not activated and a seizure spontaneously develops. This diagram shows the development of EEG abnormalities  599 , shown in (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, followed by the progression of the seizure to be manifest by neurological signs or symptoms  600 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , outside of normal range  587  and into borderline range  589  and critical range  591 , during which time EEG abnormalities  599  subsequently develop, followed by neurological signs or symptoms  600 . 
     In one preferred embodiment, control input  1  ( 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . Additional states (X)  991 , including but not limited to the time derivative of state  1  (X 1 )  991 - 1 , the integral with respect to time of state  1  (X 1 )  991 - 1 , and other functions of state  1  (X 1 )  991 - 1 , other elements of state (X)  991  and functions thereof, other elements of control input (Y)  996  and functions thereof, and other values without departing from the present invention. 
     Control input  1  (Y 1 )  996   1  is shown to vary first within the normal range  587  and to have multiple excursions into borderline range  589  and critical range  591 . During the first such excursion, which occurs in interictal period  595 , there are no episodes of EEG abnormalities  599  nor episodes of neurological signs or symptoms  600 . Sometime during the second such excursion into critical range  591 , EEG abnormalities  599  develop, as seen during sustained organized paroxysmal discharge period  596 . The EEG abnormalities  599  that appears during sustained organized paroxysmal discharge period  596  then progresses to involve a larger portion of the brain and is manifest as neurological signs or symptoms  600  which define the clinical seizure  597 . Following clinical seizure  597 , neurological signs or symptoms  600  cease and EEG abnormalities  599  cease. During and Following clinical seizure  597 , Control input  1  (Y 1 )  996 - 1  progressively moves back to normal range  587 . 
     In  FIG. 61 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time in the unperturbed state in which the present invention is activated and providing a continuous stabilizing influence on the nervous system, during controlled interictal period  604 . This diagram shows the persistence of normal EEG signals, shown in (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, occupying controlled interictal period  604 , accompanied by no neurological signs or symptoms  600 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , within the control range  586 . A single or multiplicity of additional states may be used without departing from the present invention. 
     In one preferred embodiment, control input  1  (Y 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . Additional states (X)  991 , including but not limited to the time derivative of state  1  (X 1 )  991 - 1 , the integral with respect to time of state  1  (X 1 )  991 - 1 , and other functions of state  1  (X 1 )  991 - 1 , other elements of state (X)  991  and functions thereof, other elements of control input (Y)  996  and functions thereof, and other values without departing from the present invention. 
     In one preferred embodiment for the treatment of epilepsy, control input  1  (Y 1 )  996 - 1  is calculated as a measure of the disentrainment of neural chaos. Other measures of neural chaos, functions of neural chaos, or other function of at least one of neural chaos or neural entropy may be used without departing from the present invention. Neural chaos may be measured using a Lyapunov exponent, Kolmogorov entropy, or other measure of chaos. These measures of neural chaos, disentrainment of neural chaos, entrainment of neural chaos, synchronization of neural chaos, and other functions of neural chaos may be calculated using any of the methods taught in the present invention or using other measures, including those in which information rate, neural signal correlations or cross-correlations, or other measure of entropy, chaos, or equivalent measure, or other function of neural or physiological signals are employed. Other functions include metrics of neural signal overall energy levels, energy levels within a single or multiplicity frequency bands or ratios thereof, changes in spike and wave frequencies, or other parameters. 
     Control output  1  (U 1 )  997 - 1  is used to generate Neuromodulatory signal (NMS)  998 , which is transmitted to at least one of the implementations neural modulator described herein or to other neuromodulator; these include but are not limited to neuromodulator array  561 , hippocampal modulator  534  and hippocampal modulator  535 , hippocampal modulator  536  and hippocampal modulator  357 , cortical modulator  530 , cortical modulator  531 , cortical modulator  532 , and cortical modulator  533 , olfactory nerve modulator  527 , trigeminal nerve modulator  528 , vagus nerve modulator  529 , sympathetic modulator  567 , crbitofrontal modulator  279 , prefrontal modulator  280 , precentral modulator  281 , postcentral modulator  282 , parietal modulator  283 , parietooccipital modulator  284 , occipital modulator  285 , cerebellar modulator  286 , neuromodulator  545 , neuromodulator  546 , neuromodulator  547 , neuromodulator  548 , neuromodulator  549 , neuromodulator  550 , neuromodulator  551 , neuromodulator  552 , neuromodulator  553 , neuromodulator  554 , neuromodulator array  561 , contralateral implementations of any of these, and other neuromodulators. Other variations of these neuromodulator designs or locations may be envisioned by one skilled in the art, and these are included in the present invention. Other anatomical locations or electrode designs or configurations may be used without departing form the present invention. 
