Abstract: A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.

Abstract: A cognitive function determination method includes applying a load including a sensory stimulus (5a) or tasks with different degrees of difficulty a plurality of times (S1), measuring a change in brain activity of a subject (P) when the load is applied and acquiring measurement data (S2), and determining a degree of cognitive function of the subject (P) (S3).

Abstract: Disclosed is a pain assessment method using a deep learning model, the pain assessment method including operations of receiving, by an analysis device, an image indicating activity in a specific brain area of a subject animal and allowing the analysis device to input images of regions of interest in the image into a neural network model and assess the pain of the subject animal according to a result output by the neural network model.

Abstract: Brain computer interface BCI comprising an input adapted to be connected to at least one electroencephalography EEG sensor to receive EEG signals, the BCI further comprising a processor running an associative model trained to simulate electrocorticography ECoG signal features from EEG signals received via the input, the BCI comprising an output to transmit the simulated ECoG signal features.

Abstract: A wearable device to wear on a head of a user includes a flexible band generally shaped to correspond to the user's head, the band having at least a front portion to contact at least part of a frontal region of the user's head, a rear portion to contact at least part of an occipital region of the user's head, and at least one side portion extending between the front portion and the rear portion to contact at least part of an auricular region of the user's head. A deformable earpiece is connected to the at least one side portion, the deformable earpiece including conductive material to provide at least one bio-signal sensor to contact at least part of the auricular region of the user's head. At least one additional bio-signal sensor is disposed on the band to receive bio-signals from the user.

Abstract: The method of modulating epileptogenicity in a brain of an epileptic patient includes: providing a virtual brain; providing a model of an epileptogenic and of a propagation zones and loading the models in the virtual brain to create a virtual epileptic brain; acquiring data of the brain of the epileptic patient; identifying, in the data, a location of at least one possible epileptogenic zone; fitting the virtual epileptic brain against the data acquired from the epileptic patient and parametrizing the at least one possible epileptogenic zone in the virtual epileptic brain as an epileptogenic zone; and simulating, within the virtual epileptic brain, the effect of a network modulation mimicking a clinical intervention of the brain of the patient.

Abstract: The present invention relates to a physiological monitor and system, more particularly to an electroencephalogram (EEG) monitor and system, and a method of detecting the presence and absence of artifacts and possibly removing artifacts from an EEG, other physiological signal or sensor signal without corrupting or compromising the signal. The accurate and real-time detection of the presence or absence of artifacts and removal of artifacts in an EEG or other signal allows for increased reliability in the efficacy of those signals. The strategy of rejecting artifact-corrupted EEG can result in unacceptable data loss, and asking subjects to minimize movements in order to minimize artifacts is not always feasible. The present invention allows for increased accuracy in detection and removal of artifacts from physiological signals, substantially in real time, and without the loss or corruption of signal or data in order to increase the accuracy of such signals for diagnosis and treatment purposes.

Abstract: Electrical non-invasive brain stimulation (NIBS) delivers weak electrical currents to the brain via electrodes that are affixed to the scalp. NIBS can excite or inhibit the brain in areas that are impacted by that electrical current during and for a short time following stimulation. Electrical NIBS can be used to change brain structure in terms of increasing white matter integrity as measured by diffusion tensor imaging. Together the electrical NIBS can induce changes in brain structure and function. The present methods and devices are adaptable to and configurable for facilitating the enhancement of brain performance, and the treatment of neurological diseases and tissues. The present methods and devices are advantageously designed to utilize modern electrodes deployed with, inter alia, various spatial arrangements, polarities, and current strengths to target brain areas or networks to thereby enhance performance or deliver therapeutic interventions.

Abstract: The present disclosure relates to a time-division multiplexing (TDM)-based multi-channel electrocardiogram measurement apparatus and method, which remove the influence of power line interference in a way to implement multiple channels by using a TDM method, remove an electrode DC offset (EDO) through a pre-charged capacitor, and periodically take a current out or supply a current. The TDM-based multi-channel electrocardiogram measurement apparatus and method robust against power line interference according to the present disclosure have advantages in that it can measure electrocardiogram by using multiple channels with low power and high integration based on TDM, can perform contactless measurement because an EDO is efficiently eliminated and high impedance is satisfied, and has a characteristic robust against power line interference.

