Abstract: A method is provided for determining a coupling between magnetometers of an array of N magnetometers, for example with optical pumping, where each magnetometer includes a field cancellation system capable of being activated to operate the magnetometer in zero field. This method includes a first phase during which one of the N magnetometers is a measuring magnetometer whose field cancellation system is activated and the other N-1 magnetometers have their field cancellation system deactivated. This first phase includes generation by the magnetometers, of a plurality of reference magnetic fields of known amplitudes and distinct directions, the measurement, by the measuring magnetometer, of the ambient magnetic field on a plurality of measurement axes, and determination of coupling coefficients between the measuring magnetometer and each of the N magnetometers from said measurement and said known amplitudes.

Abstract: An information processing apparatus includes: a processor configured to: acquire a moving image captured by an image capturing device attached to an operator; detect an eyeball movement of the operator using a sensor attached to the operator; and specify, from among the acquired moving image, a portion that was captured when a speed of the detected eyeball movement satisfied a predetermined condition as a boundary between actions of the operator.

Abstract: Disclosed herein is an apparatus and method for generating a driving model. The driving model generation method includes sensing to collect a brain wave signal for a driver in a mobility for a predetermined time, determining the driver's state by analyzing the brain wave signal collected for the predetermined time, detecting at least one of operational information and driving information of the mobility on the basis of the determined state of the driver, and storing the detected information. Herein, the brain wave signal includes an event-related potential (ERP), and the determining includes determining, by analyzing the ERP, whether or not the driver is in an unstable state.

Abstract: Electrodes that can be formed in a flexible band of a wrist-worn device to detect hand gestures are disclosed. Multiple rows of electrodes can be configured to detect electromyography (EMG) signals produced by activity of muscles and tendons. The band can include removable electrical connections (e.g., pogo pins) to enable the electrode signals to be routed to processing circuitry in the housing of the wrist-worn device. Measurements between signals from the active electrodes and one or more reference electrodes can be obtained to capture EMG signals at a number of locations on the band. The measurement method and mode of operation (lower power coarse detection or higher power fine detection) can determine the location and number of electrodes to be measured. These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements.

Abstract: An automated evoked potential analysis apparatus for improved monitoring, detecting and identifying changes to a patient's physiological system is described. The apparatus includes an input device for obtaining electrical potential data from the patient's physiological system after application of stimulation to a patient's nerve and a computing system for receiving and analyzing the electrical potential data. The computing system includes a processing circuit configured to: generate a plurality of evoked potential waveforms (EPs) based on the electrical potential data, measure changes or trends in the generated EPs utilizing a sliding window of analysis, display incremental changes in waveforms between full subsets of EPs, and determine an alert vote for each subset, representative of changes to the physiological system generating the EPs.

Abstract: A system includes a virtual reality system configured to enable a user to interact with a virtual environment, a plurality of biofeedback sensors configured to monitor a user, and a computer system including a virtual reality module configured to generate at least a view of the virtual environment, a biofeedback module configured to fuse output of the plurality of biofeedback sensors with a plurality of events within the virtual environment, a training module configured to generate a model of user behavior, wherein the training module executed by the computer system enables the computer system to make a prediction of a user response of the user based on a corpus of biofeedback data, and an alert module configured to generate at least one alert to the user via the virtual reality system based on the user response predicted by the computer system.

Abstract: Using microstate data to evaluate Alzheimer's disease including dementia and/or mild cognitive impairment and/or depression and optionally provide treatment based on the diagnosis and/or modify treatment and/or diagnosis based on response to treatment, optionally as measured using microstate data. In one example, the method includes analyzing EEG data to generate one or more EEG parameter; and generating a risk for one or both of depression and MCI (mild cognitive impairment) based on said one or more EEG parameters. Optionally, the disease comprises MCI. Optionally, the disease comprises depression. Optionally, the one or more EEG parameter comprises a microstate-derived parameter. Optionally, the one or more EEG parameter comprises a microstate duration derived parameter.

Abstract: A medical system implements a seizure detection algorithm to detect a seizure based on a first patient parameter. The medical system monitors a second patient parameter to adjust the seizure detection algorithm. In some examples, the medical system determines whether a seizure for which therapy delivery is desirable occurred based on a second patient parameter. If a target seizure occurred, and the seizure detection algorithm did not detect the target seizure, the medical system adjusts the seizure detection algorithm to detect the target seizure. For example, the medical system may determine a first patient parameter characteristic indicative of the target seizure detected based on the second patient parameter and store the first patient parameter characteristic as part of the seizure detection algorithm. In some examples, the first patient parameter is an electrical brain signal and the second patient parameter is patient activity (e.g., patient motion or posture).

