A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.

Provided is an earphone in which electroencephalogram electrodes come into close contact more readily when worn. An earphone includes a housing having elasticity on at least one end portion side outer layer, a speaker accommodated inside the housing, and an eartip that is fixed on the end portion side of the housing having the elasticity, and that includes a sound conduit portion through which sound from the speaker passes, and an elastic electrode that performs sensing of an electroencephalogram of a wearer.

Provided is an earphone in which electroencephalogram electrodes come into close contact more readily when worn. An earphone includes a housing having elasticity on at least one end portion side outer layer, a speaker accommodated inside the housing, and an eartip that is fixed on the end portion side of the housing having the elasticity, and that includes a sound conduit portion through which sound from the speaker passes, and an elastic electrode that performs sensing of an electroencephalogram of a wearer.

The present invention relates to systems and methods for planning and/or providing neuromodulation. An example system includes a first data input module for stimulation related basic data, a second data input module for stimulation related response data, a transfer module configured and arranged such that the stimulation related basic data received by the data input module are linked and/or translated into and/or with the response data and/or artificial response data created by the transfer module, wherein the data generated by the transfer module are transfer data, the transfer data comprising link data and/or translation data and/or artificial response data, and a mapping module configured and arranged such that based on the stimulation related basic data and stimulation related response data and the transfer data a digital characteristic map is generated, which describes an interrelation between the stimulation related basic data and the stimulation related response data and the transfer data.

The present invention is a human centric lighting (HCL) method with adjustable lighting parameters comprises the following steps: 1) User connects with the cloud through the intelligent communication device and selects the specific spectral recipe that the user wants to achieve a specific emotion from the cloud; 2) The light emitting device is configured to emit light in a specific light field according to the lighting parameters in a specific light field to select the light in a specific light field; 3) After the user performs HCL, when the effect of specific emotion is not reached, the user adjusts the lighting parameters in the selected specific spectral recipe through the intelligent communication device; 4) When the user has achieved the effect of specific emotion after performing HCL, store the corresponding lighting parameters of the adjusted spectral recipe to the cloud.

Proposed is system and method for determining a driver's fatigue to promote safe driving by determining the driver's fatigue according to a change in a brain wave of a driver during driving of a vehicle.

The process for classifying anesthetic depth includes: collecting of biological signals, conditioning of said signals, monitoring of activity of the central and autonomic systems, measurement of indexes and classification of patterns in anesthetic depth. The activity includes: i) Awake: Vigil—Ak. and recovery of verbal response—Rc. ii) Light Anesthesia: Light induction anesthesia—Li. Light recovery—Lr, Light dose, increase in drugs or patient movement (La), iii) General anesthesia: General anesthesia—Ga, one minute after the start of the surgery, and iv) Deep anesthesia: identification of the EEG burst-suppression pattern (BSP) associated with deep anesthesia.

A method for performing spatial filtering and augmenting an electroencephalogram (EEG) signal is provided. A processor constructs a spatial filter based on channel information of the EEG signal. The processor augments the EEG signal with the spatial filter. A related electronic device and a related non-transitory computer-readable storage medium are provided.

A method for generating for a subject a personalized playlist of sounds, notably songs, using the analysis of the subject's electroencephalographic signal, the method including the following steps: receiving at least one segment of electroencephalographic signal acquired from at least one electrode while the subject is listening to at least one proposed sound; extracting at least one EEG index from the electroencephalographic signal segment so as to characterize brain patterns correlated to the emotions evoked by the music and the level of appreciation of the sound; evaluating the valence of sounds listening on the basis of the EEG index; receiving a score of appreciation on the sound from the subject; including the sound in the personalized playlist (P) of the subject whenever the valence matches a predefined valence and/or the score of appreciation is higher than a predefined threshold.

A method for generating for a subject a personalized playlist of sounds, notably songs, using the analysis of the subject's electroencephalographic signal, the method including the following steps: receiving at least one segment of electroencephalographic signal acquired from at least one electrode while the subject is listening to at least one proposed sound; extracting at least one EEG index from the electroencephalographic signal segment so as to characterize brain patterns correlated to the emotions evoked by the music and the level of appreciation of the sound; evaluating the valence of sounds listening on the basis of the EEG index; receiving a score of appreciation on the sound from the subject; including the sound in the personalized playlist (P) of the subject whenever the valence matches a predefined valence and/or the score of appreciation is higher than a predefined threshold.