Patent Publication Number: US-2021169404-A1

Title: System and method to enhance dream recall

Description:
CROSS-REFERENCE TO PRIOR APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/945,906, filed on 10 Dec. 2019. This application is hereby incorporated by reference herein. 
    
    
     BACKGROUND 
     1. Field 
     The present disclosure pertains to a system and method for enhancing dream recall in a subject during a sleep session. 
     2. Description of the Related Art 
     Systems for monitoring sleep and delivering sensory stimulation to subjects during sleep are known. Electroencephalogram (EEG) sensor-based sleep monitoring and sensory stimulation systems are known. 
     SUMMARY 
     It would be advantageous to enhance dream recall following a sleep session. Delivery of repetitive sensory stimulation, at a particular frequency, to a subject during a sleep session may enhance dream recall following the sleep session. Accordingly, one or more aspects of the present disclosure relate to a system configured to deliver sensory stimulation to a subject during a sleep session in order to promote dream recall. The system comprises one or more sensors, one or more sensory stimulators, one or more processors, and/or other components. The one or more sensors are configured to generate output signals conveying information related to brain activity of the subject during the sleep session. The one or more sensory stimulators are configured to provide the sensory stimulation to the subject during the sleep session. The one or more processors are coupled to the one or more sensors and the one or more sensory stimulators. The one or more processors are configured by machine-readable instructions. The one or more processors are configured to control the one or more sensory stimulators based on the brain activity of the subject. 
     In some embodiments, the one or more sensors comprise one or more electroencephalogram (EEG) electrodes configured to generate the information related to brain activity. In some embodiments, the one or more processors are further configured to detect sleep in the subject. In some embodiments, the one or more processors may detect a sleep stage such as rapid eye movement (REM) sleep. In some embodiments, detecting sleep in the subject comprises causing a neural network to be trained based on the information related to the brain activity of the subject, as captured by the EEG electrodes. The trained neural network comprises an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer. In some embodiments, an output of one or more intermediate layers of the neural network may be probabilities that a subject is in various sleep stages. In some embodiments, based on the output signals, the trained neural network may determine periods when the subject is experiencing REM sleep during the sleep session. 
     In some embodiments, once sleep (e.g., REM sleep) is detected, the one or more processors may control the one or more sensory stimulators to deliver sensory stimulation to the subject. In some embodiments, the sensory stimulation may be auditory, haptic, visual, and/or other stimulation. In some embodiments, the sensory stimulation may comprise repetitive stimulations having a constant inter-stimulation interval. In some embodiments, the interval may be approximately 160-200 milliseconds. In some embodiments, the frequency of the sensory stimulation may be in the theta range (e.g., 4-8 Hz). In some embodiments, the frequency of the sensory stimulation may be approximately in the theta range (e.g., not less than 2 Hz and not more than 10 Hz). In some embodiments, the system may monitor the output signals to determine the effect of the sensory stimulation on the brain activity of the subject. In some embodiments, the one or more processors may control the sensory stimulators to modulate the intensity of the sensory stimulation based on the output signals in order to enhance EEG theta power. In some embodiments, enhanced EEG theta power is associated with enhanced dream recall in the subject. 
     These and other objects, features, and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic illustration of a system configured to enhance dream recall in a subject, in accordance with one or more embodiments. 
         FIG. 2  illustrates several of the operations performed by the system, in accordance with one or more embodiments. 
         FIG. 3  illustrates example architecture of a deep neural network that is part of the system, in accordance with one or more embodiments. 
         FIG. 4  illustrates brain activity of a subject during delivery of sensory stimulation, in accordance with one or more embodiments. 
         FIG. 5  illustrates a method for enhancing dream recall in a subject, in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. As used herein, the term “or” means “and/or” unless the context clearly dictates otherwise. As used herein, the statement that two or more parts or components are “coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs. As used herein, “directly coupled” means that two elements are directly in contact with each other. As used herein, “fixedly coupled” or “fixed” means that two components are coupled to move as one while maintaining a constant orientation relative to each other. 
     Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein. 
       FIG. 1  is a schematic illustration of a system  10  configured to enhance dream recall in a subject  12 . System  10  is configured to facilitate delivery of sensory stimulation to subject  12  to enhance dream recall, adjust stimulation parameters based on the response of the subject to the sensory stimulation, and/or for other purposes. System  10  is configured such that sensory stimulation including auditory, haptic, visual, and/or other stimulation is delivered before and/or during sleep. In some embodiments, the initial intensity of the sensory stimulation (e.g., when entering a period of REM sleep) may be below a perceivable threshold. In some embodiments, the stimulation is modulated according to the brain activity of subject  12 . In some embodiments, the brain activity is used to calculate an EEG theta power (e.g., running average of the square of the signal that results from filtering an EEG signal in the theta band) of the subject. In some embodiments, if the EEG theta power decreases during a time period, the one or more processors may control the sensory stimulators to increase the intensity of the sensory stimulation delivered to the subject. In some embodiments, the intensity of the sensory stimulation may be increased by a factor that is proportional to the EEG theta power decrease during the time period. In some embodiments, if the EEG theta power increases, the intensity of the sensory stimulation may remain the same. During the delivery of sensory stimulation, the brain activity of the subject may be monitored for micro-arousals such that the sensory stimulation does not wake the subject. 
