30 results about "Brain electrical activity" patented technology

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

Methods, systems, and devices are described for reconstructing spatially resolved electrical activity in the brain. In some example embodiments, EEG and MRI data are used to estimate volumetric distribution of electrostatic potential inside the MRI domain throughout the entire brain. Spatially and temporally varying field estimates can be generated using a brain wave model which is based on weakly evanescent transverse cortical wave propagation and constrained using the tissue properties gained from the MRI data. The disclosed techniques enable brain activity imaging with high spatial and temporal resolution, thereby providing a tool for assessing functional brain states and monitoring changes in those states in relation to various normal and pathological conditions.

The invention discloses a brain wave monitoring and intelligent feedback combined modularized head ring glasses device, and relates to the technical field of brain wave sensing and adjusting equipment.According to the device, brain electrical activities, eye movement behaviors, eye fatigue states, wearing fitness of the head ring glasses device, environmental noise interference and use situations of a user are monitored in real time; the brain wave fitting quality index is calculated through the pressure variance, the EEG signal-to-noise ratio and the historical template fitting degree, and self-adaptive compensation adjustment is carried out when the brain wave fitting quality index is unqualified; combining electroencephalogram and eye movement characteristics to calculate a brain-eye collaborative concentration index, and generating a comprehensive adjustment strategy when the brain-eye collaborative concentration index does not reach the standard; and calculating an emotional scene switching index according to the brain wave emotional characteristics, the situation parameters and the historical trend, and triggering personalized emotional regulation when the emotional scene switching index is not matched with the situation parameters. The device can quantify the brain wave and behavior state of the user in real time, realizes intelligent concentration and emotion management, and improves the reliability and individual suitability of electroencephalogram monitoring.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

The Neuromorphic Emulation Constructor AI (NECA) is an artificial intelligence technology for building a Personal Neuromorphic Emulation (PNE), a detailed model of an individual's brain that is anatomically correct, capable of learning and plasticity, and able to emulate the person's brain electrical activity and behavior. The NECA accesses the neuroscience literature through automated search and acquisition technologies such as Google Scholar, and it assembles the knowledge of the brain's structure and function in ways that specify how to build and test the PNE.

The application discloses an individualized transcranial electrical stimulation system and method based on a multi-scale fusion brain model, which comprises the following modules: a large-scale brain model construction module, which is used for reconstructing an individualized brain function connection matrix by using electroencephalogram (EEG) data, then constructing an individualized large-scale brain model based on an average field model, and equivalent transcranial electrical stimulation to an external current input of the large-scale brain model, and constructing a large-scale brain model under the action of external stimulation; a microscopic neural circuit dynamics model construction module, which is used for fusing single neuron dynamics and synaptic plasticity, adopting a coupled leaky integrate-and-fire model and a spike-timing-dependent plasticity model to establish long-term plasticity between synapses, and then establishing a local neural circuit model through a stimulation target and downstream brain areas; an individualized multi-scale fusion brain model construction module, which is used for replacing corresponding nodes in the large-scale brain model with the local neural circuit model, and constructing an individualized multi-scale fusion brain model; and a stimulation response simulation module, which is used for adjusting transcranial electrical stimulation parameters by using the constructed individualized multi-scale fusion brain model, and obtaining EEG activity and short-term response and long-term effect. The application can more accurately describe the complexity of brain activity by establishing an individualized multi-scale fusion brain model.

The invention relates to the technical field of measuring and positioning devices, and discloses an electroencephalograph measuring and positioning device which comprises a head-mounted connecting sleeve, first plastic mounting sleeves are fixed to the two ends of the head-mounted connecting sleeve, and second plastic mounting sleeves are rotationally connected to the outer sides of the first plastic mounting sleeves through rotating shafts; the device is provided with a fastening connection device, the positions of an elastic band of a positioning module and a positioning installation rod are conveniently fixed, the situation that an anti-skid connecting sleeve is prone to disengagement after being inserted into a positioning installation hole is avoided, and after position adjustment, measurement and positioning of the electroencephalograph are facilitated; the electroencephalograph is helped to record and position the brain electrical activity position more accurately; the device is provided with the head adjusting device, the purpose that the wearing size is convenient to adjust is achieved, the device can meet the wearing requirements of users with different head circumferences, the comfort degree of different wearers is improved, and measurement and positioning of the electroencephalograph are facilitated.

