Abstract:
New human bio-sensors in a virtual reality headset can be worn comfortably for extended periods of time, detecting brain, nerve, ocular, muscle or other bio-signals. Recording raw data, amplification, digital conversion, filtering, support subsequent manipulation by iteratively creating multiple interpretation maps. Data processing and transmission are reduced so that control of a remote device may occur in real time, based on an event in the body of a wearer. A signal interpretation engine iteratively creates numerous interpretation maps, the best correlated one being used exclusively, thus minimizing false positives and requiring minimal data to distinguish an event from any other non-presence of that event. After training and verification, operation in real time processes live, continuous data to control a remote device based on the events detected by the sensor set in a headset, arm bands, leg bands, gloves, or boots.

Description:
RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/307,578, filed Mar. 14, 2016, which is hereby incorporated herein by reference with its Appendices attached thereto and filed therewith. Also, this patent relies on information from U.S. Pat. No. 6,546,378, issued Apr. 8, 2003, entitled SIGNAL INTERPRETATION ENGINE, as well as U.S. Pat. No. 6,988,056, issued Jan. 17, 2006 entitled, SIGNAL INTERPRETATION ENGINE, both of which are hereby incorporated herein by reference in their entirety. 
     
    
     BACKGROUND 
       [0002]    1. Field of the Invention 
         [0003]    This invention relates to computer systems and, more particularly, to novel systems and methods for remote control of devices, based on biological sensors as input devices detecting muscular and brain activity of a wearer, and processing “big data” to do so in real time. 
         [0004]    2. Background Art 
         [0005]    The term “big data” may not be well defined, but acknowledges an ability to collect much more data than can be readily processed. Data collected during any period of “real time” may still require months of programming, mining, and study to determine its meaning. When data is noisy, having a comparatively small signal-to-noise ratio (SNR), the problem is exacerbated. Modern gaming systems can calculate, render, download, and display images in extensive detail. Programming to do so can be done over a period of months or years. Not so, detecting and processing user actions. 
         [0006]    Virtual reality is a term that is used in many contexts. It may not have a universal definition. Nevertheless, it may typically be thought of as an immersive sensory experience. An individual can look at a sculpture or work of art. An individual may watch a movie (the original motion picture), and may hear sounds directly or as reproduced through speakers. 
         [0007]    Permitting an individual to control what is seen is an objective of gaming systems. By moving a controller wand, controller handle, buttons, and the like, a user may “virtually fly” an aircraft, or play golf, tennis, or music. Most recently, gaming software is attempting to improve the user experience in the details. One approach is to provide a user with a screen (monitor) in a comparatively smaller format such as in goggles or a headset or the like. To that end, headsets have been subject to certain experiments to embed cameras observing the wearer. The cameras have the objective of taking images of the face or portions of the face of a user. 
         [0008]    The difficulty lies in trying to process those images, and transmit the information in them to a remote location of another gamer. Each player needs to receive those images and thus be able to see a representation of the face, body, limbs, etc. of a fellow gamer, typically an opponent. However, such image processing requires massive computational resources. Moreover, computation of resources does not respond instantly. It requires time to process information. 
         [0009]    Thus, it would be an advance in the art to provide less processing or a lesser processing requirement for information passed over a network to a remote location, such as a computer console of a fellow gamer. It would be an advance in the art to be able to find new electrical and mechanical mechanisms for collecting data, new methods for processing data, and new methods for consolidating and summarizing data in order to reduce memory requirements in computing systems and storage devices, as well as improving the speed of processing in order to provide literal “real-time” processing and transmission of user activities to a remote user or fellow user in a gaming environment. 
       BRIEF SUMMARY OF THE INVENTION 
       [0010]    In view of the foregoing, in accordance with the invention as embodied and broadly described herein, a method and apparatus are disclosed in one embodiment of the present invention as including a brainwave engine operably connected to a virtual reality headset or brainwave virtual reality headset (BVRH). In certain embodiments, the brainwave engine may incorporate all or part of a signal interpretation engine as described in detail in the references incorporated herein above by reference. 
         [0011]    Some valuable features or functionalities for a BVRH may include a system for labeling events, collecting electronic data such as encephalo-based (electroencephalographic; brain based or neuro based) data, as well as myo-based (electromyographic; muscular) data or ocular-based (electroocular; eye dipole detection). In a system and method in accordance with the invention, such data are collected through sensors, and may be allowed to be entirely mixed. By adapting a signal interpretation engine according to the invention, iterating to provide signal interpretation maps, and correlating to find the best such map, processing may be greatly speeded up. Processing and control may be done in real time. Also, separation of myo-, encephalo-, and ocular-based data may be done by processing, rather than by limiting sensors. 
         [0012]    In one presently contemplated embodiment of a system in accordance with the invention, biological sensors may collect data, such as voltages between a reference and a sensor, and between a neutral electrode and a sensor in order to provide raw data. This data may then be manipulated, by one of many mathematical processes in order to determine, and initially simply process, the wave form by many, hundreds or thousands of mathematical processing manipulations. 
         [0013]    The attempt is to observe and analyze a wave form by modifying it in a way that permits the detection of portions of the signal that correspond to an event coincident with the data. Since the signal-to-noise ratio is so small, the noise tends to dominate. By using manipulations and processes in accordance with the signal interpretation engine, one my then analyze correlations between events and processed signals. Thus, interpretation maps may be created, and tested for their best correlation against events. 
         [0014]    Thus, a BVRH may take data from a subject (user) and process that data in order to provide an interpretation map, and select the best correlating interpretation map for sensor data. Thereafter, the system may use the interpretation map, which involves rapid pass through analysis of wave forms taken live during operation of a game or other event, and quickly assess them, categorize them, and output data that will control an avatar or other device according to the biological activity of the wearer of the BVRH. 
         [0015]    Thus, the immersive virtual reality experience may be augmented with an augmented reality to form a blended reality device in which some signals are based on actual events, others on virtual events, and yet others are responses to either type of event by a user. All of which may be shared between internetworked devices. 
         [0016]    In one currently contemplated embodiment, a headset may include a head mounted display (HMD) such as goggles, helmet, lighter headgear, such as straps, and the like. A user may wear a headset containing a set of sensors. Sensors may be of a variety of devices, including capacitive, temperature, electromagnetic, or the like. 
         [0017]    In one embodiment, sensors may include electrodes that are monitored for voltages with respect to some source, such as a reference voltage, ground, or both. In one currently contemplated embodiment, biological events may be monitored through sensors. 
         [0018]    For example, biological events may include a smile, a smirk, a teeth clench, a grimace, a blink, a wink, an eyebrow raise, an eyebrow lowering, any of these events may occur with respect to a single eye or both eyes. In general, the biological event of interest may be any activity in the face or head of a user that is detectable by a sensor. Thus, biological events may be simple, such as raising an eyebrow or both eyebrows. Similarly, events may be complex, such as a teeth clench which involves many muscles in the face, and may involve furrowing of the brows simultaneously, narrowing of the eyes, and so forth. Some events may be effectively binary, such as whether an eye is open or closed. On the other hand, a biological event may be partial, proportional, or multi-state in nature. 
         [0019]    For example, a teeth clench, a grimace, or the like will typically involve the entire face. It may involve the eyes being partially closed, the brows being depressed, the mouth converted into a frown, or the clenching of teeth or both, and so forth. Thus, multiple states of multiple portions of the face of the user may effectively result in a multi-state event. This is particularly so when the specific aspect of the face, such as an eyebrow, the corner of a mouth, a chin, or the like may be involved or not involved in a particular expression. 
         [0020]    Accordingly, biological events may be isolated. Especially during learning, it may be beneficial to actually record information in which an event is being recorded in isolation. In particular, if one particular aspect of a face is being activated by a user, such as a smirk, which will typically involve only one side of the mouth being elevated in a smile, then events may be intentionally isolated by a user in order to provide a more pure or isolated signal. Nevertheless, in actual practice, multi-state events may occur, which then must be interpreted, with various aspects of a face being identified and classified into a state. 
         [0021]    For example, a smile may be a readily smile, a pleasant smile of enjoyment, or a diabolical smile. These may be identified as different types of events. By having isolated events recorded, the system in accordance with the invention may be able to put together the state of multiple portions of the face or multiple signal sources. Meanwhile, in some events, a particular part of the body may have multiple states of existence itself. Again, a brow raise, a brow lower, or the motion of the brow into any location therebetween may constitute an event or portion of the event to be recorded, and identified as such. Thus, compound events may be events in which multiple aspects of the head or face of a user are involved. 
         [0022]    Similarly, biological events may involve activation of muscle cells in the body. Sensors may be secured to arms, elbows, portions of an upper arm and forearm, locations both inboard and outboard from an elbow. Likewise, hands may be instrumented. Gloves with sensors may be used. Feet, knees, and the like may record running in place, muscle stretch, muscle tensing, and so forth. In general, biological sensors may record biological events in any portion of the body, based on activity or nerves, or activity of muscles. 
         [0023]    In one currently contemplated embodiment, biological events may be recorded in especially conformal sensors. For example, electroencephalograms are used in medicine to detect whether certain portions of the brain are active. Similarly, electrocardiograms may record signals sent by the heart. In general, electromyograms may distinguish or identify muscular activity. In a system in accordance with the invention, one may think of these all as sources of signals to be sensed and collected together. They may detect electromagnetic signals, voltages, current, strain, or the like. Sensors may be formed in various ways. However, a system and method in accordance with the invention, sensors may be formed of a flexible, electrically conductive fabric material. This fabric may be backed with a solid foil conductor that is comparatively thin enough not to distort the fabric yet hold a connector. Prior art systems may require probes that impose directly into the brain or into the skin, plate-like or pointed metal objects that press themselves into the skin or depress the skin uncomfortably. Stiff metal plates or points that may be pressed or glued to the skin in an uncomfortable manner are to be avoided herein. 
         [0024]    In an apparatus and method in accordance with the invention, a soft flexible, yet electrically conducting, fabric may be backed by a better conductor, such as a thin foil that makes electrical contact along a considerable extent of the area of the softer fabric material. These conductor foils may then be secured at some location away from the skin of a user, to connectors that can then receive wires for carrying signals. Signals, the contact surface of the skin of a user through the fabric, conducting foils, and connectors may be transmitted by wires to amplifiers. Those amplifiers may then convert signals through analog-to-digital converters (A-DC). Digital signals, now representing the voltage, between sensors and a reference, being reported into a computing system to be operated on as raw data. Processing may include registration of signals in order to establish certain locations within an event that correspond to certain locations within the wave form that is the signal. 
         [0025]    For example, typically, during a training process, a trigger, signal, button, or the like may be actuated in order to identify an event. Typically, during training, an event is typically actuated, known, and recorded. Accordingly, sensors and their signals may be recorded with time stamps, time signals, clock identification, or the like corresponding to an event. 
         [0026]    Thus, a record may include the identification of a time stamp at which time a trigger is activated. Following the trigger activation, then the biological event occurs, causing signals to be detected by sensors, passed on through the process. Accordingly, time stamps on the event record and on sensor data may thus be managed, in order to provide some identification of an event and what that event is, along with the beginning and ending times of such an event, of which match the sensor data. 
         [0027]    In this way, sensor data may be absolutely associated, corresponded, and so forth to an event. The event, of course, is one of the biological events discussed hereinabove. 
         [0028]    A computing system receiving the data in digital form and the event record identifying the event, its start time and end time, may then process the information to register the event in time with the sensor data. 
