12 results about "Brain electrical activity" patented technology

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 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 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 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.

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.

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 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.