Abstract: The invention relates to a method and a device for providing a parameter which indicates a loss of consciousness of a patient under anesthesia. The method has the steps of detecting (301) at least one EEG signal on the head of the patient; continuously determining (302) the spectral cut-off frequency in a current timeframe of the EEG signal; determining (303) the curve of the spectral cut-off frequency of the EEG signal in a period of time which begins before an anesthesia-inducing medication is administered and ends after the anesthesia-induced loss of consciousness is initiated; determining (304) the absolute minimum of the spectral cut-off frequency in the period of time, wherein a negative peak of the spectral cut-off frequency lies in the absolute minimum, and providing (305) information regarding at which point in time the absolute minimum was reached as a parameter for an indicative notification of the loss of consciousness of the patient.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

Abstract: A system response of an individual listener's brain to a sound signal stimulus is estimated. The system response represents the generation of an electroencephalography signal measured at a given measuring point of the listener's head by the sound signal stimulus. A sound signal with the sound signal stimulus is presented to the ear of the listener. An audio signal corresponding to the sound signal is generated, and audio signal data is extracted from the audio signal with a measuring electrode that measures an electroencephalography training signal. A measurement data set is generated from the first electroencephalography training signal. A noise signal is measured on the listener's head while the first sound signal impinges on the listener's ear. A Gaussian conditional probability density function taken as the system response of the listener's brain to the sound signal stimulus, with respect to the given measurement point.

Abstract: Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.

Abstract: A motor control score is automatically generated for a subject based on electroencephalography (EEG) data represented as a plurality of EEG waveforms obtained from a plurality of EEG electrodes. Each waveform is separated into at least one window, containing a portion of the EEG waveform representative of neural activity corresponding to a movement performed by the subject. At least one or more frequency components and/or signal components are determined from the EEG data in the window, and a motor control score is determined based on these components. The frequency components may correspond to event-related desynchronization (ERD) response frequency band and event-related synchronization (ERS) response frequency band. In other embodiments, the frequency components correspond to a phase response and the signal components correspond to a signal power spectrum density of the window, which are provided to a primary neural network model to determine the motor control score.

Abstract: Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

Abstract: Systems and methods of dynamic IoT device regulation and control can aid in shifting a user's emotional state from a first state of mind to a preferred second state of mind, using the user's biomarker response to device settings. Particularly, an IoT device controller may be embedded within a wearable device that is wirelessly connected to a computing device and one or more IoT devices. Initially, each wearable device can be calibrated, wherein a matrix of sensed user biomarker responses can be generated. In some embodiments, the system continuously monitors user biomarkers to detect which physiological state exists. When the user enters into the first physiological/psychological state, the system can adjust each IoT device to align with the second state. When the system detects that the user biomarker response has not shifted, the system can continuously adjust IoT settings based upon a learning algorithm having monitored user biomarkers as input.

Abstract: The present disclosure provides methods for identifying non-penetrating brain injury in a subject, as well as methods for classifying a subject that received a hit to the body that transmitted an impulsive force to the brain as either having a non-penetrating brain injury or not, by analyzing one or more components of frequency-following response (FFR) following administration of an acoustic stimulus to the subject. In addition, the present disclosure provides methods for assessing a subject's recovery from a non-penetrating brain injury. Also disclosed herein are processes and systems for automatically generating acoustic stimuli and processing brain response data to identify non-penetrating brain injuries in subjects.

Abstract: A real-time control system includes a faulty variable identification technique to implement a data-driven fault detection function that provides an operator with information that enables a higher level of situational awareness of the current and likely future operating conditions of the process plant. The faulty variable identification technique enables an operator to recognize when a process plant component is behaving abnormally to potentially take action, in a current time step, to alleviate the underlying cause of the problem, thus reducing the likelihood of or preventing a stall of the process control system or a failure of the process plant component.

