Computer-implemented methods and automated systems for real-time detection of spreading depolarizations in a brain injured patient, based an algorithm of (a) providing a reference data base of spreading depolarization waveform templates generated from EEG recordings of confirmed spreading depolarizations (SD) in a reference brain-injured patient cohort; (b) recording an EEG of the brain injured patient to generate recorded EEG waveforms; (c) detecting a slow potential change present in a recorded EEG waveform by applying a power spectral density estimate to the recorded waveform; (d) comparing a detected SPC to a reference database of SD waveform template to identify a candidate SD; and (e) rejecting a candidate SD as a false positive based on overall signal power and amplitude analysis and identifying a non-rejected candidate SD as a detected SD.

A device and method for user authentication. The device for authentication includes an extraction unit configured to extract a signal feature of a brainwave signal of a user to be authenticated and a comparison unit configured to compare the signal feature with a signal feature sample pre-stored in a feature library on an individual basis. When there a signal feature sample is matched with the signal feature, the device retrieves account information and a password of the user according to the matched signal feature sample. The device for authentication further includes a response unit configured to respond to a request of the user according to the account information and the password. The present disclosure can improve the security and convenience of user authentication.

A functional network analysis system for complex network provided by the present application, includea multi-signal-source measurement unit, a signal decomposition unitand a cross-frequency coupling analysis unit, wherein the multi-signal-source measurement unit, the signal decomposition unitand the cross-frequency coupling analysis unit are connected successively. A method using the functional network analysis system is more suitable for the decomposition of non-linear and non-stationary data in complex networks, and can reflect the dynamic properties of functional network links between different frequency bands in a complex system.

An electroencephalogram measurement system includes an acquisition unit, a decision unit, and an output unit. The acquisition unit acquires electroencephalogram information representing an electroencephalogram obtained by an electrode unit placed on a region of interest that forms part of a subject's head. The decision unit makes a decision, based on the electroencephalogram information acquired by the acquisition unit, whether or not there are any artifacts. The output unit outputs the decision made by the decision unit.

A system for non-invasively measuring a physiologic status patient tissue according to an embodiment includes a housing; an optical spectroscope with a wavelength-sensitive sensor capable of detecting light intensity, at two or more distinct wavelengths, of light scattered and/or reflected by muscle tissue of the patient in order to measure a physiologic status of the muscle tissue, a bioimpedance sensor at least partially disposed within the housing, the bioimpedance sensor configured to generate a signal corresponding to an estimation of a thickness of an adipose tissue layer between the optical spectroscope and the muscle tissue, and a processor configured to (1) receive the estimations of the thickness, (2) compare the estimations of the thickness, and (3) based on the comparison, generate a visual indication configured to assist in placement of the housing toward or at a location corresponding to the smaller or smallest thickness of the adipose tissue layer.

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.

An anesthesia stage identification method for identifying an anesthesia stage at which a patient is located is disclosed. The method includes collecting an electroencephalogram signal, calculating at least two characteristics of the collected electroencephalogram signal according to a preset frequency, and determining the anesthesia stage at which the patient is located in a corresponding time period according to the at least two calculated characteristics. The identification method can accurately determine the anesthesia stage, and resolve the problems of abnormal falling during a lucid interval and slow response speed during an induction stage caused by misjudgment.

Described are methods and a device for determining a valid intrinsic alpha frequency. Described herein are methods and a device for determining the appropriate intrinsic alpha frequency (IAF) to be applied for neuro-EEG synchronization therapy using alternating magnetic fields to gently tune the brain and affect the mood, focus and cognition of subjects. Methods and a device described herein use an algorithm to quantitatively analyze EEG recordings to determine if recorded EEG frequencies are valid, and if necessary, requiring additional recordings and analysis until a valid EEG is found. Methods and devices described herein can be utilized to calculate the intrinsic frequency of other EEG bands, including the Theta, Beta Gamma and Delta bands.

Provided in the embodiments of the present disclosure are a control method and device based on brain signal, and a human-machine interaction device, which periodically acquire EEG signals and cerebral oxygen signals within a target period, generate an electroencephalogram (EEG) wave curve representing changes of the EEG signals and a cerebral oxygen wave curve representing changes of the cerebral oxygen signals respectively within the target period, determine whether the EEG wave curve and the cerebral oxygen wave curve satisfy a condition for controlling a controlled device to perform a target operation, and control the controlled device to perform the target operation when the EEG wave curve and the cerebral oxygen wave curve satisfy the condition.

Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.