The application discloses a closed-loop transcranial electrical stimulation method, device, equipment and storage medium, and comprises the following steps of brain electrical signal acquisition, autoregressive model construction and prediction, phase extraction, phase difference calculation and adjustment, AM signal generation and TIS stimulation signal generation. The method can predict future brain electrical signals by using an autoregressive model, and combines a phase extraction technology to ensure that an electrical stimulation signal is synchronized with a specific phase of target brain electrical activity, the generated stimulation signal is used for nerve regulation in a TIS mode, the overlap of the stimulation signal interference frequency band and the brain electrical signal spectrum is effectively avoided, and therefore the space-time accuracy and individualized adjustment capability of nerve regulation are greatly improved. Moreover, the TIS signal can noninvasively regulate deep brain areas, and has better brain area stimulation effect. The application relates to nerve regulation technology.

The invention provides an electroencephalogram interpretation method based on Transformer of multi-granularity combination. The electroencephalogram interpretation method comprises the following steps: step 1, obtaining a differential entropy feature xe from an original electroencephalogram signal; step 2, using a spatial feature extraction Transform to obtain spatial features of the electroencephalogram region; 3, extracting time features of the electroencephalogram signals by using a shallow layer time feature extraction module; 4, extracting fine-grained time features by using a fine-grained convolution module; 5, optimizing the generated multi-level information by using self-attention (SA) and guidance attention (GA) to obtain an emotion label corresponding to the electroencephalogram activity; according to the method, the extraction of fine granularity and the utilization of the spatial features of the electrodes are remarkably improved, and by adding spatial feature extraction and time feature extraction operations, the algorithm efficiency is improved, the problems of insufficient utilization of the spatial features and the like are effectively solved, and the decoding accuracy is ensured.

One embodiment of the present invention generates and presents, to a user, presentation information representing a movement image associated with a body part of the user, and during the presentation period of this presentation information, acquires brain activity data representing the user's brain activity, as well as acquiring body movement data representing the movement of the body part of the user. The embodiment then obtains information representing the degree of progress of the user's training, and on the basis of this information representing the degree of progress, adjusts and changes the content of the presentation information from first content in which the user is prompted to recall the movement image in the user's brain while moving the body part, to second content in which the user is prompted to recall the movement image in the user's brain without moving the body part.

The invention discloses an intelligent magnetic pulse sleep-aiding system with a self-adaptive adjustment function. The intelligent magnetic pulse sleep-aiding system comprises sleep physiological signal acquisition equipment, an intelligent processing terminal and multifunctional insomnia treatment equipment, the intelligent processing terminal comprises a data transceiving module, a data processing module, a model analysis module and an intervention adjustment module; the sleep physiological signal acquisition device is a portable wearable acquisition device. According to the intelligent magnetic pulse sleep aiding system with the self-adaptive adjustment function, personalized magnetic pulse treatment schemes and feedback can be provided by monitoring and adjusting the neural activity mode of the brain according to the real-time brain electrical activity condition of a user, the biological rhythm and the sleep mode of a patient can be adjusted, and therefore the sleep quality is improved, and the sleep aiding effect is improved. And therefore, the treatment requirements of the user can be met more accurately.

The invention discloses a transcranial electrical stimulation method and device, and the method comprises the following steps: S1, collecting functional magnetic resonance imaging, carrying out the preprocessing, selecting a region of interest, extracting an average value of all voxel time sequences in the region of interest, obtaining a seed time sequence of the region of interest, and carrying out the calculation of the average value; calculating a Pearson correlation coefficient between the seed time sequence and other voxel time sequences of the whole brain so as to obtain a whole brain function connection diagram; s2, collecting an electroencephalogram signal, and extracting the relative power of a prefrontal lobe delta wave after preprocessing; the functional magnetic resonance imaging provides information with high spatial resolution of cerebral blood flow and metabolism, and the electroencephalogram reflects time sequence characteristics of cerebral electrical activity. The two kinds of data are combined, a target brain area is recognized by analyzing the mode and characteristics of brain activities in the sleep process, and then different brain state changes are triggered according to the transcranial electrical stimulation field intensity, so that the optimal stimulation method is obtained.

The invention relates to a method for automatically processing data on the electrical activity of a brain of a user and comprising the following steps: receiving electrical data reflecting electrical activity of the brain of the user and measured by electroencephalography; in a time window, extracting one or more activity characteristics of the brain from the electrical data; in a time window, normalising said one or more activity characteristics to produce one or more normalised characteristics; generating one or more multi-dimensional characteristic images based on said one or more normalised characteristics; and processing said one or more multi-dimensional characteristic images by means of an algorithm, so as to produce a prediction of a mental state of the user, wherein said steps are repeated with a refresh rate at least equal to 1 Hz, preferably at least equal to 3 Hz, and preferably equal to 10 Hz.