Abstract: Systems and methods for providing a computer-generated visualization of EEG data are disclosed. Raw EEG data generated from a multi-channel EEG headset (or other device) may be received. The EEG data may be run through a fast Fourier transform (FFT) to separate out various frequency components in each channel, isolating the brain wave components for each channel. A visual display may be generated based on the isolated components comprising a first display portion and a second display portion. The first display portion may comprise a geometrical mesh with predefined parameters representing the portions of a crystal. The second display portion may comprise a time-varying color visualization based on the variance of the brain waves. A composite computer display in which the first display portion is overlaid over the second display portion may be generated and provided via a display device.

Abstract: An implantable device includes one or more electrodes to sense an electrical signal from a brain and a waveform analyzer to identify a half wave in the electrical signal; determine an amplitude and a duration of the half wave; determine if the amplitude satisfies a half wave amplitude criterion defined by a set of amplitude parameters comprising a minimum half wave amplitude and a maximum half wave amplitude; determine if the duration satisfies a half wave duration criterion defined by a set of duration parameters comprising a minimum half wave duration and a maximum half wave duration; and identify the half wave as a qualified half wave when the half wave amplitude criterion and the half wave duration criterion is satisfied. A neurological event may be detected based on one or more qualified half waves and electrical stimulation therapy may be delivered to the brain in response to the detection.

Abstract: The invention concerns a method for calibrating a system for real-time measurement of the activity of a cognitive function of a test subject, the method comprising the successive steps of: acquiring electrical signals representative of a neural activity of a test subject; calculating values of markers of the cognitive function activity; generating a plurality of copies of calculated values of markers and adding noise to the generated copies; and, constructing a classifier by machine learning, based on the calculated marker values and noisy copies, the classifier being suitable for measuring the activity of the cognitive function of the test subject by calculating a probability that an electrical signal representative of the neural activity of the test subject results from a predetermined activity state of the cognitive function of the test subject.

Abstract: This invention relates to automatic medical diagnostic methods and systems. The object of the invention is to provide a method and a system of passive and fully automated nosological diagnostics. The objective is solved based on the method of the spectral-dynamic analysis of a wave signal from the body surface, recognition and assessment of the degree of matching to nosological groups of spectral-dynamic samples to determine the presence or stages of a disease. The system is made as a distributed system with multiple terminals for recording wave signals and provided with a system for transmitting them to the center for processing and establishing a diagnosis. To achieve more accurate diagnostic, a resonance and compensation biological feedback is used depending on the energetic type of active samples.

Abstract: A method for automatically detecting elements of interest in electrophysiological signals includes: delivering electrophysiological signals; producing a whitened time-frequency representation of the electrophysiological signals; setting a threshold; applying this threshold to the whitened time-frequency representation; and, in the whitened time-frequency representation, detecting local maxima that are higher than or equal to the applied threshold.

Abstract: This disclosure provides an EEG decoding method based on a non-negative CP decomposition model. The method extracts time component characteristics of the EEG of the different subjects in the boundary avoidance task, optimizes a characteristic dimension by using a 2-DPCA, and takes classification by using a support vector machine, so that differences of the EEG of subjects in different states can be reflected, and the EEG classification of the single subject has a great accuracy. The time component characteristics of the EEG can be obtained by using the channel components and the frequency components based on the non-negative CP decomposition model and by means of the interaction between the EEG modes. The characteristics of the obtained EEG time components have good separability, and the dimensions of the characteristics are optimized, so that the EEG of left and right hand movements in the boundary avoidance tasks can be effectively decoded.

Abstract: An electrical stimulation system includes at least one electrical stimulation lead, each of the at least one electrical stimulation lead including a plurality of stimulation electrodes; and a processor coupled to the lead and configured to perform actions, including: directing delivery of at least one electrical pulse through at least one of the stimulation electrodes of the at least one electrical stimulation lead to tissue of a patient; and directing discrete or intermittent measurement of an electrical characteristic of the tissue using at least one of the stimulation electrodes of the at least one electrical stimulation lead during, and after, delivery of the at least one electrical pulse to the tissue of the patient.

Abstract: A system of seizure detection including one or more processing circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine a plurality of metrics based on the EEG signal, the plurality of metrics indicating non-linear features of the EEG signal. The one or more processing circuit are configured to perform a preliminary analysis with one of the plurality of metrics, wherein the preliminary analysis indicates that the EEG signal indicates a candidate seizure or that the EEG signal is insignificant, perform a secondary analysis with one or more metrics of the plurality of metrics to determine whether the EEG signal indicates the candidate seizure or that the EEG signal is insignificant, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure.