         [0029]    For example, in one embodiment, a user, training a system in accordance with the invention may upon a triggering signal, make a smile. That smile may be held for some amount of time. It may then be relaxed. By providing a start time closely following a trigger signal, and by providing a hold time that clearly will occupy the central portion of a time region, then registration of the data may be made between the beginning and end times of the event record and corresponding sensor data, knowing that the non-event condition exists followed by a transition into the event condition, followed by a transition out of the event condition, followed by “dead space” in which the event does not exist in the data. Thus, registration of the data may permit comparative to absolute certainty as to what the state of an event is that corresponds to certain portions of the signal wave form. 
         [0030]    Thereafter, in a system and method in accordance with the invention, the classification process may involve a comparison between various events after classification. For example, in certain embodiments, it is discussed in detail in the references incorporated hereinabove by reference, in a particular feature expansion may be applied to the data corresponding to an event. Then that feature expansion may be correlated with the event. This may be done hundreds of times. 
         [0031]    Eventually, the correlations of an event to the processed expansion or feature expansion may then result in various accuracy of the correlations with the event. Eventually, it has been found best to select a particular interpretation map that provides the best correlations found. Thereafter, raw data may be received in a verification mode or operational mode, which may be processed in real time, using that best interpretation map. It has been found best not to use multiple interpretation maps, but rather than rely on a single, best correlated interpretation map in a system in accordance with the invention. 
         [0032]    In operational mode, the interpretation map may then be used as events are tracked. A user may use a headset, and let events occur naturally throughout a gaming or other experience. Events are accordingly picked up by sensors, amplified, converted to digital signals, and sent to a computer for processing. A computer then does the classification process on raw, unknown data, and thereby determining what events the interpretation map assigns to the incoming data. 
         [0033]    The classification process then provides outputs to go over a network to operate a remote device. That remote device may be an avatar visualization on a screen, a character in a scene on a monitor of a game, or even a controller for a device, such as an electromechanical device. Likewise, any other electronically controllable, remote device may be operable. It has been found that actual facial expressions may be used to control a device between states, or control movement of an object in multiple directions based on actuation by a user making facial expressions. 
         [0034]    In other embodiments, sensors in accordance with the invention may be attached to a sleeve on an arm of a user, on the leg of a user, or the like. In currently contemplated embodiments, a sleeve containing sensors may fit on an upper arm, elbow, and forearm of a user. Similarly, such a sleeve may be fitted to an arm, foot, hand, elbow, knee, or other bodily member of a user. Thus, data reflecting movement of a subject may be processed and sent to control an image represented that user. Thus, in a virtual reality headset screen (monitor) one individual may view several other individuals, mutually gaming, as each is represented by an avatar or person constructed by the computer on a screen, and actually performing the motions of the subject gamer in each case. This can operate in real time, since the processing is sufficiently fast with an interpretation map that has been made in advance, and a machine has been “trained” according to knowing events or actions on the part of a subject. 
         [0035]    It has been found that in some circumstances, particularly because the sensors may detect both myographic as well as encephalographic data, that high impedance amplifiers seem to provide important function. Moreover, the use of filters may occur in a processing center, which may be programmed into a remote computer, or as in various prototype developments can be manufactured and included in the headset without adding substantial additional weight or volume to the requirements of the headset. Signals may be communicated by wire or wireless communication systems and still provide rapid, accurate, real-time interpretation of biological activity by processing of the biological signals corresponding to biological activities. 
         [0036]    In one currently contemplated embodiment of a BVRH, multiple, comfortable conductive fabric sensors may be held against the face or other body locations of a user. These may be secured in a dry contact method by polymeric foam pads that gently urge the soft fabric against the skin of a user. Thus, cushioning is provided. Moreover, a substantially constant force in contact may be maintained in spite of movement of the bodily parts of a subject. This may be especially valuable for the use over extended periods of time, where conventional sensors, electrodes, and the like are uncomfortable, and interfere. 
         [0037]    In certain embodiments of a system in accordance with the invention, a smartphone, may be referred to as a “screen or monitor” may be inserted into the front of a headset, and used as a display. Moreover, multiple views (images) or perspectives may be presented on different halves of a smartphone, providing a stereoscopic image. Images may be transmitted through ocular pieces such as suitable optics comprising lenses effective to render the stereoscopic presentation of the smartphone focused to the eyes of a user wearing a headset. 
         [0038]    On the other hand, displays may be independent of smartphones, and completely self-contained. However, a large contingent of youthful users rely on applications on smartphones for gaming. The cost-effective headset may include simple optics, in a goggle-like headset, held onto the face with headgear. A lateral strap may pass around the circumference of the head, a vertical strap proceeding from the top of the headset to the rear of the head to stabilize with the lateral strap. Thus, the lateral strap may be thought of as occupying the location of the crown of a hat or the hat band of the hat would occupy, whereas the vertical strap proceeds from the front of the head to the back of the head to stabilize the headset. 
         [0039]    In certain embodiments, sensors may be electromyographic, electroencephalographic, or EOG type sensors. Likewise, strain sensors (stretch sensors, bending sensors) may be used. These are typically based upon resistance or electrical conductivity due to matrices or arrays of material such as rubber or elastomeric materials containing conductive particles, such as carbon, and so forth. 
         [0040]    Certain other biological sensors, such as skin microstructure sensors may also be used to detect stresses, strain (engineering terms used herein as engineering terms, stress reflecting force per unit area, and strain reflecting length of extension per unit length. Herein whenever sensors are discussed, or brainwave sensors, any type of biological activity detection sensor may be used. 
         [0041]    Thus, in summary, multiple sensors in a BVRH may detect, report or transmit, and record for measurement signals generated by electrical activity, electromagnetic activity, or both of the human head, brain, face, nerves, muscles, in any bodily member, such as arms, legs, and so forth. These biological signals may be mixed including electroencephalography (EEG) from the brain, electromyography (EMG) of the muscles of the face, head, neck, eyes, jaw, tongue, body, extremities, arms, legs, feet, hands, and so forth. Similarly, electrooculography (EOG), movement of human eyes, can also be detected. Any electrical activity or electromagnetic activity (sensible by induction coils, or the Lorenzo effect) may provide a signal useful to detect and report activation and activity of any human bodily member. 
         [0042]    These multiple-wave form signals may be recorded simultaneously through sensors that detect through the soft, flexible, conductive fabric sensors. These may touch and press gently onto and into the skin, the degree of comfort that it may be maintained for extended periods, such as hours. Signals may be amplified continuously, digitized at a high rate, typically sufficient to detect accurately in the range of 256 Hertz or higher. As a practical matter, it has been found that encephalography requires a comparatively lower frequency zero to less than 50 Hertz, while myography typically requires or results in a comparatively higher frequency signal in the range of from about 50 to about 100, 90 being typical. Thus, radio transmission, such as Bluetooth and the like, Wi-Fi transmissions, near-field, or other wireless or wired data transmission through USB, HTMI, Ethernet, and the like may operate as connections to the brainwave engine hosted on a computer responsible to process, record, and interpret real-time data. Similarly, during learning, more complex signals requiring more processing time may be handled offline, following recording of events and their data. 
         [0043]    In one embodiment, an apparatus may comprise a set of sensors, each sensor thereof comprising a fabric selected to be electrically conductive and to have a hardness approximating that of flesh of a subject. It may include a set of leads, operably connected to the sensors away from the subject, a signal processor operably connected to the leads to detect electrical signals originating at the set of sensors and convert those electrical signals to input signals readable by a computer, and a first computer system. That computer system may be operably connected to receive from the signal processor the input signals and programmed to iteratively create a plurality of interpretation maps corresponding the input signals with events representing activities of the subject. It may be programmed to minimize data processing by selecting a single interpretation map and determining events corresponding to the electrical signals based on the single interpretation map. It may also be programmed to send to a second computer system remote from the first computer system a control signal instructing the second computer system to perform an action, based on the events represented by the input signals. 
         [0044]    The second computer system may include display, as may the first. The action may be a servo control or actuation of a machine or hardware. It may control re-creating an image of the events on the display of a remote second computer. 
         [0045]    Sensors are applied to contact the face of a subject in a location selected to include at least one of above, beside, and below the eyes of the subject, the forehead below a hairline, between the eyes and the ears, and the cheeks proximate the nose and mouth of a subject. The second computer may be or include a controller of a device based on the events detected by the sensors. 
         [0046]    An initial signal processor may have an amplifier corresponding to each of the leads, a converter from an analog format to a digital format readable by the first computer system. 
         [0047]    An appliance fitted to be worn by a subject on a bodily member of the subject may provide the set of sensors secured to the appliance to be in contact with skin of the subject. The sensors may be in contact exclusively by virtue of pressure applied to the skin by the appliance. The appliance may be selected from headgear, clothing, a sleeve wrapping around the bodily member, a glove, a sock, a boot, a band or the like, to apply sensors to bare skin on the face or limbs of a subject. Pressure may be applied by the appliance completely encircling a perimeter of a bodily member, with elastomeric resilience providing a comfortable force to apply pressure. 
         [0048]    One appliance comprises a mask contacting a face of a user and comprising a display portion and a sensor portion, the mask including a pressurizing material between the display portion and the sensor portion to apply pressure to the sensors against the skin. 
         [0049]    A first computer is programmed with a signal interpretation engine, executable to create a signal interpretation map providing a manipulation of the signals effective to identify the event, based on the manipulation, an iteration algorithm to execute the signal interpretation engine repeatedly to create a plurality of signal interpretation maps, and a correlation executable effective to determine a best signal interpretation map of the plurality of signal interpretation maps. 
         [0050]    The first computer is programmed to receive operational data from the set of sensors in real time and process the operational data by using the best signal interpretation map to identify the events occurring at the first set of sensors. It may then send to the second computer instructions controlling the remote device based on the events occurring at the first set of sensors. 
         [0051]    In one embodiment, a set of sensors, each sensor thereof comprising a fabric having a hardness less than that of skin of a human. The sensors may be further characterized by an electrical conductivity sufficiently high to conduct an electrical signal therethrough. Leads connecting to sensors corresponding, respectively, thereto, conduct electrical signals from the corresponding sensors. An initial signal processing system operably connected to the leads has at least one of an amplifier, an analog-to-digital converter, and a filter, and may have an amplifier and A-D converter for each lead. 
         [0052]    A first computer system operably connected to the initial signal processing system may execute any or all of a learning executable, a verification executable, and an operational executable. The learning, verification, and operational executables each comprise executable instructions effective to identify, and distinguish from one another, multiple events, each event of which corresponds to a unique set of values, based on the computer signals received by the computer system and corresponding directly with the electrical signals originating from the set of sensors. 
         [0053]    The first computer system should be programmed to iteratively create a plurality of interpretation maps corresponding to the input signals and the events representing activities of the subject. It may minimize data processing by selecting a single interpretation map and determining the events corresponding to the electrical signals based on the single interpretation map. It is programmed to send to a second computer system remote from the first computer system a control signal instructing the second computer system to perform an action, based on the events represented by the input signals. 
         [0054]    The first and second computer systems may each comprise a display. The second display by re-create an image representing the events detected by sensors corresponding to or attached on the first display. The second computer may be or control a device in a manner based on the events. A signal processor with an amplifier corresponding to each of the leads, and a converter converting each of the electrical signals from an analog format to a digital format, renders the signals readable by the first computer system. 
         [0055]    An apparatus may include an appliance fitted to be worn by a subject on a bodily member of the subject, the set of sensors being secured to the appliance to be in contact with skin of the subject exclusively by virtue of pressure applied to the skin by the appliance. The appliance may be selected from headgear, a head-mounted display, a head-mounted audio-visual playback device, clothing, a sleeve wrapping around the bodily member, a glove, a sock, a boot, a mask, and a band that completely encircles a perimeter of the bodily member, a harness combining an array of electrical sensors and motion sensors, a harness containing sensors and stimulators applying electrical stimulation, the appliance including a pressurizing material to apply pressure to the sensors against the skin of the subject. 