     Delivering sensory stimulation to enhance the EEG theta power associated with the subject is important to dream recall. Higher frontal EEG theta power (e.g., 5-8 Hz) is associated with successful dream recall in subjects. Thus, by delivering sensory stimulation that increases EEG theta power in subjects during REM sleep, the system and method described herein may enhance dream recall following a sleep session. System  10  also leverages machine-learning models (e.g., deep neural networks and/or any other supervised machine learning algorithm as described below) for automatic, real-time or near real-time, closed loop, sensor output signals for determining the sleep stages of the subject during the sleep session. As shown in  FIG. 1 , system  10  includes one or more of a sensor  14 , a sensory stimulator  16 , external resources  18 , a processor  20 , electronic storage  22 , a subject interface  24 , and/or other components. These components are further described below. 
     Sensor  14  is configured to generate output signals conveying information related to sleep stages of subject  12  during a sleep session. The output signals conveying information related to sleep stages of subject  12  may include information related to brain activity in subject  12 . As such, sensor  14  is configured to generate output signals conveying information related to brain activity. In some embodiments, sensor  14  is configured to generate output signals conveying information related to stimulation provided to subject  12  during sleep sessions (e.g., changes in brain activity of subject  12  resulting from the stimulation). In some embodiments, the information in the output signals from sensor  14  is used to control sensory stimulator  16  to provide sensory stimulation to subject  12  (as described below). 
     Sensor  14  may comprise one or more sensors that generate output signals that convey information related to brain activity in subject  12  directly. For example, sensor  14  may include electroencephalogram (EEG) electrodes configured to detect electrical activity along the scalp of subject  12  resulting from current flows within the brain of subject  12 . Sensor  14  may comprise one or more sensors that generate output signals conveying information related to brain activity of subject  12  indirectly. For example, one or more sensors  14  may comprise a heart rate sensor that generates an output based on a heart rate of subject  12  (e.g., sensor  14  may be a heart rate sensor than can be located on the chest of subject  12 , and/or be configured as a bracelet on a wrist of subject  12 , and/or be located on another limb of subject  12 ), movement of subject  12  (e.g., sensor  14  may comprise an accelerometer that can be carried on a wearable, such as a bracelet around the wrist and/or ankle of subject  12  such that sleep may be analyzed using actigraphy signals), respiration of subject  12 , and/or other characteristics of subject  12 . 
     In some embodiments, sensor  14  may comprise one or more of EEG electrodes, a respiration sensor, a pressure sensor, a vital signs camera, a functional near infra-red sensor (fNIR), a temperature sensor, a microphone and/or other sensors configured to generate output signals related to (e.g., the quantity, frequency, intensity, and/or other characteristics of) the stimulation provided to subject  12 , the brain activity of subject  12 , and/or other sensors. Although sensor  14  is illustrated at a single location near subject  12 , this is not intended to be limiting. Sensor  14  may include sensors disposed in a plurality of locations, such as for example, within (or in communication with) sensory stimulator  16 , coupled (in a removable manner) with clothing of subject  12 , worn by subject  12  (e.g., as a headband, wristband, etc.), positioned to point at subject  12  while subject  12  sleeps (e.g., a camera that conveys output signals related to movement of subject  12 ), coupled with a bed and/or other furniture where subject  12  is sleeping, and/or in other locations. 
     In  FIG. 1 , sensor  14 , sensory stimulator  16 , processor  20 , electronic storage  22 , and subject interface  24  are shown as separate entities. This is not intended to be limiting. Some and/or all of the components of system  10  and/or other components may be grouped into one or more singular devices. For example, these and/or other components may be included in a wearable device  201 . In some embodiments, wearable device  201  may be a headset as illustrated in  FIG. 2  and/or other garments worn by subject  12 . Other garments may include a cap, vest, bracelet, and/or other garment. In some embodiments, wearable device  201  may comprise one or more sensors which may contact the skin of the subject. In some embodiments, wearable device  201  may comprise one or more sensory stimulators, which may provide auditory vibrations, haptic vibrations, light pulses, and/or other stimulation. For example, wearable device  201  and/or other garments may include, for example, sensing electrodes, a reference electrode, one or more devices associated with an EEG, means to deliver auditory stimulation (e.g., a wired and/or wireless audio device and/or other devices), and one or more audio speakers. In some embodiments, wearable device  201  may comprise means to delivery visual, somatosensory, electric, magnetic, and/or other stimulation to the subject. In this example, the audio speakers may be located in and/or near the ears of subject  12  and/or in other locations. The reference electrode may be located behind the ear of subject  12 , and/or in other locations. In this example, the sensing electrodes may be configured to generate output signals conveying information related to brain activity of subject  12 , and/or other information. The output signals may be transmitted to a processor (e.g., processor  20  shown in  FIG. 1 ), a computing device (e.g., a bedside laptop) which may or may not include the processor, and/or other devices wirelessly and/or via wires. In this example, acoustic stimulation may be delivered to subject  12  via the wireless audio device and/or speakers. In this example, the sensing electrodes, the reference electrode, and the EEG devices may be represented, for example, by sensor  14  in  FIG. 1 . The wireless audio device and the speakers may be represented, for example, by sensory stimulator  16  shown in FIG. 1 . In this example, a computing device may include processor  20 , electronic storage  22 , subject interface  24 , and/or other components of system  10  shown in  FIG. 1 . 
