204 results about "Eeg data" patented technology

The invention relates to an electroencephalogram signal classification method based on multi-scale causal convolution and KAN attention. The electroencephalogram signal classification method comprises the steps of 1, EEG data collection and preprocessing; 2, EEG data enhancement; and 3, motor imagery task classification based on EEG signals. According to the method, the spatial-temporal characteristics of the EEG signals under different frequencies can be effectively extracted, the multi-scale nonlinear characteristics are efficiently fused and weighted, and the problem that nonlinear fitting of the EEG signals in a motor imagery task is insufficient is solved, so that more accurate and reliable motor imagery classification is realized.

The invention discloses an electroencephalogram signal clustering method based on a Mama framework and comparative learning, and belongs to the crossing field of engineering application and information science, and the method comprises the following steps: collecting and preprocessing label-free electroencephalogram data; the method comprises the following steps of: constructing a Mama-based feature extractor, dividing an input electroencephalogram signal into a plurality of slices by adopting an electroencephalogram signal slice embedding strategy so as to obtain fine-grained local information, and modeling a potential context relationship by utilizing a slice perception scanning mechanism; the method comprises the following steps: constructing an original data view and an enhanced data view, and respectively inputting samples of the two data views into two Mamba feature extractors with shared network parameters for feature embedding; processing the feature pairs of the two views using an instance projector, constructing weighted instance level contrast learning wherein the distance in the original data space provides weight information; processing feature pairs of the two views by using a clustering projector, and constructing a clustering distribution discrimination contrast learning branch and a semantic perception contrast learning branch; and finally, performing joint training by using the comparative learning branches, and outputting a high-quality clustering result by ensuring feature, clustering distribution and semantic consistency.

The invention discloses an electroencephalogram cap for meditation training and a control method thereof, and relates to the technical field of intelligent wearable equipment, the electroencephalogram cap comprises a cap body, an information acquisition module, a signal processing module, a wireless transmission module, a power supply module and a control host; the information acquisition module, the signal processing module, the wireless transmission module and the power supply module are respectively arranged on the helmet body, and the control host is respectively and electrically connected with the information acquisition module, the signal processing module, the wireless transmission module and the power supply module; the information acquisition module is used for acquiring microvolt-level electroencephalogram signals of a corresponding brain region and providing a data source for state recognition, the signal processing module is used for real-time denoising, power frequency interference suppression and artifact preliminary recognition, and the wireless transmission module is used for outputting processed electroencephalogram data streams in real time and transmitting the processed electroencephalogram data streams to the corresponding brain region. The power supply module supplies power to the information acquisition module, the signal processing module and the wireless transmission module. The electroencephalogram cap is convenient to wear, multi-dimensional analysis is achieved, feedback is timely, and the comfort level of mind calming training experience and the training effect can be improved.

A method of predicting a seizure occurrence or a surgical outcome includes monitoring a patient using an electroencephalography (EEG) system, including recording EEG data that indicates a seizure, and extracting a peri-ictal segment of the EEG data that includes a pre-ictal period immediately preceding the seizure, or a post-ictal period immediately following the seizure. The method also includes processing the peri-ictal segment, including generating an electrode-wise power spectral density (PSD) feature across a frequency band defined by at least one of a delta frequency range, a theta frequency range, an alpha frequency range, a beta frequency range, and a gamma frequency range. The method also includes inputting the PSD feature into a model, wherein the model predicts a seizure occurrence or a surgical outcome of the patient.

The invention provides an electroencephalogram image reconstruction method based on frequency steering and bidirectional diffusion, and belongs to the technical field of brain-computer interfaces and computer vision. The method comprises the steps that EEG data and corresponding image data are acquired and preprocessed; constructing a reconstruction model comprising a frequency domain-space-time dynamic encoder and a bidirectional submerged space diffusion generator; wherein the frequency domain-space-time dynamics encoder adopts a frequency-oriented Mama architecture, explicitly models neural oscillation dynamics by constructing a block diagonal state matrix, and extracts features in combination with graph convolution and space-time convolution; the bidirectional submerged space diffusion generator comprises a symmetric EEG-to-image submerged space diffusion model and an image-to-EEG submerged space diffusion model, and training is carried out through generative cyclic consistency constraint; and finally, mapping the collected EEG data into image semantic features by using the trained model, and driving a pre-training generation model to reconstruct an image. According to the method, the problems that in the prior art, the electroencephalogram frequency domain specificity dynamic state is ignored, and cross-modal semantic alignment is weak are solved, and the semantic consistency of electroencephalogram decoding and the fidelity of image reconstruction are remarkably improved.