         [0056]    When the first computer is programmed with a signal interpretation engine, executable to create a signal interpretation map providing a manipulation of the signals effective to identify the event, events are distinguished based on the manipulations described in the references incorporated hereinabove by reference. However, this is done iteratively, and a best correlated interpretation map is selected from the numerous interpretation maps created. 
         [0057]    Moreover, it has been found very useful to define as many “events” and states as possible. Then during learning, each event can be defined as the existence of a “state A” when it occurs. It should be contrasted by the signal interpretation engine against every other “non-state-A” events. This helps eliminate false positives, because many muscles, nerves, neurons, etc. may be affected by numerous events. It has proven very valuable to process data from all “state A” and compare them with all “not state A” data to find the best interpretation map. 
         [0058]    The first computer is programmed with an iteration algorithm to execute the signal interpretation engine repeatedly to create a plurality of signal interpretation maps. It then applies a correlation executable effective to determine a best signal interpretation map of the plurality of signal interpretation maps. This may be repeated with data having a known event to verify its accuracy. Then the computer may receive operational data from the set of sensors in real time, and process it reliably, by using the best correlated interpretation may for every event. It is also programmed to send to the second computer instructions controlling the remote device based on the events occurring at the first set of sensors. 
         [0059]    One embodiment of a method may include instrumenting a mammal with sensors providing electrical signals reflecting biological activity in the mammal. The sensors are selected to detect at least one of muscular activity, brain activity, neural activity, and dipole movement of a biological electrical dipole in the mammal such as eye movement, tongue movement, muscle contractions/extensions, or the like. A first data signal, comprising a first digital signal readable by a computer, is obtained by operating on the raw electrical signals by at least one of amplifying, converting from analog to digital, and filtering. Once sent to a computer, the signals are used by the computer (iteratively executing a signal interpretation engine) to create a plurality of signal interpretation maps (interpretation maps) by the computer iterating through a feature expansion process operating on the digital signal. The computer then selects the interpretation map having the best correlation for determine “state A” for an event, against all other conditions of “not state A” or “not A.” 
         [0060]    Verification may be accomplished by testing each best interpretation map selected (from the plurality of signal interpretation maps) by using each map to classify a new digital signal independent from the first digital signal. The event condition and data are both known, and can be compared with the analysis by the interpretation map to verify that it is good enough to be the “best interpretation map,’ based on the greatest accuracy in correctly labeling the events. 
         [0061]    Filtering may be selected from high pass filtering, low pass filtering, notch frequency filtering, band pass filtering, or the like. Filtering may be selected to isolate from one another at least two of muscular activity, brain activity, neural activity and biological electrical dipole activity by frequency distribution. The signals comprise a first inner signal having particular correspondence to a first event constituting a biological event of the mammal, the first inner signal being characterized by a frequency in the range of from about 1 to about 200 Hertz. Muscular signals tend to range from about 30 Hertz to about 100 Hertz, and typically 50-90 Hertz. Brain signals tend to run from about 1 to about 50 Hertz, and typically 3-30 Hertz. 
         [0062]    Isolating the inner signal from the noisy signals is one reason for creating a signal interpretation map. This is done by by processing the first inner signal by feature expansion processing as described in detail in the references incorporated herein by reference. Selecting a signal interpretation map best correlating the first inner signal to the event enables receiving a second inner signal and classifying that second inner signal precisely. That second inner signal is manipulated according to the best interpretation map. Multiple interpretation maps are not used because they increase processing and do not provide a better outcome, identifying an occurrence of the event based on the classifying of the second inner signal. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0063]    The foregoing features of the present invention will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only typical embodiments of the invention and are, therefore, not to be considered limiting of its scope, the invention will be described with additional specificity and detail through use of the accompanying drawings in which: 
           [0064]      FIG. 1  is a front perspective view of one embodiment of the system in accordance with the invention; 
           [0065]      FIG. 2  is a rear perspective view thereof; 
           [0066]      FIG. 3  is a side view thereof; 
           [0067]      FIG. 4  is a process diagram illustrating the process of equipping a headset with sensors and electrodes effective to comfortably track and record muscular and brain activity of a user wearing a headset in accordance with the invention; 
           [0068]      FIG. 5  is a schematic block diagram of a generalized computer for use in a headset electronics module, a computer associated therewith, or both, which may be a network enabled in certain contemplated embodiments; 
           [0069]      FIG. 6  is a schematic block diagram of a process for recording biological events and using them in real time interpretation of events and output to a remote device controlled thereby; 
           [0070]      FIG. 7  is a schematic block diagram summarizing the learning process by which to create an interpretation for use in a system and method in accordance with the invention; 
           [0071]      FIG. 8  is a schematic block diagram of the process of interpretation map generation in summary; 
           [0072]      FIG. 9  is a screenshot of a control panel for operating a system in accordance with the invention; 
           [0073]      FIG. 10  is a screenshot illustrating various event identifiers equaling complex or combined event identifiers; 
           [0074]      FIG. 11  is a chart illustrating various experimental data in actual operation of the system in accordance with the invention tracking activities of a user and processing the biological waves therefrom to control a device presenting an avatar replicating the activities of the human subject in the system; 
           [0075]      FIG. 12  is an image of a user instrumented to detect motions of multiple bodily members as well as brain activity and nerve activity; 
           [0076]      FIG. 13  is a screenshot of experimental data from an event resolution imaging (ERI); 
           [0077]      FIG. 14  is a screenshot of experimental data from a visual study example; 
           [0078]      FIG. 15  is a screenshot of experimental data from a touch study example; and 
           [0079]      FIG. 16  is a screenshot of experimental data from a cognitive study example. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0080]    It will be readily understood that the components of the present invention, as generally described and illustrated in the drawings herein, could be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of the embodiments of the system and method of the present invention, as represented in the drawings, is not intended to limit the scope of the invention, as claimed, but is merely representative of various embodiments of the invention. The illustrated embodiments of the invention will be best understood by reference to the drawings, wherein like parts are designated by like numerals throughout. 
         [0081]    Referring to  FIGS. 1 through 4 , while continuing to refer generally to  FIGS. 1 through 11 , a system  10  in accordance with the invention may rely on a human subject  12 . A subject  12  is fitted with sensors  14  that may be non-contact electromagnetic, or otherwise. In one embodiment, the sensors  14  may be electrodes  14  that may be detected by voltage, current, or both. Typically, sensors  14  may be distributed about a ground  16 . In some embodiments, a sensor  14  may be a reference sensor  14   a.  Meanwhile, other sensors  14   b  may be detected with respect with either a reference sensor  14   a,  a ground sensor  14   c,  or both. Meanwhile, ground  16  represents a location that is grounded and may ground the sensor  14   c  at ground voltage. 
         [0082]    In the illustrated embodiment, the suite of sensors  14  is arrayed within a headset  18 . In the illustrated embodiment, a conductive fabric  20  is used to form each sensor  14 . The conductive fabric is selected to be soft, flexible, highly conductive, and conformal to the skin of a user. In certain embodiments, the fabric  20  may be cut into strips of a selected size. In one embodiment, strips have a width of approximately half an inch to an inch wide. However, in other embodiments currently contemplated and implemented in prototypes, fabric  20  may be formed in much smaller strips, or individual pieces. 
         [0083]    As a matter of user comfort, the fabric  20  may be formed such that the fabric  20  only connects to securements  21  such as glues  21 , fasteners  21 , or the like at locations remote or not close to the face on a subject  12 . For example, a conductor  22  or foil  22  may be applied as a lug  22  or contact area  22  to a strip of fabric  20  opposite to the face of a subject  12 . 
         [0084]    For example, a pad  23  may be formed of an elastomeric foam material. In one contemplated embodiment, the pad  23  is formed of an elastomeric foam such as a synthetic elastomer that is an open-cell type in order to be easily deformed or deflected by the face of a user  12  in order to maintain comfort. Various stiffness&#39;s of material may be used to form the pad  23 , in order to comfortably urge the fabric conductors  20  against the skin of a subject  12  to maintain electrical conductivity by the fabric  20 . Meanwhile, the fabric  20  may contact or connect directly to connectors  24 . In other embodiments, a conducting foil  22  may form a lug  22  or connection area  22 . 
         [0085]    Ultimately, leads  26  may be connected. For example, the lead  26   a  may connect a reference sensor  14   a.  A set or array of leads  26   b  may connect to the various other sensors  14   b.  Similarly, the lead  26   c  may connect to a round sensor  14   c,  and then to the ground  16 , literally. That is, lead  26   c  may connect between the ground sensor  14   c  and the ground  16 . On the ground  16 , through the ground sensor  14   c  and the lead  26   c  may be used as a ground voltage of zero by a data logger accumulating data in the system  10 . 
         [0086]    An amplifier  32  receiving signals from the sensors  14  may be included with an analog-to-digital converter  34  along with a processor  36  in an electronics module  38 . In some embodiments, the processor  36  may be an entire computer  40  capable of conducting learning, interpretation map creation, and operation of the system  10 . In other embodiments, minimal processing  36  may be onboard the electronics module  38 , with a maturity of heavy-duty processing being completed by the computing system  40 . 
         [0087]    Referring to  FIG. 4 , while continuing to refer generally to  FIGS. 1 through 11 , the system  10  may be manufactured by placing fabric strips  20  on a front side (away from a face of a user  12 ), and securing fabric strips  20  thereto by any suitable connection, such as a glue, adhesive, or the like. This securement material  21  need not be conductive. 
         [0088]    On the other hand, the strips of fabric  20  may be wrapped around the first layer of foam padding  23  in a headset  18 . Thus, the rear or face side of the padding  23  or the first layer of padding  23  that is actually in contact with the face or other bodily member of a subject  12  has only free fabric  20  arranged as strips  20  and urged again into contact with a bodily member, such as a face of a user  12  by that padding  23 . 
         [0089]    Meanwhile, as illustrated, the front side, opposite the face contact side of the padding  23  may receive electrically conductive securement  21  or glue  21  or the like, to connect the two ends of the fabric strips  20  for a single strip thereon to itself. Thereafter, using a similar securement mechanism  21 , optional conducting foil  22  may be added as a lug  22  to provide thorough connectivity between the fabric  20  and a connector  24 . The conductor  22  is optional. 
         [0090]    As a practical matter, a connector  24  may be any of a variety of electrical connector types. These may include clips, clamps, snaps, bayonet plugs, apertures and leads, spring-loaded electrodes clamping other springs, strips, wires, or the like. In the illustrated embodiment, beginning in the upper right and progressing clockwise, the strips of fabric  20  are first secured by some securement  21 , such as a glue  21  to the front side away from the face of the padding  23 . The fabric  20  is then wrapped around to completely circumnavigate the padding  23 . The fabric  20  may then be glued to itself with a conductive securement  21 . 
         [0091]    Thereafter, a connector  24  may also be secured by any securement  21  that is conductive to assure a high level of conductivity, and very low electrical resistance between a lead  26  that will eventually be removably connected to the connector  24 , and thus provide electrical access to the conductive fabric  20 . 
         [0092]    The next image illustrates the positioning of the padding  23  against other padding. For example, padding  23   a  may represent the layer of padding  23  closest to and in contact with a member (such as a face, arm, leg, etc.) of a subject  12 . Beyond this padding layer  23   a,  may be a base padding layer  23   b.  This base layer may be more firm, and provides a conformation of shape between the user interface layer  23   a  and the display  28  or structure  28  that provides display for a subject  12 . 