The invention discloses an electroencephalogram awakening degree classification method and system based on multi-period dynamic rhythm characteristics, and relates to the technical field of biomedical engineering. The method comprises the following steps: acquiring an electroencephalogram signal of a testee; segmenting the electroencephalogram signal of each channel by using a plurality of preset time windows to correspondingly obtain electroencephalogram signals of a plurality of time periods; respectively carrying out time-frequency analysis on the electroencephalogram signals of each channel in each time period to obtain a time-frequency spectrum, and extracting dynamic rhythm characteristics based on the time-frequency spectrum; and inputting the dynamic rhythm characteristics into a double-layer heterogeneous decision model, and predicting the electroencephalogram wake-up degree category. According to the method, the dynamic rhythm characteristics of the time-frequency spectrum in each short time period are extracted, and the wake-up state of a single subject is classified by utilizing the dynamic rhythm characteristics and the double-layer heterogeneous decision model and using a small amount of data identification, so that the dynamic response process of the electroencephalogram signal to short-time stimulation can be well captured; and the accuracy of electroencephalogram awakening degree classification is improved.

The present invention relates to wearable smart devices and neural engineering, disclosing a cognitive accessory that combines a brain-computer interface (BCI) with smart glasses, which includes an in-ear electrode module equipped with a plurality of EEG electrodes, a smart glasses frame with temples on both sides, a nose pad at the bottom of the frame, and two lenses installed on the frame. The smart glasses frame integrates a non-invasive brain signal acquisition module, an environmental perception module, an embedded processor, and an AR display module. The brain signal acquisition module uses flexible electrodes distributed inside the nose pad and the temples. By integrating flexible EEG electrodes and miniature fNIRS probes within the smart glasses frame, users can achieve efficient, non-invasive monitoring of brain electrical activity and cerebral blood oxygenation without wearing traditional complex headgear. This design makes the device more lightweight, comfortable, and convenient for long-term wear.

The application discloses a kind of consciousness disorder stimulation regulation systems and methods of fusion electroencephalogram connection identification, including simulation electroencephalogram signal data acquisition stage, connection identification analysis stage, stimulation parameter optimization stage and executable stimulation instruction conversion stage;The application has the following advantages and effects: generate whole brain electrical activity distribution map and identify key connection area by simulation electroencephalogram signal data acquisition stage, generate brain function connection atlas in connection identification analysis stage using functional connection analysis network and phase synchronization algorithm, establish the space-time association of whole brain electrical activity distribution map and brain function connection atlas in stimulation parameter optimization stage and generate optimized stimulation parameter set by fusion network using multi-objective optimization algorithm, finally executable stimulation instruction is converted based on adaptive control model in executable stimulation instruction conversion stage, so as to significantly improve the precision and adaptive ability of electroencephalogram signal stimulation regulation.

The present invention discloses a brain-computer interface system based on audio-visual matching error monitoring. According to the EEG data obtained from three feedback paradigms of audio-visual matching errors, ERP analysis is applied to extract important ERP components related to audio-visual matching errors, so as to interpret the neural response within a specific time window; then, based on time-frequency analysis, the dynamic changes of EEG signals in different bands are captured, and the phase consistency ITPC of EEG activity is combined to reveal the reliability and stability of EEG activity under different feedback modes; finally, through the improved EEGNet model, automatic feature extraction and efficient classification of signals are realized, and the robustness and generalization ability of the system are improved. EEG signals are highly nonlinear and specific. By combining ERP analysis, time-frequency analysis and deep learning models, it is helpful to more effectively understand the potential neural mechanism of audio-visual matching errors, improve the performance of brain-computer interface systems and apply them to clinical research on error processing related disorders.

The present invention discloses a fast filling device for conductive paste based on an electrode cap, the device comprising a plurality of flexible elastic bands and a plurality of suction injection balls arranged on the elastic bands. When the elastic bands are bent into the same arc as the electrode caps, the suction injection balls on each elastic band are plugged into the electrode cavities on the electrode caps in a one-to-one correspondence. The present invention installs a plurality of suction injection balls on a plurality of flexible elastic bands, and the distribution of the suction injection balls on the entire filling device is consistent with the distribution of electrodes on the electrode caps to be injected. Since the elastic bands have a certain elasticity, some of them can be bent and fitted with the electrode caps, and the suction ports of the plurality of suction injection balls are plugged into the cavities of the electrode caps. By manually pressing the injection balls, the conductive paste filling of the plurality of electrode cavities can be completed at one time. The entire filling time can be shortened to 5 minutes, which greatly improves work efficiency and makes the data recording of brain electrical activity more accurate.