     Sensory stimulator  16  is configured to provide sensory stimulation to subject  12 . Sensory stimulator  16  is configured to provide auditory, visual, somatosensory, electric, magnetic, and/or sensory stimulation to subject  12  prior to a sleep session, during a sleep session, and/or at other times. In some embodiments, a sleep session may comprise any period of time when subject  12  is sleeping and/or attempting to sleep. Sleep sessions may include nights of sleep, naps, and/or other sleeps sessions. In some embodiments, the system may identify a sleep session through observation of the subject (e.g., via camera, light detection, etc.). For example, if the subject lies down, turns off the lights in a room that the subject is in, and/or lies still for certain amount of time, the system may identify that a sleep session has begun. In some embodiments, the system may identify that a sleep session has begun with the subject switches on a sleep device (e.g., a Philips SmartSleep device). For example, sensory stimulator  16  may be configured to provide stimuli to subject  12  during a sleep session to enhance EEG signals during sleep (e.g., REM sleep) in subject  12 , and/or for other purposes. 
     Sensory stimulator  16  is configured to affect the EEG theta power associated with subject  12  through non-invasive brain stimulation and/or other methods. Sensory stimulator  16  may be configured to affect sleep through non-invasive brain stimulation using auditory, electric, magnetic, visual, somatosensory, and/or other sensory stimuli. The auditory, electric, magnetic, visual, somatosensory, and/or other sensory stimulation may include auditory stimulation, visual stimulation, somatosensory stimulation, electrical stimulation, magnetic stimulation, a combination of different types of stimulation, and/or other stimulation. The auditory, electric, magnetic, visual, somatosensory, and/or other sensory stimuli include odors, sounds, visual stimulation, touches, tastes, somatosensory stimulation, haptic, electrical, magnetic, and/or other stimuli. The sensory stimulation may have an intensity, a timing, and/or other characteristics. For example, stimulation may be provided to subject  12  to affect sleep in subject  12 . The stimulation may comprise a continuous stimulation delivered to the subject. The acoustic tones may include one or more repeating stimulations of a determined length separated from each other by a constant inter-stimulation interval. The intensity (e.g., volume, strength, brightness, etc.) of the stimulation may be modulated based on various factors (as described herein). The pitch and tone may also be adjusted. In some embodiments, the intensity of the stimulation is modulated according to changes in the EEG theta power associated with the subject. For example, the initial intensity of the stimulation (e.g., at the beginning of a period of REM sleep) may be set below a perceivable threshold. If the one or more sensors detect a decrease in the EEG theta power associated with the subject over a time period (e.g., six seconds), the one or more processors may control the sensory stimulators to increase the intensity of the sensory stimulation in response to the detected decrease in the EEG theta power. In some embodiments, the intensity may be increased by a factor that is proportional to the decrease in the EEG theta power over the time period (e.g., six seconds). If the one or more sensors detect a lack of change in the EEG theta power associated with the subject over a time period, the one or more processors may control the sensory stimulators to increase the intensity of the sensory stimulation in response to the detected lack of change. In some embodiments, the intensity may be increased by a certain factor. If the one or more sensors detect an increase in the EEG theta power associated with the subject over a time period, the one or more processors may control the sensory stimulators to continue delivering the sensory stimulation to the subject at a constant intensity in response to the detected increase in EEG theta power. This example is not intended to be limiting, and stimulation modulation parameters may vary. 
     Examples of sensory stimulator  16  may include one or more of a sound generator, a speaker, a music player, a tone generator, a vibrator (such as a piezoelectric member, for example) to deliver vibratory stimulation, a coil generating a magnetic field to directly stimulate the brain&#39;s cortex, one or more light generators or lamps, a fragrance dispenser, and/or other devices. In some embodiments, sensory stimulator  16  is configured to adjust the intensity, timing, and/or other parameters of the stimulation provided to subject  12  (e.g., as described below). 
     External resources  18  include sources of information (e.g., databases, websites, etc.), external entities participating with system  10  (e.g., one or more the external sleep monitoring devices, a medical records system of a health care provider, etc.), and/or other resources. In some embodiments, external resources  18  include components that facilitate communication of information, one or more servers outside of system  10 , a network (e.g., the internet), electronic storage, equipment related to Wi-Fi technology, equipment related to Bluetooth® technology, data entry devices, sensors, scanners, computing devices associated with individual subjects, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  18  may be provided by resources included in system  10 . External resources  18  may be configured to communicate with processor  20 , subject interface  24 , sensor  14 , electronic storage  22 , sensory stimulator  16 , and/or other components of system  10  via wired and/or wireless connections, via a network (e.g., a local area network and/or the internet), via cellular technology, via Wi-Fi technology, and/or via other resources. 
     Processor  20  is configured to provide information processing capabilities in system  10 . As such, processor  20  may comprise one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor  20  is shown in  FIG. 1  as a single entity, this is for illustrative purposes only. In some embodiments, processor  20  may comprise a plurality of processing units. These processing units may be physically located within the same device (e.g., sensory stimulator  16 , subject interface  24 , etc.), or processor  20  may represent processing functionality of a plurality of devices operating in coordination. In some embodiments, processor  20  may be and/or be included in a computing device such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a server, and/or other computing devices. Such computing devices may run one or more electronic applications having graphical subject interfaces configured to facilitate subject interaction with system  10 . 