The invention discloses an emotion recognition method based on spatio-temporal multi-scale attention convolutional neural network, which comprises: collecting EEG data of subjects for preprocessing to obtain EEG data containing spatial dimension and temporal dimension; constructing a lightweight convolutional neural network including two-stream spatio-temporal feature construction layer, hybrid attention mechanism layer, high-order fusion layer and classification layer; wherein the two-stream spatio-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the high-order fusion layer is used to re-learn from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; the trained lightweight convolutional neural network is used to identify EEG data, and the emotion recognition results of the subjects are obtained. By constructing a lightweight model with fewer parameters, the accuracy and efficiency of EEG-driven emotion recognition are improved.

The invention discloses a brain load assessment method and system based on hemodynamic information and electroencephalogram signals, and relates to the field of brain-computer interface classification systems. The technical problem that how to fuse electroencephalogram signals and hemodynamic information and comprehensively utilize complementary characteristics of the electroencephalogram signals and the hemodynamic information in time and space dimensions to achieve accurate recognition and dynamic evaluation of the mental load state in the prior art is urgently needed to be solved in the current brain-computer interface field is solved. The method comprises the following steps: acquiring EEG data and fNIRS data of a testee; performing preprocessing, selecting a suitable frequency band, removing interference such as high-frequency noise and low-frequency baseline drift, and constructing a corresponding training data set; respectively constructing an EEG single-mode network and an fNIRS single-mode network which are used for extracting related characteristics; and constructing an EEG-fNIRS multi-mode classification network for fusing data of brain imaging modes in the EEG single-mode network and the fNIRS single-mode network, and performing classification evaluation on the hemodynamic information of the subject and the brain power load data of the electroencephalogram by using the EEG-fNIRS multi-mode classification network.

The invention relates to a driver brain-controlled vehicle intention recognition method and system based on neural mechanism guidance. Recording EEG signals of the driver under different vehicle control motor imagery tasks; analyzing the collected EEG data, and identifying a key cortex activation area related to vehicle control; deploying the positions of an fNIRS emission light source and a receiving detector, and obtaining hemodynamic signals; brain region activation analysis is carried out on the collected fNIRS signals, the difference response condition of a key cortex activation region in a motor imagery task is verified, and a motor imagery data set about driver vehicle control is made; constructing a deep learning model, automatically extracting time domain, frequency domain, space and time sequence characteristics of the fNIRS signal, and training the model; the motor imagery brain signals are classified, and the vehicle control intention of the driver is output; according to the method, the physiological interpretability and the model structure rationality of the brain-controlled vehicle system are remarkably improved, and the problem of insufficient generalization caused by blind modeling is avoided.

The invention provides an EEG function connection prediction method based on fMRI deep cross-modal representation learning, and relates to the crossing field of neuroiconography and artificial intelligence. According to the method, a sample pair is constructed through time alignment of EEG and a blood oxygen level dependent signal, an end-to-end deep neural network architecture composed of a time projection unit, a cross-attention fusion encoder and a connection synthesis head is designed, and direct mapping from a BOLD signal to EEG function connection is achieved. The model adopts a composite loss function, and gives consideration to the consistency of the prediction connection matrix with the real EEG function connection in numerical precision and topological mode, thereby achieving the high-fidelity reconstruction of the EEG function connection map at the frequency domain level. According to the invention, the topological structure stability of the brain function network can be effectively maintained. Under the conditions of incomplete EEG data, serious noise interference or complete loss, the frequency domain function connection characteristics can still be stably recovered, and a reliable analysis substitution path is provided for brain function network research.

An auricular electroencephalogram (EEG) monitoring system may include an EEG recording module having a plurality of EEG sensor electrodes, configured to be coupled to a wearer's ear, and a processing unit configured to analyze EEG data recorded by the EEG recording module to detect presence or cessation of neuropsychiatric disorders of the wearer. An automatic detection-remedy system may include an auricular electroencephalogram (EEG) monitoring system and a transcutaneous auricular vagus nerve stimulation (taVNS) unit having a stimulating electrode in contact with vagus innervated auricular skin of the wearer's ear. When the presence of EEG signals suggestive of the neuropsychiatric disorder is detected by the processing unit, the processing unit is configured to immediately send signals to the taVNS unit to automatically start sending pre-determined electric stimuli to the vagus innervated auricular skin of the wearer's ear.