The utility model discloses a disposable electroencephalogram sensor with a glue storage groove, and belongs to the technical field of sensors. Comprising a flexible circuit board and an electrode insertion piece, the flexible circuit board comprises a long-strip-shaped circuit board and an arc-shaped circuit board, one end of the long-strip-shaped circuit board is integrally connected to the middle of the arc-shaped circuit board, a plurality of unfolding parts are arranged on the path of the arc-shaped circuit board at intervals, foam is attached to the surface of each unfolding part, and a mounting groove and a glue storage groove are formed in the middle of each foam; an electrode plate is arranged in each mounting groove, and a sponge plate is attached to the upper surface of each electrode plate. Electrode plates on the left side and the right side of an arc-shaped circuit board are used for collecting electroencephalogram signals of the left brain and the right brain at the same time, electroencephalogram activities of a patient can be reflected more comprehensively, by arranging a glue storage groove, when the arc-shaped circuit board is attached to the scalp of the patient, a sponge piece is squeezed, and redundant conductive glue in the sponge piece is squeezed out; and the extruded conductive adhesive flows into the adhesive storage groove to be stored, so that the overflow of the conductive adhesive is prevented.

An emotion closed-loop healing method based on brain wave and plant interaction comprises the following steps: S1, acquiring electroencephalogram data of a user in real time through an electroencephalogram signal acquisition device, S2, extracting features from the electroencephalogram data based on a pre-trained emotion recognition model, and recognizing the current emotion state of the user, and S3, according to the recognized current emotion state, determining the emotion closed-loop healing method based on brain wave and plant interaction. S4, acquiring the electroencephalogram data of the user again through the electroencephalogram signal acquisition equipment at a preset time interval after the multi-modal feedback is triggered, and S5, generating and executing a forward excitation signal. The method has the advantages that indirect emotion indexes such as heart rate and galvanic skin are abandoned, brain electrical activity is directly collected and analyzed, more real emotion information is obtained from the source, the emotion recognition accuracy is remarkably higher than that of a traditional method, an immersive natural healing environment is built through multi-mode feedback such as light, fragrance, sound and visual animation, and the method is suitable for popularization and application. User experience is comfortable and easy to accept, and the ice-cold feeling of pure electronic equipment is avoided.

This invention provides a method and system for labeling sound events that induce electroencephalograms (EEGs) using a general-purpose computer, applied in the field of EEG processing technology. The method includes: in response to receiving an EEG acquisition command, constructing N target sound data sequences corresponding to N test trials, wherein the first target sound data segment in the N target sound data sequences has different audio data characteristics from the other target sound data segments; based on a callback function, generating and sending an event labeling command to an EEG acquisition device according to multiple target sound data segments in the N target sound data sequences, so as to acquire N sets of EEG signals corresponding to the N test trials generated by the test subject based on the stimulus audio obtained from the N target sound data sequences; and based on the event identifiers in the N sets of EEG signals, performing time axis alignment and superposition averaging processing on the N sets of EEG signals to obtain target-related potential signals.

Identification and categorizing of people according to educational capability is performed as indicated by detected brain activity by use of a Brain-Computer Interface (BCI) and associated sensor electrodes. Locations on a subject's head for detection of brain electrical activity emanating from a brain region most associated with creativity are identified, and the sensor electrodes are positioned on the subject's head at positions to allow the detection of the brain electrical activity at those locations. An output of the BCI is provided to a monitor, and the subject's brain activity is monitored with the BCI connected to the sensor electrodes. Sensed quantity and speed of change measurements of brain electrical activity emanating from the brain region most associated with creativity using the BCI are monitored according to the sensed quantity and speed of change measurements.

The invention provides a multi-channel electroencephalogram acquisition system based on Hessian matrix analysis and a signal processing method. The system comprises a multi-channel brain electrode array, a signal acquisition unit, a signal processing unit and a visual display unit, the multi-channel brain electrode array comprises a central electrode and at least eight surrounding electrodes surrounding the central electrode to form a Hessian electrode array; brain potential signals of at least nine electrode points of the Hessian electrode array are synchronously collected, and a Hessian matrix representing the local curvature of a potential field at the position of a central electrode is constructed; and performing eigenvalue decomposition on the Hessian matrix to obtain at least one main eigenvalue and a main eigenvector corresponding to the main eigenvalue, and calculating a main direction angle according to the main eigenvalue to represent the intensity of the electroencephalogram activity collected at the position of the central electrode and the direction of an electroencephalogram signal source. According to the method, accurate analysis of the directivity of the electroencephalogram activity source can be achieved, good spatial filtering capacity and high signal-to-noise ratio are achieved, and weak local electroencephalogram signals can be captured.