     As shown in  FIG. 1 , processor  20  is configured to execute one or more computer program components. The computer program components may comprise software programs and/or algorithms coded and/or otherwise embedded in processor  20 , for example. The one or more computer program components may comprise one or more of an information component  30 , a model component  32 , a control component  34 , a modulation component  36 , and/or other components. Processor  20  may be configured to execute components  30 ,  32 ,  34 , and/or  36  by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor  20 . 
     It should be appreciated that although components  30 ,  32 ,  34 , and  36  are illustrated in  FIG. 1  as being co-located within a single processing unit, in embodiments in which processor  20  comprises multiple processing units, one or more of components  30 ,  32 ,  34 , and/or  36  may be located remotely from the other components. The description of the functionality provided by the different components  30 ,  32 ,  34 , and/or  36  described below is for illustrative purposes, and is not intended to be limiting, as any of components  30 ,  32 ,  34 , and/or  36  may provide more or less functionality than is described. For example, one or more of components  30 ,  32 ,  34 , and/or  36  may be eliminated, and some or all of its functionality may be provided by other components  30 ,  32 ,  34 , and/or  36 . As another example, processor  20  may be configured to execute one or more additional components that may perform some or all of the functionality attributed below to one of components  30 ,  32 ,  34 , and/or  36 . 
     Information component  30  is configured to determine one or more brain activity parameters of subject  12 , and/or other information. The brain activity parameters are determined based on the output signals from sensor  14  and/or other information. The brain activity parameters may indicate sleep in subject  12 . In some embodiments, the information indicating sleep in the subject may be indicative of sleep stages of subject  12 . For example, one of the sleep stages of subject  12  may be associated with rapid eye movement (REM) sleep, and/or other sleep. In some embodiments, the information in the output signals related to brain activity indicates a level of EEG theta power associated with the subject. 
     In some embodiments, the information related to brain activity that indicates EEG theta power is and/or includes EEG information and/or other information generated during sleep sessions of subject  12  and/or at other times. In some embodiments, brain activity parameters may be determined based on the EEG information and/or other information. In some embodiments, the brain activity parameters may be determined by information component  30  and/or other components of system  10 . In some embodiments, the brain activity parameters may be previously determined and be part of the historical sleep stage information obtained from external resources  18  (described below). In some embodiments, the one or more brain activity parameters are and/or are related to a frequency, amplitude, phase, presence of specific sleep patterns such as eye movements, ponto-geniculo-occipital (PGO) wave, slow wave, and/or other characteristics of an EEG signal. In some embodiments, the one or more brain activity parameters are determined based on the frequency, amplitude, and/or other characteristics of the EEG signal. In some embodiments, the determined brain activity parameters and/or the characteristics of the EEG may be and/or indicate EEG theta power and/or sleep stages. 
     Information component  30  is configured to obtain historical sleep stage information. In some embodiments, the historical sleep stage information is for subject  12  and/or other subjects. The historical sleep stage information is related to brain activity, and/or other physiological of the population of subjects and/or subject  12  that indicates sleep stages over time during previous sleep sessions of subject  12  and/or other subjects. The historical sleep stage information is related to sleep stages and/or other brain parameters of subject  12  and/or other subjects during corresponding sleep sessions, and/or other information. 
     In some embodiments, information component  30  is configured to obtain the historical sleep stage information electronically from external resources  18 , electronic storage  22 , and/or other sources of information. In some embodiments, obtaining the historical sleep stage information electronically from external resources  18 , electronic storage  22 , and/or other sources of information comprises querying one more databases and/or servers; uploading information and/or downloading information, facilitating subject input, sending and/or receiving emails, sending and/or receiving text messages, and/or sending and/or receiving other communications, and/or other obtaining operations. In some embodiments, information component  30  is configured to aggregate information from various sources (e.g., one or more of the external resources  18  described above, electronic storage  22 , etc.), arrange the information in one or more electronic databases (e.g., electronic storage  22 , and/or other electronic databases), normalize the information based on one or more features of the historical sleep stage information (e.g., length of sleep sessions, number of sleep sessions, etc.) and/or perform other operations. 
     Model component  32  is configured such that a trained neural network and/or any other supervised machine learning algorithms are caused to detect an EEG theta power and/or sleep in subject  12 . In some embodiments, this may be and/or include determining periods when subject  12  is experiencing REM sleep during the sleep session and/or other operations. By way of a non-limiting example, a trained neural network may be caused to indicate determine the periods of REM sleep and/or timing of the REM sleep of the subject based on the output signals (e.g., using the information in the output signals as input for the model) and/or other information. In some embodiments, model component  32  is configured to provide the information in the output signals to the neural network in temporal sets that correspond to individual periods during the sleep session. In some embodiments, model component  32  is configured to cause the trained neural network to output the determined REM sleep of subject  12  during the sleep session based on the temporal sets of information. (The functionality of model component  32  is further discussed below relative to  FIG. 2-3 ). In some embodiments, model component  32  is or includes the trained neural network. 
     Neural networks may be based on a large collection of neural units (or artificial neurons). Neural networks may loosely mimic the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a neural network may be connected with many other neural units of the neural network. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function that combines the values of all its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function such that a signal must surpass the threshold before it is allowed to propagate to other neural units. These neural network systems may be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. In some embodiments, neural networks may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by the neural networks, where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for neural networks may be more free flowing, with connections interacting in a more chaotic and complex fashion. 
