A determination system includes a head electrode that is located on a left head portion of a user when an intention of the user to move a portion on a right side of a body of the user is detected and that is located on a right head portion of the user when an intention of the user to move a portion on a left side of the body of the user is detected; an ear hole electrode that is located in an ear hole of the user; an electroencephalogram signal measurer that obtains a voltage between the head electrode and the ear hole electrode; and a determiner that determines whether or not a change in the voltage includes the intention to move the portion on the right side of the body or the intention to move the portion on the left side of the body.

Pulse waves and ballistocardiac waves propagate to a piezoelectric vibration sensor 110 attached to a seating surface 102 of a chair 100, and are output after being converted to electrical signals. Thereafter, the electrical signals are filtered by a low-pass digital filter 202P and a band-pass digital filter 202B, with the pulse waves GP output from the low-pass digital filters 202P and the ballistocardiac waves GB output from the band-pass digital filter 202B. These pulse waves GP and ballistocardiac waves GB are processed by absolute value circuits 212P, 212B and low-pass filters 214P, 214B of envelope-processing circuits 210P, 210B, to obtain envelopes. Then, pulse wave propagation velocity PWV is calculated based on the difference between the peaks of the obtained envelopes. The pulse waves/their velocity can be detected/obtained even when the piezoelectric vibration sensor is not attached directly to the human body.

Methods for detecting a seizure, by use of a wavelet transform maximum modulus (WTMM) algorithm applied to body data. A non-transitive, computer-readable storage device for storing data that when executed by a processor, perform such a method.

A method of constructing a value representative of an interaction between a plurality of brain regions. The method is implemented by an electronic device, which includes a processor and a memory. The method includes: obtaining, in the form of connectivity matrices, dynamic functional networks, which are representative of electrical signals measured for a predetermined number of points of interest, called nodes, within a cerebral cortex during a given time period; determining, from at least one of said connectivity matrices, a global efficiency score and at least one clustering coefficient score for each node of said at least one of said connectivity matrices; and calculating a value representative of an interaction between the plurality of brain networks in the form of a distribution ratio using said global efficiency score and said clustering coefficient scores.

A convenient device control apparatus for a vehicle and a method thereof are provided. The convenient device control apparatus includes a light source that emits light at a specified frequency and an electroencephalogram (EEG) sensor that measures an EEG signal of a user. An EEG analyzer analyzes a frequency of the EEG signal measured by the EEG sensor and a controller operates a convenient device in the vehicle based on the frequency of the EEG signal analyzed by the EEG analyzer.

System and method for evaluating a cognitive load on a user, corresponding to a stimulus is disclosed. Electroencephalogram (EEG) data corresponding to the stimulus of a user is received. The stimulus corresponds to a mental task performed by the user. The EEG data is split into a plurality of slots. A slot of the plurality of slots comprises a subset of the EEG data. One or more EEG features are extracted from the subset of the EEG data. The one or more EEG features are represented in one of a frequency domain and a time domain. A plurality of data points present in the one or more EEG features is grouped into two or more clusters using an unsupervised learning technique. The two or more clusters comprise one or more data points of the plurality of data points. The one or more data points correspond to a level of the cognitive load.

Described are novel systems 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 and system for assessing a mental fitness state of a person using objective physiologic signals from the person includes arranging one or more sensors in proximity to the person's head to measure at least one electroencephalogram (EEG) signal of the person, and analyzing the at least one EEG signal by computing a power in the EEG signal in an EEG signal band; determining a characteristic of the computed power that, for the EEG signal band, is indicative of an emotional or cognitive state of the person; and assessing a mental fitness state of the person based on the determined characteristic. An assessment output is generated which reports the assessed mental fitness state of the person. In at least one implementation, the EEG signal contains oscillating theta waves, and a variance in timing of maximum power occurring in the theta waves around a determined average latency is calculated.

Described are methods, devices, and systems for a novel, inexpensive, easy to use therapy for a number of disorders. Described are methods and devices to treat disorders that involves no medication. Methods and devices described herein use alternating magnetic fields to gently tune the brain and affect mood, focus, and cognition of subjects.

A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.