     Control input  1  (Y 1 )  996 - 1  is shown to remain within control range  586 , while responding to the continuous stabilizing influence provided by control output  1  (U 1 )  997 - 1 . There are no EEG abnormalities  599  and no neurological signs or symptoms  600  during the action provided by the feedback driven controller embodied in the present invention. The present invention encompasses the use of any single or multiplicity of elements of control input Y  996 , representing any single or multiplicity of disease state. One preferred state used in the treatment of epilepsy is the use of at least one measure of disentrainment of neural chaos. These and other measures of neural activity, chaos, disentrainment of neural chaos, entrainment of neural chaos, synchronization of chaos are also used in the treatment of psychosis, depression, schizophrenia, mania, bipolar disorder, rage, anxiety, and other neurological and psychiatric conditions. 
     In  FIG. 62 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time in the unperturbed state in which the control output  1  (U 1 ) is initially OFF during uncontrolled interictal period  605  and is subsequently activated and begins providing a continuous stabilizing influence on the nervous system, during controlled interictal period  606 . 
     The transition from control output  1  (U 1 ) in the OFF state during uncontrolled interictal period  605  to the ON state during controlled interictal period  606  occurs at an arbitrary time and under normal conditions. In the diagram shown, control input  1  (Y 1 )  996 - 1  has deviated from arbitrarily defined control range  586  and is still within normal range  587 . As has been shown in  FIG. 59 , this does not represent a precursor to a seizure, and even if control input  1  (Y 1 )  996 - 1  were to enter borderline range  588 , borderline range  589 , critical range  590 , or critical range  591 , this condition would not represent a precursor to a seizure. At the point shown, control input  1  (Y 1 )  996 - 1  has deviated by an arbitrary amount from target value  576 , at which point control output  1  (U 1 )  997 - 1  is activated to apply closed-loop feedback control to stabilize control input  1  (Y 1 )  996 - 1  and bring control input  1  (Y 1 )  996 - 1  into control range  586 . 
     This diagram shows the persistence of normal EEG signals, shown in (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, occupying controlled interictal period  604 , accompanied by no neurological signs or symptoms  600 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , within the control range  586 . A single or multiplicity of additional states may be used without departing from the present invention. 
     In  FIG. 63 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time in the unperturbed state in which the control output  1  (U 1 ) is initially OFF during uncontrolled interictal period  607 , is subsequently activated and begins providing a continuous stabilizing influence on the nervous system during controlled interictal period  608 , and control output  1  (U 1 ) is subsequently OFF in uncontrolled interictal period  609 . 
     The transition from control output  1  (U 1 ) in the OFF state during uncontrolled interictal period  605  to the ON state during controlled interictal period  606  occurs at an arbitrary time and under normal conditions. In the diagram shown, control input  1  (Y 1 )  996 - 1  has deviated from arbitrarily defined control range  586  and is still within normal range  587 . As has been shown in  FIG. 59 , this does not represent a precursor to a seizure, and even if control input  1  (Y 1 )  996 - 1  were to enter borderline range  588 , borderline range  589 , critical range  590 , or critical range  591 , this condition would not represent a precursor to a seizure. At the point shown, control input  1  (Y 1 )  996 - 1  has deviated by an arbitrary amount from target value  576 , at which point control output  1  (U 1 )  997 - 1  is activated to apply closed-loop feedback control to stabilize control input  1  (Y 1 )  996 - 1  and bring control input  1  (Y 1 )  996 - 1  into control range  586 . Once control input  1  (Y 1 )  996 - 1  has been stabilized into an arbitrary range, such as control range  586  or other range, control output  1  (U 1 ) is turned OFF, as shown in uncontrolled interictal period  609 . The turning of control output  1  (U 1 ) OFF is performed to conserve battery power, minimize electrical current induced tissue damage, minimize pH changes at the tissue-electrode interface, to further minimize neural habituation, which is itself a benefit of closed-loop feedback driven neuromodulation, and other benefits. 
     This diagram shows the persistence of normal EEG signals, shown in (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, occupying controlled interictal period  604 , accompanied by no neurological signs or symptoms  600 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , within the control range  586 . A single or multiplicity of additional states may be used without departing from the present invention. 