     A trained neural network may comprise one or more intermediate or hidden layers. The intermediate layers of the trained neural network include one or more convolutional layers, one or more recurrent layers, and/or other layers of the trained neural network. Individual intermediate layers receive information from another layer as input and generate corresponding outputs. The detected sleep stages (e.g., REM sleep) are generated based on the information in the output signals from sensor  14  as processed by the layers of the neural network. 
     Control component  34  is configured to control stimulator  16  to provide stimulation to subject  12  during sleep and/or at other times. The stimulation may enhance dream recall (e.g., as described herein) and/or have other purposes. Control component  34  is configured to cause sensory stimulator  16  to provide modulated sensory stimulation to subject  12  during a sleep session. In some embodiments, control component  34  is configured to cause sensory stimulator  16  to modulate the sensory stimulation delivered to subject  12  based on a change in EEG theta power associated with subject  12  (e.g., the output from model component  32 ) and/or other information. In some embodiments, control component  34  is configured to cause sensory stimulator  16  to modulate the sensory stimulation to subject  12  based on a detected change in EEG theta power and/or other information over time during the sleep session. Control component  34  is configured to cause sensory stimulator  16  to increase the intensity of sensory stimulation to subject  12  responsive to a decrease or a lack of change in the EEG theta power associated with subject  12 . For example, control component  34  is configured such that controlling one or more sensory stimulators  16  to modulate the sensory stimulation to subject  12  during the sleep session comprises: detecting sleep (e.g., REM sleep) in the subject during a sleep session, and controlling the one or more sensory stimulators to delivery sensory stimulation to the subject during the sleep session. In some embodiments, the one or more processors may control the one or more sensory stimulators to increase the intensity of the sensory stimulation in response to a lack of change or a decrease in the EEG theta power associated with the subject over a time period (as described herein). 
     In some embodiments, control component  34  is configured to control sensory stimulator  16  to modulate an intensity of the sensory stimulation delivered to subject  12  responsive to model component  32  determining that the subject is in REM sleep and that the EEG theta power associated with the subject  12  has decreased or remained the same during a time period (e.g., six seconds). For example, model component  32  and/or control component  34  may be configured such that on detection of REM sleep, model component  32  starts a (physical or virtual) timer configured to track the time subject  12  spends in REM sleep. Control component  34  is configured to control stimulation responsive to the duration that subject  12  spends in continuous REM sleep breaching a predefined duration threshold. In some embodiments, the predefined duration threshold is determined at manufacture of system  10  and/or at other times. In some embodiments, the predefined duration threshold is determined based on information from previous sleep sessions of subject  12  and/or subjects demographically similar to subject  12  (e.g., as described above). In some embodiments, the predefined duration threshold is adjustable via subject interface  24  and/or other adjustment mechanisms. 
     Modulation component  36  is configured to cause sensory stimulator  16  to modulate an amount, a timing, and/or intensity of the sensory stimulation. Modulation component  36  is configured to cause sensory stimulator  16  to modulate the amount, timing, and/or intensity of the sensory stimulation based on the brain activity parameters, values output from the intermediate layers of the trained neural network, and/or other information. As an example, sensory stimulator  16  is caused to modulate the timing and/or intensity of the sensory stimulation based on the brain activity parameters, the values output from the convolutional layers, the values output from the recurrent layers, and/or other information. For example, modulation component  36  may be configured such that sensory stimulation is delivered with an intensity that varies according to changes in EEG theta power associated with subject  12 . In this example, decreases or lack of changes in the EEG theta power cause the modulation component  36  to increase the intensity of the stimulation delivered to subject  12 . If sleep micro-arousals are detected, modulation component  36  may be configured such that the sensory stimulation ceases. 
     By way of a non-limiting example,  FIG. 2  illustrates several of the operations performed by system  10  ( FIG. 1 ) and described above. In the example shown in process  200  of  FIG. 2 , an EEG signal  204  of subject  202  (e.g., similar to and/or the same as subject  12  shown in  FIG. 1 ) is processed (e.g., by information component  30  and model component  32  shown in  FIG. 1 ) in temporal window  206 . In some embodiments, temporal window  206  may comprise six-second increments of EEG signal  204 . In some embodiments, EEG signal  204  may be processed by a deep neural network. A deep neural network may determine sleep depth and stage information of the subject (as described below in relation to  FIG. 3 ). In some embodiments, methods described in the publication “Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG.”  Frontiers in Computational Neuroscience.  Bresch, E., Groβekathöfer, U., and Garcia-Molina, G. (2018), which is hereby incorporated by reference in its entirety, may be utilized. 
     As shown in  FIG. 2 , process  200  may determine sleep stage probabilities using deep learning algorithm  208  (e.g., as described below in relation to deep neural network architecture  300  of  FIG. 3 ). In some embodiments, deep learning algorithm  208  outputs soft prediction probabilities  210 . Soft prediction probabilities  210  are prediction probabilities for individual sleep stages. The set of soft prediction probabilities  210  constitute a so-called soft decision vector, which may be translated into a hard decision by determining which sleep stage is associated with a highest probability value (in a continuum of possible values) relative to other sleep stages. These soft decisions make it possible for system  10  ( FIG. 1 ) to consider different possible sleep states on a continuum rather than being forced to decide which discrete sleep stage “bucket” particular EEG information fits into (as in prior art systems). The terms “soft” and “hard” are not intended to be limiting but may be helpful to use to describe the operations performed by the system. For example, the term “soft output” may be used, because at this stage, any decision is possible. Indeed, the final decision could depend on post-processing of the soft outputs, for example. 
