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.

A system for modeling neurological activity includes a computer having one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices. The program instructions are configured to receive electroencephalogram (“EEG”) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time; generate a graphical brain model representative of the brain; to convert the EEG data into a graphical EEG model representative of electrical activity; integrate the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and communicate the integrated EEG and brain model to a display.

A system for modeling neurological activity includes a computer having one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices. The program instructions are configured to receive electroencephalogram (“EEG”) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time; generate a graphical brain model representative of the brain; to convert the EEG data into a graphical EEG model representative of electrical activity; integrate the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and communicate the integrated EEG and brain model to a display.

A device, method, and non-transitory computer readable medium for identification of stress resilience. The method for identification of stress resilience includes stimulating a human subject by at least one of a plurality of stressful events in a virtual reality environment, acquiring multichannel real-time electroencephalograph (EEG) signals by an EEG monitor worn by a human subject, recording the real-time EEG signals received during the stressful event, transmitting the real-time EEG signals to a computing device. The computing device generates a plurality of filtered brain wave frequencies related to the stressful event by filtering the multichannel real-time EEG signals, classifies the brain wave frequencies by frequency level by applying the filtered brain wave frequencies to the deep learning model, applies each frequency level associated with the stressful event to the convolutional neural network, and identifies a level of stress resilience of the human subject associated with the stressful event.

A device, method, and non-transitory computer readable medium for identification of stress resilience. The method for identification of stress resilience includes stimulating a human subject by at least one of a plurality of stressful events in a virtual reality environment, acquiring multichannel real-time electroencephalograph (EEG) signals by an EEG monitor worn by a human subject, recording the real-time EEG signals received during the stressful event, transmitting the real-time EEG signals to a computing device. The computing device generates a plurality of filtered brain wave frequencies related to the stressful event by filtering the multichannel real-time EEG signals, classifies the brain wave frequencies by frequency level by applying the filtered brain wave frequencies to the deep learning model, applies each frequency level associated with the stressful event to the convolutional neural network, and identifies a level of stress resilience of the human subject associated with the stressful event.

The present invention relates to an AI (Artificial Intelligence) based device for providing brain information comprising: a brain signal measuring portion that collects a signal related to user's brain; a brain signal stimulating portion that stimulates the user's brain for an operation of collecting a signal of the brain signal measuring portion; and a diagnosing portion that determines the user's brain state on the basis of the collected signal.

A sleeping mask includes a signal processor for processing sensor data, an infrared light source coupled to the signal processor and configured to emit infrared light toward an eyelid of a user, and an array of infrared sensors coupled to the signal processor and configured to receive infrared light reflected from the eyelid of the user.

A multi-modal monitoring system is provided for monitoring activity of an individual. The monitoring system is configured as a computer system comprising data input, memory and a data processor. The data processor is configured and operable to receive and analyze first and second measured data concurrently collected from the individual and corresponding to, respectively, detected brain signals indicative of movement planning by the individual, and detected motion signals indicative of actual movement recognition by at least one body portion of the individual. The data analysis includes applying a multi-modal processing to the first and second measured data to decode the brain and body signals, and upon identifying that the decoded brain and body signals satisfy a condition of common decoded motor commands, generate a control signal indicative of the individual's intended physical action, which can be used for controlling operation of an execution device or assistance device(s), or for providing biofeedback.

A multi-modal monitoring system is provided for monitoring activity of an individual. The monitoring system is configured as a computer system comprising data input, memory and a data processor. The data processor is configured and operable to receive and analyze first and second measured data concurrently collected from the individual and corresponding to, respectively, detected brain signals indicative of movement planning by the individual, and detected motion signals indicative of actual movement recognition by at least one body portion of the individual. The data analysis includes applying a multi-modal processing to the first and second measured data to decode the brain and body signals, and upon identifying that the decoded brain and body signals satisfy a condition of common decoded motor commands, generate a control signal indicative of the individual's intended physical action, which can be used for controlling operation of an execution device or assistance device(s), or for providing biofeedback.

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.