     In  FIG. 64 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time before, during, and after the application of a perturbation (P)  990 , such as flashing lights. This diagram shows normal EEG signals, (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, occupying uncontrolled baseline period  610  preceding the application of perturbation (P)  990 , uncontrolled perturbation period  611  during the application of perturbation (P)  990 , and uncontrolled post-perturbation period  612  following the application of perturbation (P)  990 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , within normal range  587 , and without the occurrence of any EEG abnormalities  599  or neurological signs or symptoms  600 . Despite the application of perturbation (P)  990 , control input  1  (Y 1 )  996 - 1 , a measure of disease state, remains in normal range  587 ; and corresponding elements of state (X)  991  remain in their corresponding normal ranges. 
     In one preferred embodiment, control input  1  (Y 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . In a preferred embodiment for the treatment of seizures, control input  1  (Y 1 )  996 - 1  is a function of disentrainment of neural chaos, for which neural chaos is calculated using Lyapunov exponents, Kolmogorov entropy or other measure of chaos. Additional elements of state (X)  991 , including but not limited to the time derivative of state  1  (X 1 )  991 - 1 , the integral with respect to time of state  1  (X 1 )  991 - 1 , and other functions of state  1  (X 1 )  991 - 1 , other elements of state (X)  991  and functions thereof, other elements of control input (Y)  996  and functions thereof, and other values without departing from the present invention. 
     In  FIG. 65 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time before, during, and after the application of a perturbation (P)  990 , such as flashing lights. This diagram shows normal EEG signals, (E 1 )  985 - 1  and (E 2 )  985 - 2 , during this time span, occupying uncontrolled baseline period  610  preceding the application of perturbation (P)  990 , uncontrolled perturbation period  611  during the application of perturbation (P)  990 , and uncontrolled post-perturbation period  612  following the application of perturbation (P)  990 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , outside of normal range  587  and into borderline range  589  and critical range  591  without the occurrence of any EEG abnormalities  599  or neurological signs or symptoms  600 . 
     In one preferred embodiment, control input  1  (Y 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . In a preferred embodiment for the treatment of seizures, control input  1  (Y 1 )  996 - 1  is a function of disentrainment of neural chaos, for which neural chaos is calculated using Lyapunov exponents, Kolmogorov entropy or other measure of chaos. Additional elements of state (X)  991  may be used, including but not limited to the time derivative of state  1  (X 1 )  991 - 1 , the integral with respect to time of state  1  (X 1 )  991 - 1 , and other functions of state  1  (X 1 )  991 - 1 , other variables or constants and functions thereof, other elements of control input (Y)  996  and functions thereof, and other values without departing from the present invention. 
     Control input  1  (Y 1 )  996 - 1  is shown to vary first within the normal range  587  and to have multiple excursions into borderline range  589  and critical range  591 . During this time, there are no episodes of EEG abnormalities  599  nor episodes of neurological signs or symptoms  600 . Control input  1  (Y 1 )  996 - 1 , as well as corresponding elements of state (X)  991 , exhibit excursions outside of normal range  587 , and corresponding ranges likewise for elements of state (X)  991 , and return to normal range  587  without event. 
     In  FIG. 66 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y)  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time before, during, and after the application of a perturbation (P)  990 , such as flashing lights, during which time the present invention is not activated and a seizure subsequently develops. 
     This diagram shows EEG signals, (E 1 )  985 - 1  and (E 2 )  985 - 2 , which are initially normal during uncontrolled baseline period  613 , preceding the application of perturbation (P)  990 , and which remain normal during uncontrolled perturbation period  614  during the application of perturbation (P)  990 , and remain normal for some time thereafter during uncontrolled post-perturbation period  615  following the application of perturbation (P)  990 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , outside of normal range  587  and into borderline range  589  and critical range  591  without the occurrence of any EEG abnormalities  599  or neurological signs or symptoms  600  during uncontrolled baseline period  613 , uncontrolled perturbation period  614 , and uncontrolled post-perturbation period  615 . 
     During the second excursion of control input  1  (Y 1 )  996 - 1  into critical range  591 , EEG abnormalities  599  develop, shown in (E 1 )  985 - 1  and (E 2 )  985 - 2 , defining the beginning of sustained organized paroxysmal discharge period  616 , which is followed by the progression of EEG abnormalities  599  into clinical seizure  617 , which is manifest by neurological signs or symptoms  600 . 