     Process  200  may then determine if subject  12  ( FIG. 1 )/ 202  is in REM sleep  212 . In some embodiments, system  10  may determine if subject  12 / 202  is in REM sleep based on soft prediction probabilities  210 . In some embodiments, if a sleep stage other than REM sleep has the highest probability, system  10  may determine that subject  12 / 202  is not in REM sleep. If the subject is not in REM sleep  212 , process  200  returns to temporal window  206  and continues to predict the sleep stages (e.g., soft prediction probabilities  210 ) until it is determined that subject  12 / 202  is in REM sleep  212 . In some embodiments, if the sleep stage with the highest probability is REM sleep, then system  10  may determine that subject  12 / 202  is in REM sleep. If the subject is in REM sleep  212 , process  200  determines if there are micro-arousals  214 . Micro-arousals  214  are identified using thresholds for instantaneous alpha and instantaneous beta power values (i.e., of the EEG signal). In some embodiments, the alpha band of the EEG signal has a frequency between 8-12 Hz. In some embodiments, the beta band of the EEG signal has a frequency between 15-30 Hz. If the instantaneous EEG alpha power breaches a first arousal threshold (e.g., 13 microvolts (RMS units)) for a predetermined period of time (e.g., at least 500-millisecond) or the instantaneous EEG beta power breaches a second arousal threshold (e.g., 2 microvolts (RMS units)) for a predetermined period of time (e.g., at least 500-millisecond), then a sleep micro-arousal is detected. 
     If a micro-arousal  214  is detected, process  200  returns to temporal window  206  and continues to predict the sleep stages (e.g., soft prediction probabilities  210 ) until it is determined that subject  12  ( FIG. 1 )/ 202  is once again in REM sleep  212 . If no micro-arousals are detected, process  200  delivers sensory stimulation  224  to subject  12 / 202  to increase the EEG theta power (to enhance dream recall). In some embodiments, the sensory stimulation may be auditory, haptic, visual, or other stimulation. In some embodiments, the sensory stimulation may have a frequency in the theta range (e.g., 4-8 Hz). The stimulation may comprise a continuous stimulation delivered to the subject. The acoustic tones may include one or more series of repeating stimulations of a determined length separated from each other by a constant inter-stimulation interval. The intensity (e.g., the volume) of the stimulation may be modulated according to changes in the EEG theta power the subject (as described herein). 
     Process  200  continues to monitor temporal window  206  during delivery of sensory stimulation  224  to subject  12 . System  10  may calculate EEG theta, beta, and alpha powers  216  based on the EEG signal  204  in temporal window  206 . System  10  ( FIG. 1 ) may compare the alpha and beta powers of the EEG signal  204  to pre-defined arousal thresholds (as described above) in order to detect micro-arousals  214 . If a micro-arousal  214  is detected, process  200  ceases delivering sensory stimulation  224  to subject  202  and process  200  returns to monitoring temporal window  206  for REM sleep  212 . In some embodiments, system  10  may utilize the EEG theta power to modulate the intensity of the sensory stimulation delivered to subject  12 / 202 . For example, the initial intensity of the stimulation (e.g., at the beginning of a period of REM sleep) may be set below a perceivable threshold. The one or more sensors of system  10  may determine whether the EEG theta power  218  increased over a time period (e.g., six seconds or another time period). In response to a detected lack of change or a detected decrease in the EEG theta power  218  over the time period, the sensory stimulators may change the stimulation settings  222  to increase the intensity of the sensory stimulation. If the one or more sensors detect a decrease in the EEG theta power associated with the subject over a time period (e.g., six seconds), the one or more processors may control the sensory stimulators to increase the intensity of the sensory stimulation. In some embodiments, the intensity may be increased by a factor that is proportional to the decrease in the EEG theta power over the time period. In some embodiments, the intensity may be increased by another factor. If the one or more sensors detect a lack of change in the EEG theta power associated with the subject over a time period, the one or more processors may control the sensory stimulators to increase the intensity of the sensory stimulation. In some embodiments, the intensity may be increased by a certain factor. In some embodiments, if the one or more sensors of system  10  detect an increase in the EEG theta power  218  over the time period, the sensory stimulators may continue delivering the sensory stimulation to the subject at a constant intensity  220 . 
       FIG. 3  illustrates example architecture  300  of a deep neural network (e.g., for processing temporal window  206 , as shown in  FIG. 2 ) that is part of system  10  ( FIGS. 1 and 2 ).  FIG. 3  illustrates deep neural network architecture  300  for three (unrolled) EEG windows  304 ,  306 , and  308 . In some embodiments, windows  304 ,  306 , and  308  may be windows of an EEG signal  302  for pre-defined time periods (e.g., six seconds). Architecture  300  includes convolutional layers  310 ,  312 , and  314 , and recurrent layers  322 ,  324 , and  326 . As described above, convolutional layers  310 ,  312 , and  314  can be thought of as filters and produce convolution outputs  316 ,  318 , and  320  that are fed to recurrent layers  322 ,  324 , and  326  (LSTM (long short term memory) layers in this example). The output of architecture  300  for individual windows  304 ,  306 , and  308  that are processed are a set of prediction probabilities for individual sleep stages, which are termed “soft output(s)”  328 . “Hard” predictions  330  are determined by architecture  300  (model component  32  shown in  FIG. 1 ) by predicting  332  a sleep stage associated with a “soft” output with the highest value (e.g., as described below). The terms “soft” and “hard” are not intended to be limiting but may be helpful to use to describe the operations performed by the system. For example, the term “soft output” may be used, because at this stage, any decision is possible. Indeed, the final decision could depend on post-processing of the soft outputs, for example. “Argmax” in  FIG. 3  is an operator that indicates the sleep stage associated with the highest “soft output” (e.g., the highest probability). 