         [0093]    An additional layer  23   c  has been found effective in some embodiments in order to assure that the edges of a headset  18  conform to the face of a subject  12 . Thus, the wedge-shaped padding  23   c  may be placed near the right and left edges of the pads  23   a,    23   b  in order to assure good contact between the edges of the face of a user  12 , and the contact fabric  20  operating as sensors  14 . 
         [0094]    Ultimately, the headset  18  may be placed on the head of a user  12  as illustrated in the final image, with a display  28  mounted on a frame  29  to structurally stabilize the system  10  in operation on the head or other body member of a subject  12 . 
         [0095]    As a practical matter, sensors  14  may be formed of fabric  20  in order to contact any portion of the leg, such as a calf, ankle, foot, toe, thigh, or the like. Meanwhile, hands, forearms, fingers, upper arms, elbows, and the like may be fitted with sleeves that provide a certain amount of compressive force urging sensors  14  into contact therewith. In this way, any portion or a complete body of a subject  12  may be connected to a system  10  in accordance with the invention by a system of sensors  14  on a fitting  19  such as a headset  18 , sleeve  19 , or the like. 
         [0096]    Referring to  FIG. 5 , an apparatus  40  or system  40  for implementing the present invention may include one or more nodes  42  (e.g., client  42 , computer  42 ). Such nodes  42  may contain a processor  44  or CPU  44 . The CPU  44  may be operably connected to a memory device  46 . A memory device  46  may include one or more devices such as a hard drive  48  or other non-volatile storage device  48 , a read-only memory  50  (ROM  50 ), and a random access (and usually volatile) memory  52  (RAM  52  or operational memory  52 ). Such components  44 ,  46 ,  48 ,  50 ,  52  may exist in a single node  42  or may exist in multiple nodes  42  remote from one another. 
         [0097]    In selected embodiments, the apparatus  40  may include an input device  54  for receiving inputs from a user or from another device. Input devices  54  may include one or more physical embodiments. For example, a keyboard  56  may be used for interaction with the user, as may a mouse  58  or stylus pad  60 . A touch screen  62 , a telephone  64 , or simply a telecommunications line  64 , may be used for communication with other devices, with a user, or the like. 
         [0098]    Similarly, a scanner  66  may be used to receive graphical inputs, which may or may not be translated to other formats. A hard drive  68  or other memory device  68  may be used as an input device whether resident within the particular node  42  or some other node  42  connected by a network  70 . In selected embodiments, a network card  72  (interface card) or port  74  may be provided within a node  42  to facilitate communication through such a network  70 . 
         [0099]    In certain embodiments, an output device  76  may be provided within a node  42 , or accessible within the apparatus  40 . Output devices  76  may include one or more physical hardware units. For example, in general, a port  74  may be used to accept inputs into and send outputs from the node  42 . Nevertheless, a monitor  78  may provide outputs to a user for feedback during a process, or for assisting two-way communication between the processor  44  and a user. A printer  80 , a hard drive  82 , or other device may be used for outputting information as output devices  76 . 
         [0100]    Internally, a bus  84 , or plurality of buses  84 , may operably interconnect the processor  44 , memory devices  46 , input devices  54 , output devices  76 , network card  72 , and port  74 . The bus  84  may be thought of as a data carrier. As such, the bus  84  may be embodied in numerous configurations. Wire, fiber optic line, wireless electromagnetic communications by visible light, infrared, and radio frequencies may likewise be implemented as appropriate for the bus  84  and the network  70 . 
         [0101]    In general, a network  70  to which a node  42  connects may, in turn, be connected through a router  86  to another network  88 . In general, nodes  42  may be on the same network  70 , adjoining networks (i.e., network  70  and neighboring network  88 ), or may be separated by multiple routers  86  and multiple networks as individual nodes  42  on an internetwork. The individual nodes  42  may have various communication capabilities. In certain embodiments, a minimum of logical capability may be available in any node  42 . For example, each node  42  may contain a processor  44  with more or less of the other components described hereinabove. 
         [0102]    A network  70  may include one or more servers  90 . Servers  90  may be used to manage, store, communicate, transfer, access, update, and the like, any practical number of files, databases, or the like for other nodes  42  on a network  70 . Typically, a server  90  may be accessed by all nodes  42  on a network  70 . Nevertheless, other special functions, including communications, applications, directory services, and the like, may be implemented by an individual server  90  or multiple servers  90 . 
         [0103]    In general, a node  42  may need to communicate over a network  70  with a server  90 , a router  86 , or other nodes  42 . Similarly, a node  42  may need to communicate over another neighboring network  88  in an internetwork connection with some remote node  42 . Likewise, individual components may need to communicate data with one another. A communication link may exist, in general, between any pair of devices. 
         [0104]    Referring to  FIGS. 1 through 5 , a headset  18  may provide a fitting  19  or fitting system  19  to place on a subject  12 . In one embodiment, the headset  18  may be constituted as a mask  100  with associated frame  29  fitted by padding  23  to the face of a subject  12 . In other embodiments, the fitting system  19  may be a sleeve  19  that may look like a medical brace or the like of elastomeric and fabric material urging padding  23  against the skin of a user  12  at any other bodily location appropriate for use of a system  10 . 
         [0105]    However, in the illustrated embodiments, the mask  100  operating as a significant portion of the headset  18  may include optics  102 , such as lenses  102  in order to focus the sight of a subject  12  on a screen  104 . The screen  104  may actually be provided by a smartphone  106 . A smartphone  106  may include multiple images, such as a left and right image, each accessed by optics  102  appropriate to a left and right eye of a subject  12 . Thus, a user  12  may have a screen  104  that is independent on a smartphone  106 , or a smartphone  106  may provide this screen  104  to be viewed by a user  12  through the optics  102  of a mask  100 . 
         [0106]    A securement system  102  may include various straps  114 . For example, a circumferential strap  114   a  may extend around the head of a user  12 , such as near a crown or headband location of a conventional hat. Meanwhile, a vertical strap  114   b  may stabilize the circumferential strap  114   a,  as well as supporting the weight of an electronics module  38  thereof. In the illustrated embodiment, the straps  114  may also serve to stabilize the overall force applied by the padding  23  to the face of the subject  12 . 
         [0107]    Referring to  FIG. 6 , while continuing to refer generally to  FIGS. 1 through 11 , a process  120  in accordance with the invention operating in a system  10  may rely on one of several events  122  occurring in a human body. As discussed hereinabove, an event  122  represents an activity that has electrical consequences. 
         [0108]    Those electrical consequences may be detected on the skin of a user  12 , in a non-contact sensor, through an electrode, through an electromagnetic detector, through an invasive internal probe, or by another mechanism. In general, an event  122  represents activity by a brain cell or group of cells, a neurological pathway, such as a nerve, nerve bundles, a neuro-muscular junction, or the like. 
         [0109]    It has been determined that the signals provided through sensors  14  detecting an event  122  may be complex and still processed. It is a valuable discovery that signals need not be isolated. Of course, scientists, engineers, and mathematicians classically rely on isolating variables, data streams, and the like. 
         [0110]    However, it has been found that in a system  10  in accordance with the invention, an event  122  may involve, muscles, nerves, the brain, and so forth. Accordingly, one objective is to simply observe an event  122 , regardless of what all it may activate, actuate, or change. Accordingly, an event  122  may be identified in a way that renders distinguishable from other events. 
         [0111]    For example, events may involve motions, such as extending a foot, extracting a foot, taking a step, lifting a foot, closing a hand or opening a hand, moving a finger (digit) on a hand or a foot, bending an elbow, bending a knee, lifting a leg, lifting a foot, tilting the head, raising eyebrows, raising a single eyebrow, smiling, smirking, winking, blinking, clenching teeth, opening or closing a mouth, and so forth. It has been discovered that events  122  are often recognized, in all their complexity, with sufficient precision by a human observer that each event  122  may be characterized with a name. For example, the foregoing events  122  provides an example, additional events  122  may be identified. 
         [0112]    As a practical matter one will immediately see that these events  122  may be simple, such as a blink. Others may be complex, such as a teeth clench involving various muscular activity in the face, around the eyes, and within the mind. Similarly, some events  122  may be effectively binary, such that they may exist in one of two states. 
         [0113]    This may apply for example, if a thumb is raised or lowered. This may also apply if a finger applies pressure, or releases pressure. This may also refer to an eye being opened or closed. In other situations, events may involve multiple states. For example, an event  122  that is multi-state in nature may involve various muscles throughout the face, as well as brain activity. 
         [0114]    For example, in some embodiments of events  122  it has been found that a particular event may best be detected if juxtaposed against all other conditions and combinations thereof that are not and do not include such an event. 
         [0115]    For example, in one embodiment, one may think of an event as a wink. A wink may be considered a state. However, one may determine that to distinguish a wink from all other states that are not a wink, one may test multiple states of various aspects of the face in order to compare signals. This is important to eliminate false positives. Similarly, in training particularly, events may be identified more easily if isolated. 
         [0116]    For example, one may elect to perform an action in isolation, taking certain care to avoid involving any other actions Likewise, one may perform some action involving various facial elements, such as muscles, nerves, and the like together as a compound event. Again, one may take care to avoid involving any of the elements of that event  122  in establishing the multiple states that represent “not” that compound event. 
         [0117]    For example, it has been found that a teeth clench facial movement as an event  122  is very complex, involves many muscles, involves brainwaves, and the like. It is difficult to isolate from other similar events. In contrast, a wink involves very few muscles in the face, and is a comparatively simple, isolated event, that may be tested in a more straightforward way. 
         [0118]    Meanwhile, it has been found most effective to test not a binary, but a multistate distinction. Thus, in one embodiment, events  122  may be an identified, and their data collected as such an event  122 . Then, taking care not to replicate or repeat that identified event, every other available activity may be undertaken in sequence and identified as a “not a” event. Thus, an “event A” may be distinguished from other “non A” events  122 . 
         [0119]    In the illustrated embodiment, an event  122  may result from a trigger  124 . A trigger  124  may be any identifiable activity that may be followed by a subject  12  to initiate an event  122 . The trigger  124  may be associated with or correspond to an electrical or electronic signal that is also sent to a sensor  14  in order to identify that the event  122  being recorded will surely follow. 
         [0120]    In the illustrated embodiment, the events  122   a  may begin with a response to a signal from a trigger  124 . A user  12 , observing some outward signal initiated by a trigger  124  may then act to accomplish one of the events  122   a.  Sensors  14  may receive signals such as EEG signals  125   a,  EMG signals  125   b  or EOG signals  125   c.  Electrooculography refers to sensing eye motions. This may be done by muscles, nerves, or visual sighting, such as cameras. Accordingly, the sensors  14  may be selected to receive and sense EEG signals  125   a  perceived from the brain, EMG signals  125   b  perceived from muscles, and EOG signals  125   c  perceived from the eyes. Sensors  14  may then send their output signals  127   a  to an amplifier  128 . Amplifiers  128  may be of high gain or low gain, high impedance or low impedance, and the like. It has been found useful to use comparatively high gain, amplifying systems signals  127   a  from about ten times to about one thousand times their initial magnitudes. A gain of about one hundred and more has been found suitable and necessary in many applications. 
         [0121]    The amplifiers  128  on each of the channels of sensors  14 , where each channel represents a single sensor  14  may be sent through a dedicated amplifier  128 , or a multiplexed amplifier  128 . Time division multiplexing, or code division multiplexing may be used to process high numbers of signals. 
