Aspects of the disclosure can provide a method and device for detecting EEG signals of a first person in proximity to the device. The device can include a non-contact EEG directional circuit having non-contact sensors, the non-contact EEG directional circuit being configured to detect the EEG signals produced by a brain of the first person without making contact with the first person. The device can further include a processor coupled to the non-contact EEG directional circuit that is configured to analyze the EEG signals to detect patterns in the EEG signals that correspond to a state of the first person in proximity to the non-contacting sensor and feedback device that is configured to provide a second person with an indication of the state of the first person in proximity to the non-contacting sensor. Additionally, the device can include a contact EEG circuit having sensors that are in contact with the second person and that is configured to detect second EEG signals produced by a brain of the second person, wherein the processor is coupled to the contact EEG circuit and is configured to analyze the second EEG signals to detect patterns in the second EEG signals that correspond to a state of second the person.

There are disclosed systems and methods for seizure diagnosis by video electroencephalography (Video-EEG). There is disclosed a fully automated, portable, point-of-care diagnostic video EEG device. In an embodiment, the device includes a tracker configured for placement on a patient. The tracker has a set of sensors disposed thereon. An EEG headset is configured for detecting electrical activities of a brain of the patient. The EEG headset is configured for communicating the electrical activities of the brain of the patient. A telescoping stand provides built-in sensors. A mobile computing device is in communication with the built-in sensors and in communication with the EEG headset. A set of wheels provides controlled movement of the telescoping stand. Other embodiments are also disclosed.

A method of estimating the likelihood of brain concussion from neurophysiological data acquired from the brain of the subject is disclosed. The method comprises: identifying activity-related features in the data; constructing a subject-specific brain network activity (BNA) pattern having a plurality of nodes, wherein each node represents a feature of the activity-related features, and each pair of nodes is assigned with a connectivity weight; calculating a BNA pattern similarity describing a comparison between the constructed BNA pattern and a baseline BNA pattern being specific to the subject; and assessing the likelihood of brain concussion responsively to the BNA pattern similarity.

Methods of analysis to extract and assess brain data collected from subject animals, including humans, to detect intentional and unintentional brain activity and other unexpected signals are disclosed. These signals are correlated to higher cognitive brain functions or unintended, potentially adverse events, such as a stroke or seizure, and to translation of those signals into defined trigger events or tasks.

Disclosed is a method for scoring in real time neural signals of a subject with respect to a reference state characterized by k=1 . . . K reference covariance matrices, the method including the following steps: (i) obtaining neural signals from the subject; (ii) computing a covariance matrix of the neural signals; (iii) computing the Riemannian distances between the covariance matrix and k=1 . . . K reference covariance matrices; (iv) computing a continuous score s in real time based on at least one of the distances computed in step (iii). Also disclosed is a system and method for self-paced modulation or external modulation of neural activity of a subject.

A method of processing EEC signals received from a plurality electrodes. The method comprises processing the EEC signals to determine a modulation index value for each electrode, determining one or more electrodes that have a modulation index value above a threshold level observed during ictal activity, and using the determined one or more electrodes, to identify one or more possible regions of interest corresponding to seizure zones of a subject's brain.

Apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing electrodes or other sensors placed proximate to portions of the body below the head to develop raw signals without physically touching the body and penetrating hair and clothing. Additionally, apparatus and methods for monitoring electrical activity within the brain of a person (“brainwaves”) employing non-contacting sensors placed proximate to portions of the head to develop raw signals. The raw signals are filtered to produce analysis signals including frequency components relevant to brain electrical activity while attenuating unrelated frequency components. The apparatus and methods can be used for biofeedback-based attention training, human performance training, gaming, biometrics, cognitive state detection, and relaxation training. Either wired or wireless signal connections are made to electronic circuitry, typically including a digital computer, for performing signal processing and analysis functions.

A method for storing data of photoelectrically synchronous brain activity recording, said method comprising: generating data when a photoelectrically synchronous brain activity detection system is operating; generating from said data a data storage file comprising a basic information data segment, a near-infrared spectrum data segment and a brain electrical activity data segment, and sequentially storing said data segments into a .neg file in binary form according to the above order. The method can store comprehensive test information, flexibly configure the near-infrared and brain electrical measurement information, and realize synchronous storage of near-infrared data and brain electrical data and maintain file version compatibility.

Described are novel methods for the diagnosis of specific mental disorders using neurometrics. EEG parameters are compared to thresholds to determine if a person is suffering from autism spectrum disorder, Alzheimer's disease, anxiety, depression, or schizophrenia.

A method for processing EEG signals includes reading the EEG signals from two frontal electrodes of an electroencephalograph (301); converting the EEG signals to a frequency domain (305); determining values of a BIS/BAS response on the basis of an asymmetry between the EEG signals (208). The method includes calculating the asymmetry between the EEG signals in the frequency domain in a frequency range from 26 to 29 Hz.