     For example, a useful property of neural networks is that they can produce probabilities associated with pre-defined sleep stages (e.g., Wake, REM, N1, N2, N3 sleep). Model component  32  ( FIG. 1 ) is configured such that the set of probabilities constitute a so-called soft decision vector, which may be translated into a hard decision by determining which sleep stage is associated with a highest probability value (in a continuum of possible values) relative to other sleep stages. These soft decisions make it possible for system  10  to consider different possible sleep states on a continuum rather than being forced to decide which discrete sleep stage “bucket” particular EEG information fits into (as in prior art systems). In some embodiments, other methods of determining sleep stages may be used to determine the sleep stage of the subject (e.g.,  12 , as shown in  FIG. 1 ) in addition to or instead of the systems described herein. For example, systems using two or more EEG signals may be used to determine the sleep stage of the subject. 
     Returning to  FIG. 1 , model component  32  is configured such that both the values output from convolutional layers, and the soft decision value outputs, are vectors comprising continuous values as opposed to discrete values such as sleep stages. Consequently, convolutional and recurrent (soft decision) value outputs are available to be used by system  10  to modulate the intensity of the stimulation according to changes in EEG theta power associated with the subject during sleep (e.g., a period of uninterrupted REM sleep). As described herein, modulation parameters may be determined (e.g., using information component  30  shown in  FIG. 1 ) based on the raw sensor output signals (e.g., EEG signals). 
     As described above, modulation component  36  is configured to cause sensory stimulator  16  to modulate an amount, timing, and/or intensity of the sensory stimulation. This may be done to enhance dream recall, and/or for other purposes. Modulation component  36  is configured to cause sensory stimulator to modulate the amount, timing, and/or intensity of the sensory stimulation based on the one or more brain activity and/or other parameters, values output from the convolutional and/or recurrent layers of the trained neural network, and/or other information. As an example, volume of auditory vibration, the strength of haptic vibration, the brightness of visual stimulation, and/or intensity of another type of stimulation intensities may be adjusted and/or otherwise controlled (e.g., modulated) based on value outputs from the deep neural network such as convolutional layer value outputs and recurrent layer value outputs (e.g., sleep stage (soft) prediction probabilities). In some embodiments, modulation component  36  is configured to cause one or more sensory stimulators  16  to cease delivery of sensory stimulation and/or decrease the intensity of the sensory stimulation responsive to an indication that subject  12  is experiencing one or more micro-arousals. 
     In some embodiments, modulation component  36  is configured to modulate the sensory stimulation based on the brain activity and/or other parameters alone, which may be determined based on the output signals from sensors  14  (e.g., based on a raw EEG signal). In these embodiments, the output of a deep neural network (and/or other machine learning models) continues to be used to detect sleep stages (e.g., REM sleep), as described above. However, the stimulation intensity may instead be modulated based on brain activity and/or other parameters or properties determined based on the sensor output signals (e.g., EEG theta power and/or other parameters). In some embodiments, the information in, or determined based on, the sensor output signals can also be combined with intermediate outputs of the network such as output of the convolution layers or the final outputs (soft stages) to modulate intensity (e.g., as described herein). 
       FIG. 4  illustrates brain activity  400  of a subject during delivery of sensory stimulation to a subject (e.g.,  12 , as shown in  FIG. 1 , and/or  202 , as shown in  FIG. 2 ) during a sleep session. As illustrated by brain activity  400 , graph  402  shows timing of the stimulation  408  delivered to subject  12  and the EEG signal  406  of subject  12 . As shown in graph  402 , the timing of the waves of the EEG signal  406  align with the frequency of stimulation  408  (e.g., 5 Hz). For example, after two seconds of stimulation  408 , EEG signal  406  has a frequency of approximately 5 Hz (i.e., matching the frequency of stimulation  408 ). This is shown in spectrogram  404 , which shows a peak  410  at 5 Hz. Brain activity  400  thus illustrates delivery of sensory stimulation to a subject (e.g.,  12 , as shown in  FIG. 1 , and/or  202 , as shown in  FIG. 2 ) in order to increase EEG theta power, in accordance with process  200  (e.g., as shown in  FIG. 2 ). 
     Returning to  FIG. 1 , electronic storage  22  comprises electronic storage media that electronically stores information. The electronic storage media of electronic storage  22  may comprise one or both of system storage that is provided integrally (i.e., substantially non-removable) with system  10  and/or removable storage that is removably connectable to system  10  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  22  may comprise one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), cloud storage, and/or other electronically readable storage media. Electronic storage  22  may store software algorithms, information determined by processor  20 , information received via subject interface  24  and/or external computing systems (e.g., external resources  18 ), and/or other information that enables system  10  to function as described herein. Electronic storage  22  may be (in whole or in part) a separate component within system  10 , or electronic storage  22  may be provided (in whole or in part) integrally with one or more other components of system  10  (e.g., processor  20 ). 