         [0122]    However, in certain prototypical systems in accordance with the invention, the number of sensors  14  in an experiment may be from about four to about  32  sensors on a single appliance such as a mask  100  or headset  18  worn by a subject  12 . These amplifiers  128  may be dedicated each to a single channel attached to the headset  18 . Meanwhile, analog-to-digital converters  132  may take each of the signals and convert them into a format more readily and by a computer system  40 . In fact, A/DCs  132  may include additional processing, typically to normalize signals. For example, the outputs  127   b  from the amplifier  128  may be processed before being passed into the converters  132 . 
         [0123]    However, at some point prior to passing a signal  127   c  to a computer system  40 , it is helpful to normalize a signal by dividing it by some probable or maximum or impossible maximum. In this way, the values of the signals  127   c  received by a computer  40  will always range from a number value of zero and one. That is, by normalizing a signal  127   c,  dividing its value by the maximum permitted or expected value, the signal  127   c  condition is best if always normalized to a value between zero and one,  100 , or some normative maximum. 
         [0124]    Continuing to refer to  FIG. 6 , a record initiation  126  may occur as a direct consequence of an event  122   a.  Accordingly, that event record may output the signal  127   d  to the computer system  40  in order to associate a timestamp on the event record initiation  126  to the signal  127   c  corresponding to the particular event identified by that initiation  126 . 
         [0125]    Typically, the sensors  14  and the record initiation element  126  will read from the same clock  130 . That clock  130  may be part of the computer system  40 . In other embodiments, the sensors  14  may have associated therewith their own microprocessor having a clock  130 . Similarly, the event record initiation element  126  may also be a part or a programmed element of such a microprocessor. Thus, in general, a process  120  or system  120  in accordance with the invention may include and represent, as in this diagram, both hardware and software, as well as steps in a process  120 . 
         [0126]    In general, processing of the signals  127   c  by the computer system  40  may involve registration  142  of signals  127   c . For example, following a trigger  124 , a timestamp is associated with a record. 
         [0127]    In a learning configuration  134  the event record initiation  126  is important in order to correspond the signal  127   c  to a timestamp from the clock  130 , and the initiation signal  127   d . At a later point in the process  120 , registration  142  may involve aligning a timestamp and a signal  127   d , with a timestamp in a signal  127   c . The actual data representing an event  122   a  whose data is represented in the signal  127   c  may be identified more precisely as to its beginning and ending time. One mechanism for registration  142  is to intentionally render an event  122   a  to move from a non-existent state at the beginning of a data record  140   a  and then progress to an activated or different state at a later point. This is typically somewhere within a central portion of the data file or stream that represents the data record  140   a.    
         [0128]    Thereafter, as the event  122   a  signals reverses or winds down, comes to a close, the condition returns back to its initial inactive or inactivated state. Thus, the data record  140   a  for a particular event  122   a  may progress from a non-active condition, to a maximum and held condition, and then transition back to the non-existing condition. Registration  142  may actually occur by measuring the maximum value of a signal  127   c , and selecting a time period over which that signal is within some fraction, such as within ninety percent or eighty percent of that maximum value. This establishes a value and a duration in which an event  122   a  has been held in its activated condition. 
         [0129]    Thereafter, the registration process  142  may measure or calculate the time outside of the activating condition both following, and preceding the maximum activation value at which the signal drops off to approximately zero effective signal. In this way, data may actually be registered as to its maximum signal value, the duration of the maximum signal value, a duration of signal within a certain percentage or fraction of the maximum value of the signal, as well as the transition periods preceding and following ascent to that maximum value. 
         [0130]    As a reality check, registration  142  may actually take place after some initial signal processing to filter out noise. In other embodiments, registration  142  may simply select the timestamp, and process the entire duration of signal  127   c  in a particular record  140   a . Thereafter, a more precise registration  142  may be done after the automated and iterative selection process  144 , and the engine classification process  146 . 
         [0131]    In a system  10  in accordance with the invention, it has been found useful to execute a classification process  146  repeatedly. In fact, it has been found useful to march through all learning data  140   a  one segment at a time. Segments may be broken up into any time found useful. 
         [0132]    For example, in one embodiment, it has been found useful to record and event  122   a  having a total time of recordation of from about half a second to several seconds. Many times, events may occur within a period of about two or three seconds. Thus, an entire record  140   a ,  140   b,    140   c  may correspond to an event  122   a  over a period over about two or three seconds. That overall event  122   a  may be recorded in a record  140   a  reflecting a signal  127   c.    
         [0133]    The automated and iterative selection process  144  then marches through the entire time duration of a record  140   a  in pieces. For example, these may be from about ten to about one hundred fifty milliseconds each. In one currently contemplated embodiment, each segment of time selected for evaluating the signal  137   c  recorded in a record  140   a  may be about one hundred twenty eight milliseconds long. Each segment may simply advance a mere ten, twenty, thirty, or fifty milliseconds forward from the previous. 
         [0134]    Thus, the segments of signals  127   a  may actually overlap one another. In other words, a large sample of data covering 128 milliseconds may begin immediately or after some delay from the point of the timestamp provided by the signal  127   d . It may then advance by ten, twenty, thirty, or more milliseconds to a new time segment, also occupying a total duration of 128 milliseconds. Thus, the individual samples or segments may march through taking samples from an overall record  140   a  corresponding to the total elapsed time of a particular event  122   a.    
         [0135]    Another part of the automated and iterative selections  144  may involve operating the classification engine  146 . The details of the entire classification engine  146  are not repeated here. The classification engine  146  is described in great detail of the materials incorporated hereinabove by reference. However, in a system and method in accordance with the present invention, the classification engine  146  may be operated on each segment of each record  140   a  of each event  122   a  reflecting the signals  127   c.    
         [0136]    Out of the classification engine  146  come numerous signal interpretation maps. As part of the automated and iterative selection  144 , those maps are then correlated with the event  122   a . Again, correlation involves any of numerous available “numerical methods” as that term is known in the mathematical and engineering arts. 
         [0137]    Numerical methods are a class of computational methods that rely on numerical approximations to functional relationships that may or may not be definable. Accordingly, numerical methods are used in accordance with mathematical approximation theory to provide convergent solutions to insoluble mathematical equations. Thus, given a delta (some small limit) one may find an epsilon (some bounded value) within which one may compute and still be within the required delta of the actual, but not explicitly known, value of the undefined or insoluble function. 
         [0138]    Again, numerical methods fills volumes of textbooks and reference books. Accordingly, to describe them all is beyond the scope of this document. However, the terms used herein are understood in the art of numerical methods as solution techniques. Accordingly, terms like Runge-Kutta, Norton&#39;s method, the method of steepest descent, shooting methods, predictor-corrector methods, least squares fit, and the like may be used to solve approximately or to estimate approximately with any desired degree of accuracy a curve, a correlation, or the like. 
         [0139]    Meanwhile, numerous statistical methods exist for correlating numbers or functions or values, or the like with each other with some degree or percentage of certainty. Thus, one may say that with ninety five percent certainty, or at ninety five percent accuracy, some value represents a correlation between two mathematical things. 
         [0140]    Accordingly, in the automated iterative selection process  144 , the classification engine  146  conducts feature expansion processing, feature expansion, and a correlation, and eventually selects an expansion technique for processing signals  127   c . Accordingly, correlations will show which interpretation maps output by the classification engine  146  best match the “event A” or the condition A for an event. 
         [0141]    In a system and method in accordance with the invention, all events  122   a  that are not event A or condition A of event A may be processed as well, and identified as “not A.” In this way, a best correlating signal interpretation map may be selected as the signal interpretation map that will ultimately be used in a process  136  identified as an operational configuration  136 . 
         [0142]    The operational configuration  136  again passes through events  122   b , in which sensors  14  detect conditions that are forwarded as sensors  14  detect signals  125   a,    125   b,    125   c,  or the like, and output those as signals  127   a , which are typically voltages, currents, or the like. Those signals  127   a  are then amplified by amplifiers  128  to be output as signal  127   b  into A/DCs  132  that will eventually output signals  127   c  to the computer system  40  to be saved as verification data  140   b  or operational data  140   c.    
         [0143]    The difference between verification data  140   b  and operational data  140   c  is that the actual event conditions, referred to hereinabove as “condition A” and “not condition A,” meaning all other conditions that do not include a conditional A within them, are known. Thus, the verification data  140   b  is much like the learning data  140   a . The events  122   b  are known, and the system  10  is engaged to classify those events  122   b . Eventually, those classifications are compared with the known conditions of the events  122   b . If the classifications are accurate, then the signal interpretation map is considered adequate. Thereafter, the operational process  136  may operate online in real time to take operational data  140   c  from actual events  122   b , that are not known, and classify those events  122   b  as actual data. In this way, a wearer  12  or user  12  can simply perform or behave while operating a game or remote device  138 . The remote device  138  may be a computer hosting an avatar. The device  138  may be a controller controlling any device that is mechanically configured to permit electronic control of its activities. 
         [0144]    Referring to  FIG. 7 , while continuing to refer generally to  FIGS. 1 through 11 , a process  150  may proceed according to the following algorithm or methodology. Learning data  140   a  is received as the signals  127   c  becoming learning data  140   a  stored in a computer  42 , such as in a data storage  46 . The learning data  140   a  is broken into time segments. Accordingly, events  122   a  have been recorded, through their signals  125  that eventually become the outputs  127  recorded in the records  140   a . Each includes an identification of event  122   a , the signals  127   c  or their physical electronic representations, with the binding therebetween. 
         [0145]    The learning system  154  operates in accordance with the references described hereinabove and incorporated hereinabove by reference to produce interpretation maps  152 . The classification system  156  then takes map verification data  140   b  and classifies it by applying an interpretation map  158 . Again, the interpretation process  158  uses an interpretation map in order to identify membership in a category or class and a probability that a particular event  122   b  detected is a member of that class or category. An event  122   b  will have a type or name and may include other interpretations, such as a degree of a condition. Thereafter, the non-associated data  140   c  or operational data  140   c  that is not bound to any particular event may be streamed into the classification system  156  using the signal interpretation map previously developed by the engine classification system  146  and the vast interpretation map available to provide an interpretation  158 . 
         [0146]    A signal  127   d  is processed by the computing system  40  in order to return a control signal  127   e  to operate a remote device  138 . Again, any remote device will do. Anything from an engine, to a computer controller, to a mechanical device, image controller, servo-controls, or the like may be controlled in accordance with activities of a series of events  122   b  corresponding to a wearer  12 . 
         [0147]    Think of a robot or electro-mechanical device, remote from a user  12  or subject  12 , that operates in accordance with the actions of a wearer  12  of sensors  14  in a suite, a combination of sets  18 , such as headsets  18  or arm bands, leg bands, gloves, shoes, etc. Ultimately, the destination of the control signals  127   e  is selectable by a person or organization. 
         [0148]    For example, the signals may simply activate an avatar, a computer-generated image. That computer-generated image may be a face or full body. Similarly, a robotic animal may operate as a remote device  138  industrial machine, process, or robot to be controlled by a human wearer  12  of set  18  of sensors  14  in order to replicate the actions of an animal. Similarly, an actual animal may be provided with sensors  14  in order to replicate a digitally animated animal on a screen  104  of a system  10 . 
         [0149]    Referring to  FIG. 8 , the summary illustrated in the process  160  is detailed in the reference materials incorporated hereinabove by reference. As illustrated, a control module  162  provides outputs to a data module  164  which in turn provides data to a feature expansion module  166 . This information combined with weight tables, or weighting in a weight table module  168  may be provided to a consolidation module  170 . This may provide both super position  172  and aggregation  174 . Ultimately then map generation  180  may include typing confidence  176 , classification  177 , and optimization  178 . Again, discussing all the details of these is not required at this point because they represent systems in use in a method and apparatus in accordance with the current invention. 
