A monitoring apparatus includes a wearable electronic device having an audio port and a headset having at least one earbud, at least one physiological and/or environmental sensor, and circuitry that processes signals produced by the at least one physiological and/or environmental sensor and transmits the processed signals to the electronic device via the audio port. The headset may include a microphone in audio communication with the electronic device via the audio port, and the circuitry modulates audio signals produced by the microphone and signals produced by the at least one physiological and/or environmental sensor for transmission to the electronic device via the audio port. The circuitry may power the at least one physiological and/or environmental sensor via power supplied by the electronic device through the audio port and may include a processor that coordinates collection, modulation, and/or transmission of signals produced by the at least one physiological and/or environmental sensor.

A method and system for monitoring impairment indicators. The method includes, during a first time window, measuring a first movement signal related to movement of the person with a movement sensor associated with the person, and measuring a first biological signal of the person with a biological sensor attached to the person. The method further includes electronically storing at least one numerical descriptor derived from the first movement signal and at least one numerical descriptor derived from the first biological signal as reference data for the person. The method includes during a second time window, measuring a second signal related to movement of the person with the movement sensor, and measuring a second biological signal of the person with the biological sensor. The method further includes comparing at least one numerical descriptor derived from the second signal and at least one numerical descriptor derived from the second biological signal to the reference data to identify an impairment indicator.

[Problem]

It is intended to quantitatively evaluate deterioration in brain function associated with a disease such as dementia, with a high degree of accuracy by a simplified method using signals acquired from a few sensors arranged on the scalp.

[Solution]

The present invention relates to a brain activity measurement device comprising: a signal acquisition part configured to acquire a signal from a brain of the subject, using three sensors attached to different locations on the surface of the head of a subject; a data extraction part configured to extract, from each of the three signals acquired from respective ones of the sensors, a deep-brain potential signal having a specific frequency band arising from an activity of a deep brain region, and acquire data from the extracted deep-brain potential signal with a sampling period; a correlation value calculation part configured to calculate a correlation value indicative of a correlative relationship among the deep-brain potential signals acquired from each respective sensors, based on a phase relationship among three pieces of time-series data each extracted from the respective sensors by the data extraction part; and an index value calculation part configured to analyze the deep-brain potential signals from the deep brain region, based on the calculated correlation value, to calculate an index value for determining a brain function.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spiking, and power changes in the sensed signals, etc. A detection tool may be selected and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.

The invention concerns a method of analysing the brain activity of a patient performing a given task or in response to an external stimulus, by comparison of standardized data with data in a database, by means of fuzzy logic algorithms.

A transmission device can be carried by the subject. A biological signal recording device can perform wireless communication with the transmission device. A transmitter transmits biological signal data corresponding to a biological signal of a subject. A storage stores the biological signal data. A receiver receives the biological signal data. A recorder records the biological signal data received by the receiver. A detector detects a missing portion in the biological signal data recorded by the recorder or a receipt of the biological signal data by the receiver. A notifier notifies the missing portion or transmits an acknowledgment of the receipt to the transmission device. A complementary transmitter retrieves biological signal data corresponding to the notified missing portion from the storage or identifies unreceived biological signal data and retrieves the identified biological signal data, and transmits the retrieved biological signal data. A complementary recorder records the biological signal data transmitted by the complementary transmitter.

The present invention discloses a depression assessment system based on physiological information, comprising an information acquisition module, a signal processing module, a parameters calculation module, a feature selection module, a machine learning module and an output result module. The present invention further discloses a depression assessment method based on various physiological information, comprising the following steps: 1, processing electrocardiogram (ECG) signal and one or more of photoplethysmography (PPG) signal, electroencephalogram (EEG) signal, galvanic skin response (GSR)signal, electrogastrography (EGG) signal, electromyogram (EMG) signal, electrooculogram (EOG) signal, polysomnogram (PSG) signal and temperature signal, and calculating signal parameters; 2, normalizing the obtained signal parameters, and performing the feature selection on parameters set formed by the normalized signal parameters to obtain feature parameters set; and 3, performing machine learning by utilizing the obtained feature parameters set, and establishing a depression assessment mathematic model to assess the depression level by utilizing a relationship between the feature parameters set and the depression level. The present invention has the advantage that the subjectivity of the assessment by utilizing the depression rating scale can be avoided.

The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.

A high CMRR neural signal amplifier is configured for supply rail common mode feedback (SR-CMFB) whereby a set of CMFB signals is provided to supply rails of front end LNAs. High CMRR is maintained through buffering outputs of front end signal LNAs and a reference LNA coupled to signal and reference inputs of second stage amplifiers, respectively; and buffering the reference LNA output using an active/guard buffer pair, whereby across a plurality of distinct multiplexing time intervals, during each multiplexing time interval one buffer of the pair functions as an active buffer that drives second stage amplifier reference inputs corresponding to second stage amplifier outputs being multiplexed to a set of multiplexor outputs, and the other buffer of the pair functions as a guard buffer coupled to other second stage amplifier reference inputs corresponding to second stage amplifier outputs not being multiplexed to the set of multiplexor outputs.

The present invention discloses a method and system for authentication that is coercion resistant by using music that invokes reproducible neurological responses uniquely to a user in order to authenticate the user. The neurological responses may include neurochemical activities inside the brain and brain waves. The unique neurological response of a user to his or her selected music is stimulated by neurochemical release such as dopamine. During the most pleasing part of the music, the user's neurochemical activities and brain waves notably change, which can be captured by using sensors. The timing, rate and extent of the notable change in neurological responses can be used to form a user's unique and non-transferable password. The present invention can be used to authenticate a user while preventing against standard attacks as well as coercion attack, i.e., forcing the user to provide his or her authentication material such as a password.