121 results about "Cerebral activity" patented technology

A mental state awareness system comprises a non-invasive brain interface assembly configured for detecting brain activity of a user, a processor configured for determining a mental state of a user based on the detected brain activity, and a biofeedback device configured for automatically providing biofeedback to the user indicative of the determined mental state of the user.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

The invention provides a method of estimating cerebral sources of electrical activity from a small subset of EEG channels utilizing existing methods to provide a 3-dimensional, discrete, distributed, linear solution to the inverse problem using inputs consisting of a small number of EEG channels (e.g., 4 channels) augmented with synthetic EEG data for the other channels. The resultant image of cerebral electrical activity in the region of the EEG channels from which data is recorded is of comparable spatial resolution in the corresponding region to images of cerebral electrical activity obtained using a complete set of EEG channels (e.g., using 24 channels).

A method and automated system for assisting in the prognosis of the progress and for assisting in the diagnosis of Alzheimer's disease in patients suffering from mild cognitive impairment (MCI). Also provided is a method for training such a system related to identifying discriminant regions of the brain and using these regions to fine tune the assistance method, based on new known cases.

Imaging data (PET or SPECT) is used, representing the cerebral activity in a plurality of spatial zones (voxels). The method then includes a normalization processing of the image data, and analysis of the cerebral activity values read in a selection of voxels forming at least one predetermined discriminant region, defined by its coordinates within a spatial reference system.

The invention discloses an emotional control system based on electroencephalogram feedback, comprising an information acquisition module, a signal processing control module, a feedback module, a human-machine interface module, an evaluation module and a data management module. The invention relates to the field of emotional control and electrophysiology, and the ability to control emotions is trained by using electroencephalogram biofeedback technology; since the brain is plastic, electroencephalograms may reflect transient changes in cerebral activity; the ability of the brain to control emotions is trained through biofeedback technology, it is possible to improve the ability of a subject to control emotions, and by using an electroencephalogram biofeedback system herein, it is possible to effectively improve the ability of personnel to adjust negative emotions.

A method of identifying a spatial distribution of areas includes: a first step of determining a rough spatial distribution of at least one area in a sensory area of cerebral cortex in each of a left hemisphere and right hemisphere of the brain that is a target area of the identification; a second step of applying a sensory stimulus to the identification target area; a third step of obtaining brain activity information; and a fourth step of using the brain activity information to calculate one of a coefficient of cross-correlation, and coherence, between brain activity information of areas, assessing synchrony between the left and right hemispheres, and changing the spatial distribution determined in the first step.

The invention discloses a resting-state functional magnetic resonance data processing method based on primary epilepsy, which belongs to the technical field of data processing for disease detection, and comprises the following steps: S1. data collection; S2. data preprocessing; S3 .Data analysis: including ReHo data analysis, ALFF data analysis and FC data analysis, all ReHo, ALFF, FC maps are analyzed separately, and then aggregated to determine the changed brain regions. The data processing method of the present invention is extremely valuable for the exploration of multifocal epileptic lesions, avoids the difference in brain activity caused by complex task stimuli, and basically controls the experimental conditions of different subjects at the same level, and is easy to cooperate and more It is easy to draw reliable conclusions. It understands the pathogenesis of certain clinical symptoms of epileptic patients from the perspective of brain function, can help locate epileptic foci and surrounding functional areas, and guide epilepsy surgery and the resection range of epileptic foci.

The invention relates to the field of brainwave signal processing, in particular to a frequency domain feature extracting algorithm applied to single-lead portable brainwave equipment. The algorithm is applicable feature extraction of the single-lead portable brainwave equipment and capable of reflecting cognition states. The algorithm includes: preprocessing, and feature expressing, extracting and indexation indicating. The algorithm outputs an alertness level index and a nervousness level index. The alertness level index: S1(t)=c(t)/a(t), wherein t refers to time, and a and c respectively refer to the energy of alpha and theta; the nervousness level index: S2(t)=b()t*c(t), wherein t refers to time, and b and c respectively refer to the energy of beta and theta. Compared with judgments totally depending on experiences and subjectivity in the prior art, the algorithm has the advantages that the mental state of a testee can be judged scientifically and objectively, the multi-index synergy degree indexes reflecting the optimal working states can be further extracted, and whether brain activity states is suitable for work or not can be comprehensively expressed.

A harmonization system for a brain activity classifier harmonizing brain measurement data obtained at a plurality of sites to realize a discrimination process based on brain functional imaging: obtains data, for a plurality of traveling subjects as common objects of measurements at each of the plurality of measurement sites, resulting from measurements of brain activities of a predetermined plurality of brain regions of each of the traveling subjects; calculates, for each of the traveling subjects, prescribed elements of a brain functional connectivity matrix representing the temporal correlation of brain activities of a set of the plurality of brain regions; using a generalized linear mixed model, calculates measurement bias data 3108 for each element of the brain functional connectivity matrix, as a fixed effect at each measurement site with respect to an average of the corresponding element across the plurality of measurement sites and across the plurality of traveling subjects; and thereby executes a harmonizing process.