         [0150]    Referring to  FIG. 9 , a control panel on a computer screen or other screen  104  is shown. 
         [0151]    This may include fields  184   a,    184   b  for portions of a bodily member or region being recorded. Similarly, panels  186  display classification of epics or time periods corresponding to events  122 . Various control buttons  188  may provide for set up, loading of files, identification of files by name, devices, and the status, such as whether or not a device is physically or electromechanically connected, or even electronically connected over a network. 
         [0152]    Similarly, the classification engine  146  or other electronic engines and modules may be identified as to their status. Again, communication ports, classification status, and the like may be reported. Channels may be selected, and have been demonstrated. Channels may include any number, all of which any subset may be selected for observation. 
         [0153]    Meanwhile, the channels selected will output their data on a screen  190  or display  190  as charts showing signals  127   e . During learning, the signals  127   c  from record  140   a  may be displayed on the screen  190 . During verification, the verification data  140   b  may be displayed. In particular, operational data  140   c  may be displayed on the screen  190  by channel. To the extent desired, one may display either the data  127   c , which is comparatively raw, or the data  127   d  that has been processed. 
         [0154]    One may select the filters  194  through which a signal  127   f  may pass.  127   d  has been used above for two signals, most of the  127   d &#39;s need to be changed to  127   f &#39;s.  127   d  comes from the box  126 .  127   f  comes from the box  136 . In the illustrated embodiment, a subject  12   a  is controlling an object or device  196   a.  Accordingly, an operation  200  illustrates various state outputs  202 . In fact, the state outputs  202   a  through  202   h  represent various states, charts, or devices. Accordingly, in each event a user  12  provides signals  125  that are processed and illustrated as data  204 . In fact the data graphs  204   a  through  204   h  represent different states,  202   a  through  202   h  corresponding thereto. 
         [0155]    Accordingly, as a subject  12  changes the state  202  of the face of the subject  12  from the condition illustrated by the subject  12   b  ,  12   c,    12   d,    12   e,    12   f,    12   g,    12   h , and so forth, the signals  127  corresponding to the charts  204  or graphs  204  are created. These exist for monitoring purposes. They are somewhat informative, although not typically interpretable directly by a user  12 . 
         [0156]    In accordance with the signals  127  proceeding from the graphs  204 , the controlled devices  206   a  through  206   h  are controlled thereby. In this case, the controlled device  206  is a monitor  196  or screen  196  illustrating an avatar control in accordance with the actions of the subject  12 . In accordance with the charts  202  and the devices  206   g,  representing the avatars, one will see that a neutral facial expression, a smile, an eyebrow up, eye blink, left wink, left smirk, right or left wink or smirk, or a combination thereof, a smile with brow up or down, mouth open or closed, the brow alone moving up or down, and the like may all be seen. 
         [0157]    As a practical matter, it has been found that certain facial expressions involve more muscles and therefore more data. In the illustrated embodiment, for example, a teeth clench was found to involve many more muscles, and be a much more complex signal. Accordingly, it was much more difficult to separate out from other signals. 
         [0158]    Referring to  FIG. 10 , while continuing to refer generally to  FIGS. 1 through 11 , in one embodiment, a screen  190  illustrates an image  196  along with various states  198 . The states  198  or event sources  198  may be identified in terms that are intelligible or understandable by a user  12 . For example, this illustration shows various facial blends of actions including a lower lip down left, a lower lip down right, a lower lip in, a lower lip out, and so forth. 
         [0159]    A smile may be identified as illustrated here, being either right, left, or both. Similarly, a nose scrunch, sometimes referred to in literature as “wrinkling ones nose,” may be identified. Similarly, a mouth being opened, closed, in a whistling open, or a larger or more gaping open, or the like may all be identified, and have been. Thus, one may see the output of the signals  127   e  where the remote device  138  is a screen avatar and its associated event source identifications output by the system  10 . 
         [0160]    Referring to  FIG. 11 , while to continue to refer generally to  FIGS. 1 through 11 , certain experimental embodiments are illustrated. In the illustrated embodiments, various events  122  were created by a subject  12 . The signals were processed, then provided as controls to a computer generated avatar. This demonstrates direct control of a remote device by a human subject  12  wearing sensors  14 . 
         [0161]    Referring to  FIG. 12 , a virtual reality system  208  may involve a subject  12  equipped with a headset  18  of a system  10  in accordance with the invention. In the illustrated embodiment, various elements are illustrated. For example, the individual user  12  or subject  12  may be dressed with clothing that is instrumented, and be free to move within an environment  208  in any direction  210 . 
         [0162]    For example, forward and back directions along an axis  210   a , the up and down directions along an axis  210   b , or movement right and left along an axis  210   c  may all be accommodated. In addition, movement in any circumferential direction  210   e  may occur about any of the axes  210   a ,  210   b ,  210   c . In the illustrated embodiment, a user  12  may be using a bodily member as either motion, weapon, or the like, that bodily member  211  may be any portion of the body of the subject  12 . 
         [0163]    A user  12  or subject  12  may wield an inactive article  212 . An inactive article  212  may be a sword, a bo (cudgel), nun chucks, knife, or the like. This inactive article  212  may be instrumented, or not. If instrumented, then the inactive article  212  may provide spatial identification of itself within the virtual reality environment  208 . For example, it may have sensors that are detected by light, motion, or other types of sensors. Meanwhile, the inactive article  212  may actually have electronics on board, or be detectable by electronics associated with a nearby computer system  40  associated with the environment  208 . 
         [0164]    Similarly, the user  12  may hold other articles, such as active articles  214 . Active articles  214  may be such things as guns, bows, launchers, or the like. An active article  214  may be thought of as something that typically launches a projectile or effect, and thereby affects (in the virtual environment  208 ) an area beyond its own envelope (occupied space). For example, a gun as an active article  214  may be aimed, and will shoot, not really or literally, but virtually, a projectile along a direction. Such a projectile may be replaced with a beam showing from the active article  214 , such as a barrel of a gun, the tube of a launcher, or the like. 
         [0165]    In a system  10  in accordance with the invention, the user  12  or subject  12  may be provided with a system of sensors  218  or sensor sets  218 . These sensors  218  may be manufactured as discussed hereinabove. The sensor sets  218  may contact the skin, to detect both EMG data and EEG data. The brain itself will not typically be detectable by a sensor sent  218   a  in a glove embodiment  218   a , nor a boot sensor set  218   b.  However, nerve junctions, various neural pathways, and the like may still be detected by contact sensors, or non-contact sensors contained in the various sensor sets  218 . 
         [0166]    In this regard, a suit worn by a user  12  may include various sensor sets  218 . For example, a sensor set  218  may be an elbow sleeve  218   c  extending from a forearm through an elbow region and onto an upper arm. Similarly, a knee or leg set  218   d  may extend from a calf through a knee, to a thigh. Similarly, a torso set  218   e  may cover any portion of a torso of a user  12 . Likewise, a trunk set  218   f  may include an abdomen and upper thigh area, subject to significant motion. 
         [0167]    In a system  10  in accordance with the invention, cameras or other targets on any of the sensor sets  218 , or any of the inactive articles  212  or active articles  214  may be used. Even light emitting elements may be connected on the inactive articles  212  or active articles  214 . However, that is not the principal point here. Here, the sensor sets  218  operate just as the headset  18 , such as with its conducting fabric  20  backed by padding  23  in order to assure contact between the fabric  20  and the skin of a user  12 . Again, by processing signals in accordance with the invention, the myographic data and the electroencephalographic data tell the computer system  40  through the headset  18 , and the other sensor sets  218  where the subject  12  intends to move, and where the subject  12  has moved. 
         [0168]    The earliest indicator of motions of a subject  12  will be reported by encephalographic sensors  14  in the headset  18 . Meanwhile, at neuromuscular junctions, the neurological signals may be detected by the sensor sets  218 , as discussed hereinabove. Thus, a subject  12  may engage in virtual activities, including fisticuff, wheeling of inactive articles  212  or active articles  214 , in response to views of images generated virtually on the screen  104  of the headset  18 . 
         [0169]    In such a virtual reality system  208 , a link  216 , such as a wireless link  216  may communicate between the headset  18  and a nearby computer system  40  as discussed hereinabove. The benefit of this is that subject  12  need not be encumbered by the limiting presence of wires extending from any of the sensor sets  18 ,  218  communicating with a computer system  40  present for doing additional intensive processing. 
         [0170]    The user  12  may game against others in the virtual environment  208  through an internetwork  220 , such as the internet communicating with a remote computer  222  corresponding to the computer  40 , but applying to a different user elsewhere. To the extent that encephalographic data may be relied upon, signals will be much faster, and much more quickly available than those that rely on EMG data. Moreover, either of these is available much more quickly than sensed data from targets  224  that may be placed on the articles  212 ,  214 . 
         [0171]    The use of cameras, although possible as a hybridization of a virtual reality system  208  are possible, but unnecessary. Here, the combination of encephalographic (brainwaves, neurowaves) data and electromyographic (muscle waves or signals) do not require as much processing after the learning period as would cameras. Cameras rely on so much image recognition processing that the ability to track movements of a subject  12  would be much slower, require much more processing, bigger computers, including remote computers  40  separate from the headset  18 . 
         [0172]    In a system and method in accordance with the invention, Applicant has progressed beyond the prior art concept of binary state classifications to multi-state. In the system illustrated, a particular condition or “state A” was defined. This condition or “state A” was then compared and tested against all conditions that did not include state A, and thus all identified conditions that were “not A.” This provided much improved accuracy. In prior art systems, false positives have been always problematic. In a system in accordance with the present invention, precision was provided that was greatly improved, and typically was completely accurate in controlling a remote device  206  by the events  122  generated by a subject  12 . 
         [0173]    Likewise, in a system in accordance with the invention, mixed EMG, EEG, and EOG signals are and may be processed simultaneously, as a single signal. In other embodiments, exercised in a system and method in accordance with the invention, filters, such as high pass filters, low pass filters, and the like have selected according to preferred ranges of frequency to separate out events recorded in a single data stream  127  output by a system  120  in accordance with the invention. 
         [0174]    A particular benefit has been the development of comfortable sensors  14 . These sensors  14  may be wet or dry, but have been found completely adequate as dry sensors. This stands in contrast to prior art systems which typically require comparatively invasive, even painful, penetrations, whether or not the skin is broken by sensors  14 . It has been found that one may apply sensors  14  to record EMG and EEG signals simultaneously from particular location. 
         [0175]    In other embodiments, it has been found placing the sensors  14  at locations closer to neuromuscular junctions provides enhanced neurological signals. Meanwhile, pure EMG and EEG data have been found to be somewhat offset (out of phase) from each other. For example, EMG data is somewhat delayed, inasmuch the EEG data represents the thoughts controlling the mechanical actions recorded in EMG data corresponding to events. Thus, to correlate EEG data with EMG data in accordance with the invention, it has been possible to process and filter data in order to register EEG data with the EMG data for closer correlation that accommodates the time delay therebetween. 
         [0176]    It has been a further advance to automate feature expansion in order to be able to do real time analysis of signals  127  output from an event  122 . By choosing only a single, best, signal interpretation map, processing is very fast, and classification engine  146  may quickly identify a condition representing an event  122 . 