Abstract: Provided are methods for diagnosing or selecting a mammal in need of a stimulant ADHD drug, conducting a non-specific electro-dermal responses (EDAs) analysis and/or an auditory sustained attention (ASAT) analysis on the mammal and administering or adjusting the dose of a stimulant ADHD drug to be administered to the mammal.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: An apparatus for measuring patient data includes a frame having a plurality of electrode hubs. Each hub can include one or more electrode members. The frame can be configured to receive a head of a patient. Each of the electrode hubs can have a single electrode member or a plurality of electrode members that extend from or are connected to an outer member for contacting a scalp of the head of the patient. The outer member can have at least one circuit configured to transmit data received by at least one of the electrode members to a measurement device via a wireless communication connection (e.g. Bluetooth, near field communication, etc.) or a wired communication connection.

Abstract: Examples described herein may predict therapy efficacy and/or therapeutic parameters using a comparison of individual patient status data and brain network response maps for the therapy. For example, VNS parameters may be predicted using a comparison of patient EEG data and brain network response maps of VNS therapy at various parameters.

Abstract: Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing device monitoring protocols and detecting abnormal patient usage of the device.

Abstract: An illustrative multimodal measurement system includes a wearable assembly configured to be worn by a user and comprising a plurality of light sources each configured to emit light directed at a target within the user, a plurality of detectors configured to detect arrival times for photons of the light after the light is scattered by the target, and a plurality of electrodes configured to be external to the user and detect electrical activity of the target.

Abstract: Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore, utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.

Abstract: A system and method for calculating representative frequency signatures for target regions of the brain. The method includes aggregating representative classified microelectrode recordings taken from regions of the brain of multiple patients to an initial database. The classified microelectrode recording data is then transformed into the frequency domain. Frequencies profiles, grouped according to each target region of the brain they represent, are stored in a database. A coherence or Z-score representing the prominence of a frequency with respect to other frequencies within a group of frequencies profiles for each target region of the brain they represent is then calculated and stored, as well as the average power of the frequencies within the group of frequency profiles. Redundant frequencies or frequencies above or below pre-determined thresholds are removed from the group of frequency.

Abstract: An electronic device to acquire and analyze electroencephalogram data of the subject includes a cognitive function examination control section that presents examination data used for a cognitive function examination of the subject at the time of executing an operation having a different purpose from that of an electroencephalogram measurement, and a cognitive function analysis section that extracts an index of a cognitive function of the subject from electroencephalogram data of the subject measured when presenting examination data.

Abstract: Methods, systems, and devices for event prediction are described. The method may include receiving real-time data associated with one or more measurements by a device. The method may also include updating one or more slope lines based on past instances of the real-time data and generating a feature vector based on the real-time data and the one or more slopes lines. The method may further include passing the first feature vector through an artificial neural network (ANN) and identifying an ANN output that acts as a predictive score. The method may also include predicting whether or not an event will occur based on the predictive score. In some cases, the predictive score may be passed through a voting mechanism, and the prediction of whether or not the event will occur may depend on an output of the voting mechanism.

Abstract: Systems and methods are disclosed to detect and/or classify electrophysiological signals as motor events.

Abstract: There is provided a method of image classification. The method includes: providing a set of category mapping discriminators, each corresponding to a respective category, wherein each category mapping discriminator of the set of category mapping discriminators is configured for discriminating features relating to input images that belong to the respective category of the category mapping discriminator; extracting a plurality of features from an input image using a machine learning model; determining, for each of the set of category mapping discriminators, an output value based on the plurality of extracted features using the category mapping discriminator; and determining a classification of the input image based on the output values of the set of category mapping discriminators.

Abstract: A device and a signal processing method that can monitor human memory performance by recognizing and characterizing high-gamma (65-250 Hz) and beta (14-30 Hz) band oscillations in the left Brodmann Area 40 (BA40) of the brain that correspond with the strength of memory encoding or correct recall. The signal processing method detects high-gamma and beta band oscillations in the electrical signals recorded from left BA40, and quantifies the spectral content, power, duration, onset, and offset of the oscillations. The oscillation's properties are used to classify the subject's memory performance on the basis of a comparison with the subject's prior human memory performance and the properties of the corresponding oscillations. A report of the subject's current memory performance can be utilized in a closed loop brain stimulation device that serves the purpose of enhancing human memory performance.