     In one preferred embodiment, control input  1  (Y 1 )  996 - 1  calculated by signal processor  71  is representative of disease state, which is implemented as state  1  (X 1 )  991 - 1  in control circuit  72 . In a preferred embodiment for the treatment of seizures, control input  1  (Y 1 )  996 - 1  is a function of disentrainment of neural chaos, for which neural chaos is calculated using Lyapunov exponents, Kolmogorov entropy or other measure of chaos. Additional elements of state (X)  991  may be used, including but not limited to the time derivative of state  1  (X 1 )  991 - 1 , the integral with respect to time of state  1  (X 1 )  991 - 1 , and other functions of state  1  (X 1 )  991 - 1 , other variables or constants and functions thereof, other elements of control input (Y)  996  and functions thereof, and other values without departing from the present invention. 
     Control input  1  (Y 1 )  996 - 1  is shown to vary first within the normal range  587  and to have multiple excursions into borderline range  589  and critical range  591 . During the first such excursion, which occurs in uncontrolled post-perturbation period  615 , there are no episodes of EEG abnormalities  599  nor episodes of neurological signs or symptoms  600 . Sometime during the second such excursion into critical range  591 , EEG abnormalities  599  develop, as seen during sustained organized paroxysmal discharge period  596 . The EEG abnormalities  599  that appears during sustained organized paroxysmal discharge period  596  then progresses to involve a larger portion of the brain and is manifest as neurological signs or symptoms  600  which define the clinical seizure  597 . Following clinical seizure  597 , neurological signs or symptoms  600  cease and EEG abnormalities  599  cease; and this remains the case during uncontrolled interictal period  618 . During and Following clinical seizure  597 , Control input  1  (Y 1 )  996 - 1 , reflective of disease state, progressively moves back to normal range  587 . 
     In  FIG. 67 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time before, during, and after the application of a perturbation (P)  990 , such as flashing lights. The present invention is shown continuously activated, producing control output  1  (U 1 )  997 - 1  to provide a continuous stabilizing influence, and the potential for a neurological signs or symptoms  600  is prevented. Furthermore, the very potential for even a precursor, i.e. EEG abnormalities  599 , is prevented, because the nervous system is prevented from entering a state in which such neurological signs or symptoms  600  or their precursors may develop and progress. Said neurological signs or symptoms  600  include but art not limited to a seizure, status epilepticus, headache, manic episode, depressive episode, anxiety episode, panic attack, rage episode, psychotic episode. 
     In  FIG. 68 , a time diagram of the EEG tracings, (E 1 )  985 - 1  and (E 2 )  985 - 2 , control input  1  (Y 1 )  996 - 1 , state  1  (X 1 )  991 - 1 , and control output  1  (U 1 )  997 - 1  are shown versus time before, during, and after the application of a perturbation (P)  990 , such as flashing lights. The present invention is shown conditionally activated, when control input  1  (Y 1 )  996 - 1  moves outside of control range, to resume the production of control output  1  (U 1 )  997 - 1  to provide a continuous stabilizing influence and prevent the potential for a neurological signs or symptoms  600  or their precursors. There are no EEG abnormalities  599  or neurological signs or symptoms  600  present and no potential for them to occur at any point in this figure. 
     This diagram shows EEG signals, (E 1 )  985 - 1  and (E 2 )  985 - 2 , which remain normal during uncontrolled baseline period  622 , preceding the application of perturbation (P)  990 , and which remain normal during uncontrolled perturbation period  623  during which time perturbation (P)  990  is applied, and remain normal thereafter during uncontrolled post-perturbation period  624  and controlled post-perturbation period  625 . This figure demonstrates fluctuation of control input  1  (Y 1 )  996 - 1 , and correspondingly of state  1  (X 1 )  991 - 1 , outside of control range  586  without the occurrence of any EEG abnormalities  599  or neurological signs or symptoms  600 . 
     When control input  1  (Y 1 )  996 - 1  moves outside of control range  586  but remains within normal range  587 , then control output  1  (U 1 )  997 - 1  is delivered to maintain the nervous system in a stable state and thereby prevent even the possibility of undesirable neurological signs or symptoms  600  or their precursors, i.e. EEG abnormalities  599 , from developing. The precise time of initiation of delivery of control output  1  (U 1 )  997 - 1  is somewhat arbitrary, as the action is taken no t in response to any event but as a measure to prevent a neural state, as quantified by control input  1  (Y 1 )  996 - 1  and correspondingly of state  1  (X 1 )  991 - 1 , from exiting normal range  587  and progressing into a critical range  591  in which neurological signs or symptoms  600  may subsequently develop. 