         [0177]    In certain embodiments, it has been found useful to divide one portion of a bodily region or bodily member from another. For example, in the illustrations above, the upper face and lower face may be processed individually. In complex signals, or complex events  122 , certain activities may give false positives for other activities that are somewhat different, but which effect muscles similarly. 
         [0178]    It has been found important to consider the order in which classifications are processed. For example, teeth clench has been found to create overwhelming signals  125 ,  127 . In a teeth clench mode, so many other events are implicated to some extent or another, that all other events may be ignored in the presence in such a data avalanche. 
         [0179]    Meanwhile, the latency between one gesture and another after an event  122  has been found to be useful in processing and classifying events. The classification engine  146  may actually detect from brainwaves events sooner than from muscle waves. Similarly, certain events  122 , such as a smile may be captured with the first hint. Accordingly, in one process, the transitions move from a non-A condition to an A condition over some well-known and mapped time period. 
         [0180]    Accordingly, it is possible to then reduce the amount of data that is required when such an event  122  occurs, and transition into generating the event  122  as an output on the remote device  138 . Similarly, other gestures that cause events  122 , such as the brows typically require a longer hold for training, notwithstanding they may be detected live with a comparatively shorter sampling time. These are all new developments incorporated in a system in accordance with the invention. 
         [0181]    A library of time and frequency settings has been created. For example, EMG data tends to occur at higher frequencies than EEG data. Thus, higher frequencies indicate sources, and therefore events  122  according to those sources. Likewise, frequencies of signals  125  may range from about ten Hertz (cycles per second) up to about ninety Hertz, and above may be recorded usefully. The brainwaves may often be down as low as three Hertz. Thus, brainwaves may typically be isolated from the signals  125  by subsequent signal processing, and thus output signals  127  that are in a lower frequency range. Meanwhile, a low pass filter may isolate electromyographic signals  125   b.    
         [0182]    It has been found best to select about three to five frequencies with each of the iterations  144  to be processed by the classification engine  146 . Then it has been found useful to run up to 200 iterations or different settings. Accordingly, the classification engine may then process to create multiple signal interpretation maps. It has also been useful to evaluate the data in order to determine latency of a signal, as well as what frequencies are used and picked out of the data stream  127  to be processed by the classification engine  146 . 
         [0183]    Likewise, some events  122  have been found to be dependent or to occur longer period of time. Others are found to be more discrete. For example, a smile has been found to have a start portion, a hold portion, and a release. Even if the transitions are inaccurate or ignored, the signal interpretation engine  146  will typically be able to detect a smile, including initiation, a hold, and a release. 
         [0184]    Each library may contain files of parameters, representing numbers to pick. First, frequencies to be tried over an entire event  122 . Similarly, latency or the time period between initiation of an event  122  and certain aspects of the signal  125  occurring may be important. 
         [0185]    Similarly on recording data, in learning mode  134 , it is possible to key in, trigger, timestamp, or otherwise obtain an exact start. In later operational mode  136 , the classification engine  146  must detect events  122  according to their leading or header information. Thus, processing the header or transition period changing from a non-state-A condition to a state-A condition or beginning it become much more important for the classification engine  146  to detect. 
         [0186]    Again, importantly it is very important to test many events  122  in order to clearly distinguish between an “A condition” and a “not-A condition.” Thus, rather than the prior art systems of binary events  122  and their detection in binary signals  125 , it has been necessary to process data differently, and more of it, in order to avoid false positives. 
         [0187]    Applicants have identified multiple use cases and methodologies for brainwave virtual reality systems. These include brainwave virtual reality systems for: 
         [0188]    1) Surface Facial Expressions &amp; Emotions for Human Avatars, 
         [0189]    2) Deep-Brain Human Feelings of Frontal Lobe &amp; Limbic System. 
         [0190]    3) Thought to Speech Engine for Silent Human Communications, 
         [0191]    4) Thought to Action Engine for Animating Human Avatars, 
         [0192]    5) Peak Performance Sports Training for the Human Brain &amp; Body, 
         [0193]    6) Personal Brain Health &amp; Fitness and Human Wellness Training, 
         [0194]    7) Human Brain Meditation Guidance with Light &amp; Sound, 
         [0195]    8) Neurofeedback Training &amp; Brainwave Biofeedback Therapy, 
         [0196]    9) Light, Sound, &amp; Video Therapies &amp; Entertainment, 
         [0197]    10) Brain-Monitored Education &amp; Learning in School &amp; Beyond, 
         [0198]    11) Social Human Avatar Dating in Virtual World Environments, 
         [0199]    12) Social Media for BVR Immersive Human Connections, 
         [0200]    13) Facial Muscle Relaxation Training, 
         [0201]    14) Personal Human Smile Training, 
         [0202]    15) Personal Human Facial Beauty Awareness &amp; Beautification, 
         [0203]    16) Pure-Thought Navigation of the Internet &amp; VR Metaverses, 
         [0204]    17) Brain-Monitored Business Negotiations &amp; Transactions, 
         [0205]    18) Brain-Monitored Citizenship &amp; Immigration Applications, 
         [0206]    19) Personal Self-Improvement for Life, Health, &amp; Fitness, 
         [0207]    20) Monitoring of Blood-Brain Barrier Drug Crossings, 
         [0208]    21) Human Drug Main-Effect &amp; Side Effect Profiling, 
         [0209]    22) Therapies for Autism and Autistic Disorders, 
         [0210]    23) Therapies for ADD, ADHD, and other Psychological Disorders, 
         [0211]    24) Therapies for Seizures, Epilepsy, &amp; Migraine Headaches, 
         [0212]    25) Therapies for Multiple Sclerosis, ALS, Lou Gehrig&#39;s Disease, 
         [0213]    26) Therapies for Parkinson&#39;s Disease &amp; other Neurological Disorders, 
         [0214]    27) Therapies for Involuntary Facial Twitches &amp; Blinking Disorders, 
         [0215]    28) Web Browsing &amp; Fast Internet Searches at the Speed of Thought, 
         [0216]    29) Mouse-Cursor Point &amp; Click, 
         [0217]    30) Neuro-Linguistic Training, 
         [0218]    31) Human Brain Behavioral Modification, 
         [0219]    32) Stress Monitoring and. Stress Reduction Games &amp; Therapies, 
         [0220]    33) Thought to Action Engine for Magic &amp; Super-Powers in All Worlds, 
         [0221]    34) Emotion &amp; Feeling Therapies for Increasing Self-Awareness, 
         [0222]    35) Reading Comprehension &amp; Word Misunderstanding Games, 
         [0223]    36) Joy &amp; Peace Therapy Games to Create Personal Peace &amp; Joy, 
         [0224]    37) Neuro-Plasticity Acceleration Therapy to Repair Damaged Brains, and 
         [0225]    38) Mathematics Learning Games for All Students to Learn Math Fast. 
         [0226]    Throughout this patent application, it is to be understood that wherever “BVRX” is mentioned,that the value of “X” may be any number of sensors. 
         [0227]    As to Surface Facial Expressions &amp; Emotions for Human Avatars, the BVRX Headset and Brainwave Engine can be used to learn the brainwave patterns and the facial muscle-wave patterns corresponding to each smile, wink, frown, blink, eyebrows-raised, eyebrows-furrowed, mouth-open, mouth-closed, big smile, little smile, no smile, right smirk, left smirk, eyes open, eyes closed, eyes rolling, eyes still, eyes look right, eyes look left, and other facial gestures, human facial expressions, and movements of the human face. These learned facial patterns can then be used with the Brainwave Engine, using the mathematical software method of the Apr. 24, 1997 Signal Interpretation Patent, to create a sufficient set of Interpretation Maps to Correctly and Accurately Animate the Face of a Human Avatar or Animal Avatar to that it closely matches, resembles, and mimics the Human Facial Expressions of the Individual Human Beings who is actually wearing the BVRX Headset with the 8 Integrated Brainwave Sensors. 
         [0228]    The Facial-Expression Animated Human Avatar can then be located, activated, and deployed within any virtual space, simulated world, or metaverse for all the reasons, games, uses, and purposes of Human Facial-Expression Social VR including, face to face conversational VR, board room VR, dating VR, social chat VR, monitored facial muscle exercise VR, facial-expression therapeutic use cases, facial-muscle relaxation therapies, authentic facial-expression presence VR, poker-face VR, general social VR, social casino-game VR, and other social VR uses where a live human avatar face is helpful. 
         [0229]    As to Deep-Brain Human Feelings of Frontal Lobe &amp; Limbic System, the BVRX Headset and Brainwave Engine can be used to Find, Image, Capture, Record, identify, Interpret, Track, and Monitor the Full Range of Human Emotions and Feelings including the Human Emotions of Joy, Happiness, Peace, Serenity. 
         [0230]    Facial Expression Tracking includes the capture of apparent “Surface Facial Emotions” because human emotions can sometimes be partially guessed simply by closely inspecting the surface facial features of the human face. However, the BVRX Headset and Brainwave Engine can also be used to accurately capture and monitor the more real and authentic “Deep Brain Emotions” and “Deep Brain Feelings” of the human brain, mind, and heart. The 8 Brainwave Sensors of the BVRX Headset. 
         [0231]    The Human Limbic System is located deep inside the human brain, and this Limbic system is largely responsible for generating and maintaining the true emotional feelings and real deep emotions of a human being. The only way to accurately find, record, and capture these deep, true, limbic emotions is with an advanced technology that can measure and probe the behavior of the deep-brain limbic system activity. The BVRX Headset is such a technology because it has 8 brainwave sensors that can sense, measure, and record the electrical activity emanating from regions deep inside the human brain. No surface facial camera can capture these deep brain activity. But BVRX technology can. 
         [0232]    As to Thought-to-Speech for Silent Human Communications, the BVRX Headset &amp; Brainwave Engine can be used create a “Thought to Speech Engine” in which a person&#39;s language-thoughts are captured and automatically translated into audible speech. An individual&#39;s Silent Pure Word Thoughts can be correlated with Brainwave Patterns which are then interpreted and translated into Clear Spoken Speech by finding, capturing, and isolating the exact brain wave patterns that correspond to, and precede each spoken word. The Brainwave Sensors in the BVRX Headset measure and record raw electrical human brainwaves and facial muscle-waves as they flow from a. human head. These raw human brainwaves contain word-specific patterns that precede by milliseconds the actual audible speaking of the specific words. 
         [0233]    The Brainwave VR Thought-to-Speech Engine can be used in  4  different modes as follows: i) Quiet Speaking Mode, ii) Whisper Mode, iii) Silent Mouthing Mode, iv) Pure Thought Mode. 
         [0234]    As to a Thought to Action Engine for Animating Human Avatars, the BVRX, BVR16, and BVR32 Headsets and. Brainwave Engine can be used in the manner described in the 1997 Patent to capture human thoughts of movement and motor intentions to animate the bodies, limbs, faces, hands, feet, toes, and fingers of human avatars to help them move and navigate in virtual worlds. 
         [0235]    As to Peak Performance Sports Training for the Human Brain &amp; Body, the BVRX Headset &amp; Brainwave Engine can be used to help athletes and other people improve their flow, efficiency, smoothness, accuracy, and overall performance in their sports activities, games, business transactions, decision making, movement execution, and also improve in many other areas of life. This is done by helping the athlete find and identify which brainwave patterns precede his best sports movements, and then helping him find and repeat these healthy brain wave patterns of peak performance in order to help him re-enter the flow of peaceful, focused movement-execution. This is a type of brainwave-pattern biofeedback to augment and optimize peak performance in sports, games, and every area of life. 