Abstract: Various examples are directed to sleep management devices and methods of operating the same. A sleep management device may comprise a display. The sleep management device may be programmed to detect a first wake event and select a first wake routine associated with the first wake event. The sleep management device may execute the first wake routine at least in part by modulating an output of the display for a first duration. The sleep management device may also detect a second wake event different than the first wake event and second wake routine associated with the second wake event. The sleep management device may execute the second wake routine at least in part by modulating the output of the display for a second duration longer than the first duration.

Abstract: An apparatus and method for monitoring a driver with epilepsy using a brain wave signal are disclosed. The monitoring method includes collecting a brain wave signal for a driver with epilepsy in a mobility for a predetermined time, determining the driver's state by analyzing the brain wave signal collected for the predetermined time, and controlling an operation of the mobility on the basis of the determined driver's state.

Abstract: The present disclosure relates to a modular system for deep brain stimulation (DBS) and electrocorticography (ECoG). The system may have an implantable neuromodulator for generating electrical stimulation signals adapted to be applied to a desired region of a brain via an attached electrode array. An aggregator module may be used for collecting and aggregating electrical signals and transmitting the electrical signals to the neuromodulator. A control module may be used which is in communication with the aggregator module for controlling generation of the electrical signals and transmitting the electrical signals to the aggregator.

Abstract: One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

Abstract: Provided is an electrode sheet allowing easy connection of a wiring that is extended, when required, to a measurement position on a living body. The electrode sheet (main sheet (1), auxiliary sheet (10)) includes a sheet-shaped flexible substrate (2, 11), wirings (3, 12) formed on the flexible substrate (2, 11), electrodes (5, 14) formed on the flexible substrate (2, 11) and electrically connected to the wirings (3, 12), and an insulating layer (4, 13) laid on the flexible substrate (2, 11) in such a manner that the wirings (3, 12) are overlaid with the insulating layer (4, 13) while the electrodes (5, 14) are exposed. The electrodes (5, 14) are formed of a conductive material in which conductive particles are dispersed in a thermoplastic resin.

Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: An internal device (1) is implanted in a living body (300). A control section (6) causes a communication section (5) to wirelessly transmit data corresponding to electroencephalogram signals of the living body (300) which are detected through a group of N (N is 2 or more) electrodes, to an external device (200). When the communication section (5) receives a designation signal designating a group of M electrode(s) (2a), M being smaller than N, the communication section (5) is caused to transmit data corresponding to electroencephalogram signals of the living body (300) which are detected through the group of M electrode(s) (2a), to the external device (200) in real time.

Abstract: An information processing apparatus includes a processor configured to acquire biological information of plural users determined to be within a predetermined area and operate an apparatus based on the biological information of the plural users.

Abstract: Disclosed herein are an apparatus and method for generating 1:1 emotion-tailored cognitive behavioral therapy in a metaverse space through an artificial intelligence (AI) control module for emotion-tailored cognitive behavioral therapy (CBT) that can measure electroencephalogram (EEG) signals when a user views and feels a metaverse virtual space and can generate metaverse virtual space content for 1:1 emotion-tailored cognitive behavioral therapy in the metaverse virtual space based on the emotional state (joy, fear, sadness, pleasure, anger, disgust, or depression) of the measured EEG signals.

Abstract: The clinical diagnosis and monitoring of patients with neurological conditions may be established through behavioral examinations, assessments or evaluations, or neuroimaging scans. The system and methods described herein diagnose the cognitive function of a subject by measuring the neural response of the subject to one or more naturalistic sensory stimuli. The system measures the subject's sensory evoked response to the naturalistic sensory stimuli by computing the statistical comparison between the subject's neural signal and either the raw stimulus signal or the stimulus' signal envelope. A latency value, or other signal feature, is extracted from the subject's sensory evoked response and a diagnosis of the subject's cognitive function is then made based on the identified latency value or other extracted signal feature.

Abstract: Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

Abstract: A method and system for determining the state of a patients brain under anesthesia. The present invention recognizes that anesthesia compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of physiological characteristics and signatures therein based on a selected anesthesia compound. The present invention aids the monitoring and/or interpreting of the physiological characteristics to a state of the patient's brain.