The present invention discloses a real-time evaluation method and evaluation system for group emotion homogeneity. The method comprises the steps as follows: enabling testees to be in the same emotion induction environment, and collecting the original electroencephalograph (EEG) signals of multiple persons at the same time through online multichannel EEG equipment; and based on the average instantaneous phase per second of the beta frequency band and the energy value per second of the alpha frequency band obtained after wavelet transformation, calculating the time synchronization degree and the valence consistency degree in real time, and finally obtaining a group emotion homogeneity index for the objective evaluation of group emotion homogeneity.

The present invention discloses a real-time evaluation method and evaluation system for group emotion homogeneity. The method comprises the steps as follows: enabling testees to be in the same emotion induction environment, and collecting the original electroencephalograph (EEG) signals of multiple persons at the same time through online multichannel EEG equipment; and based on the average instantaneous phase per second of the beta frequency band and the energy value per second of the alpha frequency band obtained after wavelet transformation, calculating the time synchronization degree and the valence consistency degree in real time, and finally obtaining a group emotion homogeneity index for the objective evaluation of group emotion homogeneity.

The present invention provides systems and methods for detection and diagnosis of concussion and/or acute neurologic injury comprising a portable headwear-based electrode array and computerized control system to automatically and accurately detect cortical spreading depression and acute neurological injury-based peri-infarct depolarization (CSD/PID). The portable headwear-based electrode system is applied to a patient or athlete, and is capable of performing an assessment automatically and with minimal user input. The user display indicates the presence of CSD/PID, gauges its severity and location, and stores the information for future use by medical professionals. The systems and methods of the invention use an instrumented DC-coupled electrode/amplifier array which performs real-time data analysis using unique algorithms to produce a voltage intensity-map revealing the temporally propagating wave depressed voltage across the scalp that originates from a CSD/PID on the brain surface.

Biometric identification using electroencephalogram (EEG) signals is provided. Embodiments are targeted for biometric applications, where an individual can be identified with a precision of over 99%, using sensed brain signals. In particular, a method is described which can extract unique biomarkers from EEG response signals to classify individuals, also referred to as simple visual reaction task-based EEG biometry (SVRTEB). A subject experiences a simple stimulus or task, and a multi-channel EEG response is recorded. Unique biomarkers are extracted from the recorded EEG response (e.g., as periodogram data points corresponding to different frequencies observed in the brain waves, which can be used to identify a person). A novel signal processing approach uses neural network-based architecture to analyze the EEG response and identify the subject. This signal processing architecture can be readily implemented on hardware and provides high accuracy, precision, and recall.

Biometric identification using electroencephalogram (EEG) signals is provided. Embodiments are targeted for biometric applications, where an individual can be identified with a precision of over 99%, using sensed brain signals. In particular, a method is described which can extract unique biomarkers from EEG response signals to classify individuals, also referred to as simple visual reaction task-based EEG biometry (SVRTEB). A subject experiences a simple stimulus or task, and a multi-channel EEG response is recorded. Unique biomarkers are extracted from the recorded EEG response (e.g., as periodogram data points corresponding to different frequencies observed in the brain waves, which can be used to identify a person). A novel signal processing approach uses neural network-based architecture to analyze the EEG response and identify the subject. This signal processing architecture can be readily implemented on hardware and provides high accuracy, precision, and recall.

The present invention includes systems and methods of using real-time neurofeedback to improve the correspondence between first-person experience and specific brain activation patterns in a manner that minimally affects the experience itself. The present invention provides meditators the ability to enhance their control over their own brain activity, such as posterior cingulate cortex (PCC) activation. The present invention also provides methods for treating a disease or disorder of a subject by measuring the subject's brain activity via fMRI, presenting a representation of the measured brain activity to the subject, and instructing the subject to reduce the represented brain activity by altering their meditative state. The present invention also provides a system and method of using fMRI neurofeedback to directly correlate subjective experience with neural activation.

According to one embodiment, a speech imagery recognition device is configured to recognize speech from electroencephalogram (EEG) signals during speech imagery. The speech imagery recognition device comprises an analysis processor and an extractor. The analysis processor is configured to analyze discrete signals, which are obtained from EEG signals received from a plurality of electrodes, for each of the electrodes and output a spectral time sequence. The extractor is configured to obtain eigenvectors for each phoneme from the spectral time sequence and output a phoneme-feature vector time sequence based on the eigenvectors.

According to one embodiment, a speech imagery recognition device is configured to recognize speech from electroencephalogram (EEG) signals during speech imagery. The speech imagery recognition device comprises an analysis processor and an extractor. The analysis processor is configured to analyze discrete signals, which are obtained from EEG signals received from a plurality of electrodes, for each of the electrodes and output a spectral time sequence. The extractor is configured to obtain eigenvectors for each phoneme from the spectral time sequence and output a phoneme-feature vector time sequence based on the eigenvectors.

A computer-implemented process for measuring brain activity of a subject, including: receiving electroencephalogram (EEG) data representing EEG measurements of electrical brain activity of the subject; and fitting parameters of a neural population model to the EEG data to determine corresponding values of said parameters, including one or more values of an inhibitory postsynaptic potential rate constant (γ.sub.i), where γ.sub.i is representative of states of brain responsiveness, an increase in γ.sub.i relative to a reference value indicating an enhanced state of brain responsiveness, and a decrease in γ.sub.i relative to the reference value indicating a decrease in the state of brain responsiveness.

A computer-implemented process for measuring brain activity of a subject, including: receiving electroencephalogram (EEG) data representing EEG measurements of electrical brain activity of the subject; and fitting parameters of a neural population model to the EEG data to determine corresponding values of said parameters, including one or more values of an inhibitory postsynaptic potential rate constant (γ.sub.i), where γ.sub.i is representative of states of brain responsiveness, an increase in γ.sub.i relative to a reference value indicating an enhanced state of brain responsiveness, and a decrease in γ.sub.i relative to the reference value indicating a decrease in the state of brain responsiveness.