A brain network activity estimation system includes means for obtaining brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, means for constructing a feature estimation model which receives the brain wave measurement data as input data and determining a parameter which defines the feature estimation model, means for calculating a feature value for each module based on an output value from each module that is calculated when the brain wave measurement data is provided as the input data, means for calculating an image feature value for each brain network based on the functional magnetic resonance imaging measurement data, and means for determining one or more modules which express activities of a specific brain network among a plurality of modules by evaluating correlation between the feature value for each module and the image feature value for each brain network.

A device for measuring and/or stimulating a brain activity, preferably an EEG device, comprising means for transmitting and/or detecting physiological signals produced by the brain of an individual, and a support for the transmission and/or detection means, wherein the support is configured to extend over the top of the individual's head, the support comprising means for removably attaching to an accessory intended to be worn by the individual, on his or her head, such as an audio headset, the support being configured such that, when the device is worn by the individual, the means for transmitting and/or detecting physiological signals are held in substantially close contact with the individual's head by the accessory.

Neurofeedback training executed by a brain activity training apparatus for performing a training to change the correlation of connectivity between brain areas utilizing correlation of measured connectivity between brain areas involves repetition of a plurality of trials. Each trial includes a resting period T_rest, a self-regulation period T_NF and a presenting period T_Score presenting feedback information. The brain activity training apparatus calculates, from signals detected from a trainee in the resting period by a brain activity detecting device, a baseline level of degrees of activity of prescribed regions corresponding to the functional connectivity as the object of training, calculates, from signals detected in the self-regulation period and from the baseline level, time correlation of degrees of activity of the prescribed regions corresponding to the functional connectivity as the object of training, and calculates information to be fedback.

[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.

The invention provides for a medical instrument (100, 400, 500, 600) comprising an activity measurement system (106) configured for measuring brain activity data (138) from a subject (102). The medical instrument further comprises a stimulus presentation system (108) configured for providing sensory stimulus to the subject. The medical instrument further comprises a memory (130) for storing machine executable instructions (132) and for storing a stimulus reinforcer database (134). The stimulus reinforcer database comprises entries. Each entry comprises commands configured for controlling the stimulus presentation system to provide the sensory stimulus to the subject. The medical instrument further comprises a processor (120) for controlling the medical instrument. Execution of the machine executable instructions causes the processor to: control (200) the stimulus presentation system with a set of entries (136) selected from the stimulus reinforcer database to repeatedly provide sensory stimulus to the subject; control (202) the activity measurement system for performing the measurement of the brain activity data during each sensor stimulus; select (204) a chosen entry (140) from the set of entries using the brain activity data; and store (206) the chosen entry in the memory.

An ear worn brainwave brain activity dual phone communication device that works independently for enabling human beings to communicate with their mind, move objects and machines by way of their monitored brain activity. The communication system is sufficiently independent to initiate and receive communication without requiring a mobile phone, since contact information is stored in the communication device. The communication system performs functions with an audible command or brainwave brain activity such as initiating communication, accessing content, moving and controlling other machines and computer devices, accessing tools.

This brain activity classifier harmonization system, for harmonizing brain measurement data acquired from a plurality of institutions and achieving discrimination processing by brain function imaging, acquires, with respect to a plurality of moving examination who are under common measurement at each of a plurality of measurement sites, data from the results of measuring brain activities in a predetermined plurality of brain regions of each of the moving examination subjects, calculates, for each of the moving examination subjects, predetermined elements of a brain functional connectivity matrix representing the time correlation of brain activity for a set of the plurality of brain regions, uses a generalized linear mixed model method to calculate, for each of the predetermined elements of the functional connectivity matrix, measurement bias data (3108) serving in each of the measurement sites as a fixed effect from the mean of the elements of the plurality of measurement sites and the plurality of moving examination subjects, and executes harmonization processing.

[Object] An object is to provide a brain activity analyzing method for realizing a biomarker using brain function imaging for neurological/mental disorders.

[Solution] From data of resting-state functional connectivity MRI obtained by measuring groups of healthy subjects and patients, a correlation matrix of degrees of activities among prescribed brain regions is derived. By a sparse canonical correlation analysis (SCCA) of attributes of subjects and the correlation matrix, elements of correlation matrix that connect to canonical variables corresponding only to the diagnosis label are extracted. By sparse logistic regression (SLR) during Leave-One-Out Cross Validation of a first sum-set of elements of correlation matrix obtained as a result of feature extraction by a sparse regularized canonical correlation analysis, a second sum-set of elements of the correlation matrix is extracted. By discrimination analysis using sparse logistic regression on the second sum-set, a discriminator is generated.