Abstract: An intention decoding apparatus and a decoding method that quickly and precisely analyze brain waves for decoding an intention decision without requiring an advance brain wave measurement for, for example, a calibration step, and an intention conveyance assist apparatus, an intention conveyance assist system, and a program using a decoding result of the intention decision in the brain. The intention decoding apparatus that analyzes brain waves to decode an intention performs a process to examine a dispersion by classifying brain wave data of event-related potentials corresponding to a plurality of stimulus events into any one stimulus event and the other stimulus events among the plurality of stimulus events, on all the stimulus events, and identifies a classification where the dispersion becomes maximum to identify the one stimulus event in the classification as the intention. The intention conveyance assist apparatus includes a presentation unit that presents a decoding result.

Abstract: This invention is a head-worn device (e.g. headband, halo, or headset) with sensors (e.g. electrodes) which record brain activity. In an example, the device can be undulating with concave undulations which rest on the tops of a person's ears. In an example, the device can further comprise right side and left side ear prongs (e.g. arms, segments, or portions) which curve around the posterior and upper surfaces of a person's right and left ears.

Abstract: Disclosed are a method and an apparatus for classifying patients with brain disorders based on EEG analysis. The method for classifying patients with brain disorders based on EEG analysis includes: (a) receiving an EEG data set with a clinical diagnosis label, wherein the EEG data set includes a plurality of EEG signals and age information; (b) augmenting the EEG data set through a deep learning-based screening model, and augmenting the EEG signals and the age information in different schemes; (c) training the deep learning-based screening model to classify EEG data of patients into a target clinical diagnosis label by using the augmented EEG signals and age information; and (d) predicting a brain diagnosis label of brain disorders by applying EEG data of patients to the trained deep learning-based screening model.

Abstract: The present invention provides a risky driving prediction method and system based on a brain-computer interface, and an electronic device. The risky driving prediction method based on the brain-computer interface includes: performing, by a driver, at least one responsiveness test item before starting a vehicle; applying a brain-computer interface to acquire an electroencephalogram signal of the driver performing the at least one responsiveness test item, and analyzing the electroencephalogram signal to generate feedback information; and analyzing the feedback information based on a preset test standard to determine whether the driver has a risky driving behavior. Therefore, by using the risky driving prediction method, it can be detected whether the driver is in a state suitable for driving the vehicle without requiring the driver to do any body movement or language response.

Abstract: A system is configured to provide a survey interface that collects response data, including both quantitative and qualitative response data, using multiple capture mediums. Mediums used to capture response data include input forms that collect structured response data on particular questions, as well as multimedia input forms that capture and collect free form multimedia response data in video form. This mix of quantitative and qualitative response data is analyzed across multiple modalities and used to develop an indexed response dataset, which may be queried to determine a set of pre-configured insights. An insight interface visualizes these pre-configured insights and accepts additional queries to provide a query interface that draws from the static indexed response dataset to allow for dynamic, conversational querying for additional insights.

Abstract: The present invention relates to a method for generating a sensorial stimulus using electroencephalographic data acquired from a subject, the method comprising the following steps: receiving electroencephalographic data comprising at least two electroencephalographic signals measured simultaneously from at least two electroencephalogram channels; calculating an alpha phase neuromarker on consecutive epochs of the electroencephalographic data, the alpha phase neuromarker being representative of at least the phase coherence between alpha waves recorded from the at least two electroencephalogram channels; and generating a sensorial stimulus representing alpha phase neuromarker using an output generator.