     By this action of control output  1  (U 1 )  997 - 1 , the very potential for even a precursor, i.e. EEG abnormalities  599 , is prevented, because the nervous system is prevented from entering a state in which such neurological signs or symptoms  600  or their precursors may develop and progress. Said neurological signs or symptoms  600  include but art not limited to a seizure, status epilepticus, headache, manic episode, depressive episode, anxiety episode, panic attack, rage episode, psychotic episode. 
     SUMMARY 
     In one preferred embodiment, the control input  1  (Y 1 )  996 - 1  is calculated as a measure of disentrainment of neural chaos among regions of the nervous system. This representation of control input  1  (Y 1 )  996 - 1  then reflects degree of neural entrainment, which is inversely related to entrainment of neural chaos and synchronization of neural chaos. During normal interictal states, when no seizure or other neurological signs or symptoms  600  are present, the nervous system possesses a normal degree of neural entrainment, that is, the various neural regions exhibit some correlation in their level of chaos. A seizure occurs as a significant portion of neural tissue exhibits synchronization of activity. By minimizing neural entrainment and maximizing disentrainment, seizures and their precursors may be prevented. 
     This is fundamentally different from and a dramatic improvement over the system described by but not enabled by Fischell U.S. Pat. No. 6,016,449. Fischell describes a system that detects and terminates a seizure. The patent then claims but does not enable a system to detect and terminate a precursor to a seizure, by detecting EEG thresholds. Precursors are not defined and their detection no described nor enabled. In either of these two cases of terminating seizures or precursors to seizures, the aberrant neural condition has already occurred and its progression t seizure is imminent and inevitable without intervention. At this point, terminating the process, which has already begun, is difficult and may not be possible. In the Fischell designs, an event detection signal is generated upon detection of a seizure or its precursor, in either case of which, progression to clinical seizure has begun or is imminent, as manifest by EEG abnormalities  599 . 
     In contrast, the neurological control system  999  taught in the present invention controls fundamental neural states, thereby preventing the development of neural states in which a seizure is even possible. By maintaining neural disentrainment within the normal range, seizures do not occur or are extremely unlikely. Certain pharmacological agents may have this effect as well, by preventing the nervous system from being able to initiate the process that culminates in a seizure. The present invention can continuously monitor and maintain a desired level of therapy, controlling desired neural states to remain within stable regions and out of regions in which neurological signs and symptoms may develop. Through the use of feedback control, the present invention insures that the appropriate neural states, including but not limited to the level of disentrainment of neural chaos, which is inversely related to synchronization of neural chaos and entrainment of neural chaos, remains within the normal range, preventing the nervous system from entering a state, which itself is free of any abnormalities or signs or symptoms, in which it is even possible to develop a seizure or other neurological signs or symptoms  600 . 
     Not only is a seizure prevented, the potential for a seizure is prevented. Furthermore, the potential for a precursor to a seizure is prevented. The present invention controls the degree of disentrainment of neural chaos as measured in at least one region of the brain. This degree of disentrainment, the quantification of which is embodied in control input  1  (Y 1 )  996 - 1 , normally varies throughout the day. Certain external inputs, such as perturbations as well as medications, can alter this degree of disentrainment. If control input  1  (Y 1 )  996 - 1  is maintained in normal range  587 , the development of a precursors to a seizure, specifically EEG abnormalities  599  as manifest during sustained organized paroxysmal discharge period  596 , are prevented. Neural state, as quantified in control input  1  (Y 1 )  996 - 1 , varies overtime. Under certain conditions, i.e. when control input  1  (Y 1 )  996 - 1  is in critical range  591 , a seizure may possibly occur, but it is not predetermined to occur under this condition. 
     Maintaining control input  1  (Y 1 )  996 - 1  outside of critical range  591  and preferably in normal range  587  and more preferably in a subset of normal range  587  labeled control range  586  prevents even the precursor, i.e. EEG abnormalities  599 , of a neurological signs or symptoms  600  from developing. The present invention prevents the nervous system from entering a state in which it is possible to have a seizure. The present invention monitors the degree of response to therapy and modulates therapy to maintain effect within the desired therapeutic range, i.e. control input  1  (Y 1 )  996 - 1  within control range  586 . Under such conditions, no neurological signs or symptoms  600  occur. No precursors, i.e. EEG abnormalities  599 , occur, since the present invention applies a continuous stabilizing influence to modulate the nervous system and thereby maintain it in a stable state.