1. Name of the product in this design: EEG amplifier. 2. Purpose of this design: This product is used to collect and amplify human brain electrical signals, and in conjunction with brain electrical analysis software, to monitor, record and analyze brain electrical activity. It is mainly used in medical institutions, research institutes and rehabilitation centers for brain function assessment and auxiliary diagnosis of neurological diseases. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key features: the front view.

The application discloses an electroencephalogram-eyemovement multi-modal data fusion early screening intelligent system and method for depression. In a virtual reality context, the system stimulates the emotional and cognitive responses of the subject through a carefully designed cognitive task, and synchronously collects brain electrical activity and visual behavior data using a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, the weighted fusion formula or graph neural network is used to realize the deep fusion of multi-modal data, automatically adjust the weight of each mode, extract the interaction features between EEG and eye movement, and then accurately determine the depression risk of the subject through the classifier. Experiments show that the method can effectively capture the weak physiological abnormalities of mild depression patients, has high sensitivity, high accuracy and real-time online screening advantages, and meets the non-invasive, portable and intelligent clinical application requirements.

The present application relates to the technical field of medical care, and in particular to a hydrogen machine based on EEG control and a method for dynamically regulating its flow rate. The hydrogen machine based on EEG control includes an EEG detection module, a data processing module, and a hydrogen output module; the EEG detection module is used to capture the user's EEG; the hydrogen output module is used to output hydrogen-rich gas; the data processing module is simultaneously connected to the hydrogen output module and the EEG detection module signal, and the data processing module stores program instructions, which, when executed by the data processing module, implement a method for dynamically regulating the flow rate of a hydrogen machine based on EEG control. The present application has the effect of accurately intervening in the hydrogen inhalation pattern according to the user's EEG activity.

A medical device system is configured to sense electroencephalogram activity and to sense cardiac electrical activity. The system may detect a concerned heart rate from cardiac electrical activity and determine that the concerned heart rate is a haemodynamically unstable heart rhythm based on sensed brain electrical activity. The medical device system may generate an output for providing a response to determining that the concerned heart rate is a haemodynamically unstable heart rhythm.

The invention relates to the technical field of electroencephalogram signal feature extraction, and discloses an electroencephalogram signal feature extraction method based on artificial intelligence. An electroencephalogram data frequency band component extraction step; calculating an adjacent matrix of each frequency band; carrying out feature selection on values in the plurality of frequency band adjacent matrixes; calculating an adjacent matrix between every two frequency bands; feature selection is carried out on values in the adjacent matrixes of every two frequency bands; and in the electroencephalogram data preprocessing step, a 50Hz wave trap and independent component analysis are utilized. By constructing a cross-channel electroencephalogram network, accurate extraction of cross-channel electroencephalogram features is realized, a cross-frequency electroencephalogram network is constructed, on this basis, deep mining of cross-frequency electroencephalogram features is further realized, electroencephalogram information of different scales is effectively fused, and by cooperating with a deep neural network, the accuracy of the cross-channel electroencephalogram features is improved. The electroencephalogram activity in the chronic pain state can be analyzed more comprehensively, and the accuracy and efficiency of feature extraction are remarkably improved.

The invention discloses a lightweight depression detection model based on time sequence features, and particularly relates to the technical field of biological feature extraction and recognition, based on a TSLDRM architecture, the TSLDRM architecture comprises an LDEM and a GTEM; the different preprocessed time sequences serve as input data to be input into the detection model, the length of the input data is unified, and the LDEM and the GTEM are used for detecting short-term dynamic states of abnormal electroencephalogram activity fragments in the time sequences including instantaneous fluctuation and sudden change of physiological signals and in EEG signals; the method comprises the following steps: carrying out feature extraction on human physiological indexes, the long-term stability of heart rate variability, the overall change trend of the physiological indexes within a certain period of time when the voice rhythm is continuously abnormal, and the chronic physiological status or behavior pattern related long-term trend of a depression patient to obtain multivariable interaction feature representation and global trend feature representation; modeling of short-term dynamic and long-term trends in a time sequence is realized.