     Subject interface  24  is configured to provide an interface between system  10  and subject  12 , and/or other subjects through which subject  12  and/or other subjects may provide information to and receive information from system  10 . This enables data, cues, results, and/or instructions and any other communicable items, collectively referred to as “information,” to be communicated between a subject (e.g., subject  12 ) and one or more of sensor  14 , sensory stimulator  16 , external resources  18 , processor  20 , and/or other components of system  10 . For example, a hypnogram, EEG data, REM sleep stage probability, and/or other information may be displayed for subject  12  or other subjects via subject interface  24 . As another example, subject interface  24  may be and/or be included in a computing device such as a desktop computer, a laptop computer, a smartphone, a tablet computer, and/or other computing devices. Such computing devices may run one or more electronic applications having graphical subject interfaces configured to provide information to and/or receive information from subjects. 
     Examples of interface devices suitable for inclusion in subject interface  24  comprise a keypad, buttons, switches, a keyboard, knobs, levers, a display screen, a touch screen, speakers, a microphone, an indicator light, an audible alarm, a printer, a tactile feedback device, and/or other interface devices. In some embodiments, subject interface  24  comprises a plurality of separate interfaces. In some embodiments, subject interface  24  comprises at least one interface that is provided integrally with processor  20  and/or other components of system  10 . In some embodiments, subject interface  24  is configured to communicate wirelessly with processor  20  and/or other components of system  10 . 
     It is to be understood that other communication techniques, either hard-wired or wireless, are also contemplated by the present disclosure as subject interface  24 . For example, the present disclosure contemplates that subject interface  24  may be integrated with a removable storage interface provided by electronic storage  22 . In this example, information may be loaded into system  10  from removable storage (e.g., a smart card, a flash drive, a removable disk, etc.) that enables the subject(s) to customize the implementation of system  10 . Other exemplary input devices and techniques adapted for use with system  10  as subject interface  24  comprise, but are not limited to, an RS-232 port, RF link, an IR link, modem (telephone, cable or other). In short, any technique for communicating information with system  10  is contemplated by the present disclosure as subject interface  24 . 
       FIG. 5  illustrates method  500  for enhancing dream recall in a subject (e.g.,  12 , as shown in  FIG. 1 , and/or  202 , as shown in  FIG. 2 ). The system comprises one or more sensors, one or more sensory stimulators, one or more processors configured by machine-readable instructions, and/or other components. The one or more processors are configured to execute computer program components. The computer program components comprise an information component, a model component, a control component, a modulation component, and/or other components. The operations of method  500  presented below are intended to be illustrative. In some embodiments, method  500  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  500  are illustrated in  FIG. 5  and described below is not intended to be limiting. 
     In some embodiments, method  500  may be implemented in one or more processing devices such as one or more processors  20  described herein (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  500  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  500 . 
     At an operation  502 , output signals conveying information related to brain activity of the subject during the sleep session are generated. The output signals are generated during a sleep session of the subject and/or at other times. In some embodiments, operation  502  is performed by sensors the same as or similar to sensors  14  (shown in  FIG. 1  and described herein). 
     In some embodiments, operation  502  includes providing the information in the output signals to the neural network in temporal sets that correspond to periods of time during the sleep session. In some embodiments, operation  504  includes causing the trained neural network to output detected REM sleep for the subject during the sleep session based on the temporal sets of information. In some embodiments, operation  502  is performed by a processor component the same as or similar to model component  32  (shown in  FIG. 1  and described herein). 
     At an operation  504 , sleep is detected in the subject (e.g.,  12 , as shown in  FIG. 1 ) during the sleep session. In some embodiments, sleep is detected by determining sleep stage probabilities using a deep neural network (e.g., as described in relation to  FIG. 3 ). In some embodiments, it is determined that the subject is asleep when the highest probability output by a deep neural network is a sleep stage (e.g., REM, N1, N2, N3 sleep, etc.). In some embodiments, the system may identify REM sleep in the subject (e.g., as the highest probability output of the deep neural network described in  FIG. 3 ). In some embodiments, operation  504  is performed by a processor component the same as or similar to control component  34  (shown in  FIG. 1  and described herein). 
     At an operation  506 , the one or more sensory stimulators are controlled to deliver sensory stimulation to the subject during the sleep session to enhance dream recall in the subject. For example, upon detection of sleep (e.g., REM sleep) in the subject, the one or more processors may control the one or more sensory stimulators to deliver sensory stimulation (e.g., auditory, haptic, visual, and/or other stimulation) to the subject (e.g.,  12 , as shown in  FIG. 1 , and/or  202 , as shown in  FIG. 2 ). In some embodiments, the intensity (e.g., volume, strength, brightness, etc.) of the sensory stimulation may be modulated based on one or more brain activity parameters of the subject (e.g., instantaneous EEG theta power, as described in relation to  FIG. 2 ). For example, a decrease or lack of change in EEG theta power may cause the one or more processors to control the one or more sensory stimulators to increase the intensity of the sensory stimulation. An increase in EEG theta power may cause the one or more processors to control the one or more sensory stimulators to continue delivering the sensory stimulation at a constant intensity. In some embodiments, operation  506  is performed by a processor component the same as or similar to control component  34  and/or modulation component  36  (shown in  FIG. 1  and described herein). 
     In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination. 
     Although the description provided above provides detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the disclosure is not limited to the expressly disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.