The method of identifying a potentially surgically operable target zone in an epileptic patient's brain includes: providing a computerized platform modelling various zones of a primate brain and connectivity between said zones; providing a model of an epileptogenic zone and a model of the propagation of an epileptic discharge from an epileptic zone to a propagation zone; obtaining a patient's personalized computerized platform; deriving the potential target zones based on modularity analysis; evaluating the target zones' effectiveness by simulating epileptic seizures propagation in the personalized patient's computerized platform; evaluating the target zones' safety by simulating spatiotemporal brain activation patterns in a defined state condition and comparing the simulated spatiotemporal brain activation patterns obtained before removal of the target zone with the spatiotemporal brain activation patterns obtained after removal of the target zone; identifying the target zones which satisfy both effectiveness and safety evaluation criteria as potentially surgically operable target zones.

The method of identifying a potentially surgically operable target zone in an epileptic patient's brain includes: providing a computerized platform modelling various zones of a primate brain and connectivity between said zones; providing a model of an epileptogenic zone and a model of the propagation of an epileptic discharge from an epileptic zone to a propagation zone; obtaining a patient's personalized computerized platform; deriving the potential target zones based on modularity analysis; evaluating the target zones' effectiveness by simulating epileptic seizures propagation in the personalized patient's computerized platform; evaluating the target zones' safety by simulating spatiotemporal brain activation patterns in a defined state condition and comparing the simulated spatiotemporal brain activation patterns obtained before removal of the target zone with the spatiotemporal brain activation patterns obtained after removal of the target zone; identifying the target zones which satisfy both effectiveness and safety evaluation criteria as potentially surgically operable target zones.

Systems, methods and programs for processing EEG data for display and/or automatically detecting a seizure in a patient based on one or more spectrograms created from the EEG data. EEG data from a patient may be paired into channels based on electrode locations. Spectrograms are generated from EEG data from channels, respectively. The spectrograms of different channels are grouped and a median power spectrogram (MPS) is calculated for the group. The MPS may be used to automatically determine whether the patient had a seizure by applying a machined learned model (ML) model. The ML model is trained and tested using historical EEG data from a plurality of patients. The MPS or a relationship between a plurality of MPS of different groups may be displayed on a bedside monitor in real-time for viewing by a bedside clinician.

Systems, methods and programs for processing EEG data for display and/or automatically detecting a seizure in a patient based on one or more spectrograms created from the EEG data. EEG data from a patient may be paired into channels based on electrode locations. Spectrograms are generated from EEG data from channels, respectively. The spectrograms of different channels are grouped and a median power spectrogram (MPS) is calculated for the group. The MPS may be used to automatically determine whether the patient had a seizure by applying a machined learned model (ML) model. The ML model is trained and tested using historical EEG data from a plurality of patients. The MPS or a relationship between a plurality of MPS of different groups may be displayed on a bedside monitor in real-time for viewing by a bedside clinician.

The present invention relates to an AI (Artificial Intelligence) based method for providing brain information comprising: a step 1 in which a brain signal obtaining portion of a brain signal measuring portion irradiates near-infrared ray to user's brain and detects light penetrating a cerebral cortex of the brain; a step 2 in which a brain signal processing portion of the brain signal measuring portion determines a level of oxygenation of hemoglobin in blood flow of the user's brain on the basis of the detected light; a step 3 in which a brain signal analyzing portion of the brain signal measuring portion extracts at least one brain activation area from a plurality of activated areas of the user's brain on the basis of the determined level of oxygenation of hemoglobin; and a step 4 in which a diagnosing portion determines a state of the user's brain on the basis of the at least one brain activation area extracted successively for a predetermined period of time, wherein a signal collecting operation of the brain signal obtaining portion in the step 1 is performed in a state that the user is moving.

The present invention relates to an AI (Artificial Intelligence) based method for providing brain information comprising: a step 1 in which a brain signal obtaining portion of a brain signal measuring portion irradiates near-infrared ray to user's brain and detects light penetrating a cerebral cortex of the brain; a step 2 in which a brain signal processing portion of the brain signal measuring portion determines a level of oxygenation of hemoglobin in blood flow of the user's brain on the basis of the detected light; a step 3 in which a brain signal analyzing portion of the brain signal measuring portion extracts at least one brain activation area from a plurality of activated areas of the user's brain on the basis of the determined level of oxygenation of hemoglobin; and a step 4 in which a diagnosing portion determines a state of the user's brain on the basis of the at least one brain activation area extracted successively for a predetermined period of time, wherein a signal collecting operation of the brain signal obtaining portion in the step 1 is performed in a state that the user is moving.

System and method for processing, non-concurrently collected, electroencephalogram (EEG) data and resting state functional magnetic resonance imaging (rsfMRI) data, non-invasively, to create a patient-specific three-dimensional (3D) mapping of the patient's functional brain network. The mapping can be used to more precisely identify candidates of resective neurosurgery and to help create a targeted surgical plan for those patients. The methodology automatically maps the patient's unique brain network using non-concurrent EEG and resting state functional MRI (rsfMRI). Generally, the current invention merges EEG data and rsfMRI data to map the patient's epilepsy/seizure network. Correlated sections of the brain and inversely correlated sections of the brain are identified to determine which sections of the brain work in conjunction with each other and which sections work oppositely to each other during epileptic episodes.

A modular electroencephalograph (EEG) system comprises a carrier board comprising one or more electrode connectors, one or more power supplies, and one or more analog-to-digital converter (ADC) modules. Each of the ADC modules comprises multiple input channels, input signal routing, at least one instrumentation power supply, configuration switches for the at least one instrumentation power supply and the input signal routing, an ADC, a programmable gain amplifier, and an ADC communications bus. Each of the one or more ADC modules electrically connects to one of the one or more electrode connectors and one of the one or more power supplies of the carrier board. An embedded computer is configured to run a real time operating system (RTOS), wherein each ADC communications bus of the one or more ADC modules is electrically connected to the embedded computer via a serial interface.

A method for artifact suppression in a sensed signal includes receiving the sensed signal sensed in a brain of a patient, wherein the sensed signal includes a neural signal and artifacts from a cardiac signal, decomposing the sensed signal into a plurality of components of the sensed signal, determining a first group of components, from the plurality of components, that are correlated with one another, determining an estimate of the cardiac signal based on the first group of components, wherein the estimate of the cardiac signal includes the cardiac signal and components of the neural signal, and generating a denoised neural signal based on the estimate of the cardiac signal and a second group of components of the plurality of components of the sensed signal, wherein the cardiac signal is suppressed in the denoised neural signal, and wherein the second group of components excludes the first group of components.

An illustrative system includes a brain interface system configured to be worn by a user and to output brain activity data associated with the user; a sleep tracking device configured to be worn by the user and to output sleep tracking data associated with the user; and a computing device configured to generate, based on the brain activity data and the sleep tracking data, sleep routine data representative of a target sleep routine for the user.