         [0236]    As to Personal Brain Health &amp; Fitness and Human Wellness Training, and Brain-Monitored Education &amp; Learning, the BVRX Headset &amp; Brainwave Engine can be used to closely monitor the activity of the human brain in various settings and situations where training, fitness, education and learning or the like may be the primary goal or one of the goals. 
         [0237]    For example, it has been observed that large brain-state changes occur while reading past a misunderstood word. Similarly, math learning may be monitored. This applies to Mathematics Learning Games for Students to Learn Math Fast. 
         [0238]    As to Personal Human Smile Training, the BVRX Headset and Brainwave Engine can he used to provide very helpful Smile-Feedback and therapeutic Personal Human Smile Training for all human beings including patients and individuals suffering from Autism, ADD, ADHD, and other types of neurological, emotional, psychosomatic, psychological, and other facial-expression disorders. 
         [0239]    Sometimes our human smile is not as good, smooth, beautiful, handsome, clear, convincing, genuine, or sincere-looking as we want it to be. Sometimes the smile of our lips and mouth does not properly match the smile or pseudo-smile of our eyes and eye-muscles. And sometimes simply looking in an old low-tech regular common glass mirror (as we have been doing for decades) is just not enough to give us the full feedback we desire and the important information we really need about our own personal smile and other facial expressions of our very own human face. It is very helpful to see the human face and smile of our very own personal avatar in VR, and at the very same time to see indicators and signs on or near our avatar face that indicate the true nature and current flavor of our deep seated limbic brain emotions and true inner feelings. 
         [0240]    It is very helpful to see our (1) true inner feelings, while at the same time seeing our (2) avatar face in VR, and also seeing our (3) actual face in this real world. By seeing all three of these reflections of our human emotions at the same time we can get the necessary feedback and useful information we need to help us relax into healthy healing deep brain states of peace and contentment, as our stress melts away, and we allow our true feelings of peace and tranquility and happiness reflex into our true smiles and avatar smiles in a genuine and natural way so our smiles in VR, AR, and regular base reality will be truly beautiful, handsome, genuine, sincere, natural, and very good looking. In this way we can use BVRX Technology to teach and train ourselves to have more genuine human smiles of greater beauty and genuine human warmth and truth. 
         [0241]    As to Therapies for Involuntary Facial Twitches &amp; Blinking Disorders, Human Brain Behavioral Modification, Stress Monitoring and Stress Reduction Games &amp; Therapies, the necessary and immediate feedback may be provided directly to a user  12  through the headset  18  as in  FIG. 11 . 
         [0242]    As to Thought-to-Action Engine for Magic &amp; Super-Powers in all Virtual Worlds, the BVRX Headsets and Brainwave Engine can be used in the manner described above to find, capture, interpret, and translate Human Brain Thought and Human Brain Intention to move things, flip switches, change things, and do things to other things in the real world (via computers, electronics, relays, motors, actuators, etc.) and in all virtual worlds. This will effectively give all human beings (with BVR# Headsets) the super powers and magic abilities of the action heroes of Hollywood&#39;s best Fantasy Films and Science Fiction Movies. 
         [0243]    Uses of this BVR Headset Invention has been shown to remotely control devices capable of computer and network communication. For example, the foregoing may apply to Human Facial Expression Recognition, Human Avatar Facial Animation in VR, Human Avatar Virtual Body Animation, Human Avatar Guidance, Movement, and Control, Human Emotion Detection and Tracking, Biosignal Electric Control of wheelchairs, BioSignal Control of Virtual Mouse Cursor, BioSignal Point and Click to Select Virtual Objects, and Brainwave Video Game Influence and Control. 
         [0244]    With Soft and Comfortable, Soft, Brainwave Sensors as described hereinabove, one may monitor control, and verify individuals&#39; Human Self-Learning, Facial Expression Recognition, Brain-State Capture, Control of Video Games, and Brainwave Control of Prosthetic Limbs. Brainwave signals may substitute for spinal cord reconnection. 
         [0245]    The Brainwave Virtual Reality (BVR) Headset, Brainwave Engine, and Brain Operating System (BUS) constitute a BVR Technological Platform enabling the following applications: BVR Avatars: A BVR Avatar is a brainwave-controlled avatar game character in a virtual reality simulated environment that is at least partially controlled by the brainwaves (or body waves) of the brain (or body) of the human player. 
         [0246]    BVR Dating Avatars have enhanced abilities for a better virtual dating experience for singles, couples, friends, strangers, friend groups, families, family members, business associates, members of organizations, sports teams, clubs, and other individuals and groups and people of all ages. The BVR Technology can enhance the abilities of the Dating Avatars and improve the Player-Avatar Connection to improve BVR Social Dating in many ways. BVR Facial Expression Recognition Technology allows each avatar to see its date&#39;s facial expressions live in real time to enhance the avatar dating experience. 
         [0247]    The BVR Technology can also be used to allow an avatar to better sense it&#39;s date&#39;s moods and emotions by capturing the various brainwave patterns of distinct human emotional brain states and. making this information available to one or more of the dating avatars or dating game players. The BVR Avatar Human Emotion Interpretation, Capture, Imaging, Tracking, and Reporting for Virtual Dating, Game Playing, Emotion-Communication, Business Consultations, Job interviews, Emotional Health Assessment, and Emotion Therapy. 
         [0248]    The BVR Technology can also be used to allow enhanced avatar-to-avatar communication during the simulated virtual dating experience. The BVR Technology can be used to capture and recognize the intended word-patterns of the brainwaves and facial muscle-waves of each human player&#39;s head and face as each word is spoken, silently mouthed, silently spoken, whispered, thought, intended, silently spoken with the mouth closed, or barely spoken, softly spoken, or spoken in a different way, or regularly spoken. The captured BVR Brainwave Word-patterns or facial muscle-wave word-patterns can then be used to provide and generate good word-synthesized clearly spoken words from one human player to another via their respective dating avatars or directly between the two human beings seeking to communicate. 
         [0249]    Brainwave Augmented Reality (BAR) Technology that uses the silent mouthing of words to generate the audible speech of spoken words to provide more convenient BAR-assisted communications worldwide between all people. BAR Technology for human thought-to-speech recognition by capturing and interpreting the brainwave patterns that precede and generate spoken words. BVR Technology and BAR Technology for brainwave control of motors, machines, remote controlled aircraft, drones, cars, trucks, equipment. BVR &amp; BAR Technology for the scientific study and mapping of the human brain and animal brains. 
         [0250]    One embodiment of the foregoing may be characterized as Event Resolution Imaging (ERI). The advanced mathematical “waveform interpretation engine” intelligently sorts through massive amounts of complex data to locate meaningful information. The ERI engine is software in accordance with the invention acts as a Brain Operating System (BOS) to be applied to any type of waveform, such as sound waves, heart waves (EKG), muscle waves (EMG) and especially brain waves (EEG). By its signal processing, the ERI interpretation engine searches for the small hidden signal that is normally undetectable in the midst of a vast background of unwanted noise. 
         [0251]    Referring to  FIGS. 13 through 16 , actual computer screenshots illustrate how the ERI interpretation engine worked, in some applications. 
         [0252]    Each screenshot image basically includes some jagged lines (waveforms), followed by smoother curvy lines, then various icons and symbols at the bottom. The jagged blue lines are actual human brainwaves recorded from multiple EEG electrodes (brainwave sensors) placed on a person&#39;s scalp. These brainwaves were processed with the ERI engine to create the curvy lines, which could be called the “interpreted” waveforms. The brainwaves represent the raw data that contain small, but meaningful signals hidden somewhere in the midst of a very large amount of “background noise.” Once the ERI engine sorted through the complex blue brainwaves, it found the small hidden signals. It then amplified these signals and erased all the background noise to make them very distinct and visually noticeable. These now very crisp signals are the curvy lines. 
         [0253]    So what are these meaningful hidden signals that the crisp, curvy lines represent? In this case, they are the alternating movements of a person&#39;s right and left thumbs. The interpreted waveform signals indicate which thumb was moved at precisely what time and for how long. Through the process of Event Resolution Imaging (ERI), what were once unimaginably complex raw brainwaves are elegantly transformed into simple quantifiable signals. 
         [0254]    Referring to  FIGS. 13 through 14 , as demonstrated, the method of Event Resolution Imaging (ERI) was successfully used to interpret brainwave packets from a motor movement study on a trial by trial basis (single trial signal interpretation). While the previous example was from a brainwave study involving thumb movement detection, very similar results have been obtained from studies involving various visual, touch, cognitive, and other neurally represented human events. 
         [0255]    The screenshot image shows ten columns of data from the study. Each 384 ms epoch (column) contains either a Lower Left Quadrant Visual Flash or a Lower Right Quadrant Visual Flash event-type. The epochs alternate by event-type, beginning with the lower left quadrant flash epochs. The epoch label indicates which event-type the epoch truly was. The epoch classification channel gives the type of epoch assigned by the method. The probability channel assigns a computer calculated probability that the epoch was a lower left quadrant flash. The activation channel gives the degree to which the epoch met the criteria for its classification, from +1 for a lower left quadrant flash to −1 for a lower right quadrant flash. Notice how the wave patterns in the Single Trial Event Related Signals (STERS) correspond to the two different event types. Also, notice that although the STERS waveforms are generally robust, they do reveal significant differences in amplitude, shape and latency between epochs of the same event-type. 
         [0256]    Referring to  FIG. 15 , a touch study screenshot shows thirteen columns of data from the study. Each 484 ms epoch (column) contains either a touched or a non-touched event-type. The epochs alternate by event-type, beginning with touched epochs. The epoch label at the top indicates which type the epoch “truly” was. The epoch classification channel gives the type of epoch assigned by the program to the epoch. The Probability channel assigns a computer-calculated probability that the epoch contained a “touch”. The Activation channel gives the degree to which the epoch met the criteria for its classification, from +1 for touched, to −1 for non-touched epochs. The Accuracy channel places a check mark if the label matches the true epoch type, an “X” if it doesn&#39;t. Notice that although the STERS touched waveforms are generally robust, they do reveal significant differences in amplitude, shape, and latency between distinct touched epochs. 
         [0257]    As to  FIG. 16 , there is no movement, no sensations, purely thinking, in a purely cognitive study human thought was tracked in milliseconds. In the cognitive comprehension study, a human subject viewed a computer screen displaying a written sentence describing a situation in a picture scene such as “The horse is kicking the man.” The subject first read the sentence and viewed a correct picture (such as a picture of a horse kicking a man) and also some incorrect pictures (such as a picture of a man sitting on a horse). The pictures were then presented sequentially (one at a time) on the screen while 5 channels of raw EEG data were recorded from the subject&#39;s scalp. Raw EEG Signals: Nine 1,100 ms epochs (columns) of Raw EEG Signal data are shown in the figure above. Note that it is difficult to visually discern discriminant patterns in the 5 Raw EEG Signal channels. Cognitive EEG Signal: The Cognitive EEG Signal channel is a highly processed combination of EEG data from the 5 Raw EEG Signal channels and 5 epochs. A particular weighting pattern has been learned (discovered) and applied to a collection of amplitude, phases, locations, frequencies and latencies to generate the Cognitive EEG Signal. Note how this Cognitive EEG Signal robustly reveals the presence of a correct picture on the display screen. The fact that the Cognitive EEG Signal exhibits striking differences between correct and incorrect pictures is an indication that the subject comprehends and understands the meaning of the particular English sentences. 
         [0258]    The present invention may be embodied in other specific forms without departing from its purposes, functions, structures, or operational characteristics. The described embodiments are to be considered in all respects only as illustrative, and not restrictive. The scope of the invention is, therefore, indicated by the appended claims, rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.