Abstract: The present invention relates to measuring electrical impedance, and particularly to measuring impedance of electrodes used to acquire physiological signals. The measurement of electrode impedance is typically performed to ensure proper electrode-to-skin contact, and thus verify the quality of the acquired signals. Electrode-to-skin contact impedance has also clinical utility for monitoring, diagnosis, prognosis or treatment, as it can be used to measure skin conductivity, which is function of physiological processes. The present invention relates in particular to a substantially continuous method for performing such measurement. The measurement is performed in such a way that it does not affect the bioband, the range (or ranges) of frequencies that contains components used for diagnostic, prognostic, triage, and/or treatment purposes. The present invention therefore performs this impedance measurement without affecting the physiological signal while allowing for uninterrupted monitoring of said signal.

Abstract: An electronic device that detects and analyzes the energy emitted by brains of individuals. This device measures and analyzes the energy emitted from the brain activity of, for example, students, enabling the classification of students with learning difficulties based on the quantity of emitted energy. Leveraging established measuring devices, supported by computer programs akin to those utilized in electrocardiography, enables the prediction of both the quantity and speed of brain energy based on the magnetic field's intensity. By comparing the intensity of the magnetic field resulting from electrochemical energy in the brain with standard measurements for the general populace, learning difficulties can be diagnosed effectively.

Abstract: A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A device mounted on a subject's head during sleep can gather neurovascular data by measuring transcranial impedance. The device can be in the form of a single band, multiple bands, or a headcap that are worn on the subject's head. Electrodes located along an inner surface of the wearable device contact the subject's head and measure transcranial impedance. Transcranial impedance measurements are gathered from multiple locations on the subject's head and over multiple frequencies to obtain a more complete assessment of glymphatic flow.

Abstract: The method for determining an onset time and an excitability of a brain region that is not observed as recruited or not recruited in a seizure activity of an epileptic patient brain includes: providing a dynamical model of a propagation of an epileptic seizure in the brain networks; providing a statistical model which defines the probability of generating sets of observations of the state of the brain networks by said dynamical model; training the dynamical model of the propagation of an epileptic seizure using the statistical model and the data set of observations of the training cohort; and inverting the trained dynamical model and inferring the onset time and excitability of a third region from the onset time that is observed for the first and second regions using the statistical model.

Abstract: The present invention relates to a device for detecting a state of true perception loss of a human, the device including processing means operable to detect from information on electrical signals sensed adjacent to the scalp of the human the activity of oscillations present in the electrical signals as a marker for the state of true perception loss of the human.

Abstract: An operation system and an operation method that may improve operability are provided. The operation system includes a circuitry configured to determine a predicted value of an operator motion after a predetermined prediction latency from a current time based on a bio-signal using a prescribed machine learning model, the bio-signal captured from the operator and a controller configured to control a motion of a robot based on the predicted value. The operation method includes a step of determining a predicted value of an operator motion after a predetermined latency from the current time based on a bio-signal using a prescribed machine learning model, the bio-signal captured from the operator, and a step of controlling a motion of a robot on the predicted value.

Abstract: Knowledge transfer between recurrent neural networks is performed by obtaining a first output sequence from a bidirectional Recurrent Neural Network (RNN) model for an input sequence, obtaining a second output sequence from a unidirectional RNN model for the input sequence, selecting at least one first output from the first output sequence based on a similarity between the at least one first output and a second output from the second output sequence; and training the unidirectional RNN model to increase the similarity between the at least one first output and the second output.

Abstract: Some implementations disclosed herein present a computer-generated reality (CGR) environment in which a user participates in an activity, identify a cognitive state of the user (e.g., working, learning, resting, etc.) based on data regarding the user's body (e.g., facial expressions, hand movements, physiological data, etc.), and update the environment with a surrounding environment that promotes a cognitive state of the user for the activity.

Abstract: A control device for a vehicle for determining perceptual load of a visual and dynamic driving scene, the control device being configured to: receive an image sequence representing the driving scene, extract a set of scene features from the image sequence, the set of scene features representing static and/or dynamic information of the driving scene, calculate a time-aggregated representation of the image sequence based on the extracted set of scene features, calculate an attention map of the driving scene by attentional pooling of the time-aggregated representation of the image sequence, and determine the perceptual load of the driving scene based on the attention map. The invention further relates to a corresponding method.

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: 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 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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.