This invention discloses a preprocessing method for highly polluted pediatric EEG data, belonging to the field of EEG signal processing technology. The invention proposes a robust motion artifact detection method based on a multi-channel voting mechanism. This method overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision-making, automatically identifying time periods requiring restoration. A comprehensive preprocessing workflow integrating PCHIP interpolation restoration, adaptive Bayesian wavelet denoising, bandpass filtering, and independent component analysis is designed. This workflow is validated using real pediatric continuous task EEG data. This invention demonstrates excellent performance in preserving frequency band information and improving the weighted signal-to-noise ratio, achieving an average accuracy of 96.4% (205 test cases) and a maximum of 100% based on permutation entropy feature classification. This invention effectively solves the preprocessing challenge of pediatric EEG data in high-noise environments, significantly improving the accuracy and reliability of subsequent classification tasks.

This invention relates to the field of EEG emotion recognition technology, specifically to an emotion recognition method and system based on two-level KL divergence. The method includes: acquiring EEG data and calculating the first KL divergence between the left and right hemispheres in each region before and after external sensory stimulation, at each set frequency band; averaging the first KL divergence of each segment in each hemisphere as the feature before and after external sensory stimulation; calculating the second KL divergence based on this feature; and concatenating the feature before and after external sensory stimulation with the second KL divergence as input to a classification model. This invention deeply mines the emotionally sensitive features in EEG signals through two-level progressive distribution difference calculation and combines it with a classification network to achieve model training and accurate classification, thereby achieving precise recognition of subtle emotional changes.

This invention presents a system using an alternative method to quantifying levels of substances from the EEG data using a non-linear equation based on the same essential features of previous disclosures. The essential features consist of predetermined frequency bands, the amalgamated ratio and a regression formula: (Formula (I)) where Y is the EEG- based substance level quantification.

The present disclosure relates to a method and device for extracting and detecting time-frequency joint features of epilepsy based on covariance decomposition, an electronic device and a storage medium. The method comprises: performing data preprocessing on collected electroencephalogram (EEG) data to generate EEG data; calculating a covariance matrix, eigenvalue decomposition, feature extraction and stretching processing on the EEG data after decentralization to generate a time-domain feature vector; performing spectral feature extraction and frequency-domain covariance feature extraction respectively to generate a spectral feature vector and a frequency-domain covariance feature vector; generating time-frequency joint features based on a preset feature fusion strategy; and classifying and identifying the time-frequency joint features based on a preset binary classification method to complete epilepsy prediction. The present disclosure extracts and fuses time-frequency multi-scale feature information, which can effectively shorten the manual data labeling time of medical workers, improve the labeling efficiency of epilepsy seizure events, and provide a new approach and method for clinical application of epilepsy detection.

This application relates to the field of data processing technology, and in particular to a data fusion method and system based on neural pathways. The method includes the following steps: First, acquiring electroencephalogram (EEG) data and eye-tracking (EMT) data, aligning them using timestamps, and preprocessing them separately; then, extracting EEG features based on the preprocessed EEG data; extracting EMT features based on the preprocessed EMT data; next, fusion of features based on the EEG and EMT features by downsampling the data; finally, evaluating the synergy of the fused features. This application constructs a complete evaluation system by building a framework from a stimulus-visual paradigm to a signal acquisition platform and analysis methods related to neural structures. The time delay between the most relevant points in the time domain of eye-tracking and EEG features is used as an indicator of brain-eye synergy. The difference between these indicators has interpretability based on physiological structures, constituting a novel evaluation indicator for brain-eye synergy when assessing dynamic visual acuity.

This invention discloses a data processing method and system based on the changing trends of image features, relating to the field of cerebral infarction early warning technology. The data processing method includes: feature extraction: processing image data and modal data separately using feature analysis methods to obtain image features, image feature change data, modal data features, modal data feature change data, and the regions where the features are located. In this invention, image data provides intuitive information about brain structure and lesions, while modal data supplements key features such as hemodynamics, brain tissue electrical activity, and electrical impedance from different physiological dimensions. TCD data can monitor blood flow velocity and vascular resistance in real time, BI data is sensitive to changes in pathological states such as cerebral edema, and EEG data reflects subtle changes in neuronal electrical activity. Through feature extraction and analysis, the combination of multi-source heterogeneous data achieves the correction of image feature changing trends, thereby ensuring the accuracy of data processing.

This application discloses a wearable EEG signal processing method and device based on feature fusion. The method includes: synchronously latching the main channel EEG signal from bioelectric electrodes and multimodal auxiliary signals from various auxiliary sensor interfaces within a preset period; inputting the multimodal auxiliary signals into a pre-trained interference identification model, outputting a structured interference quantization vector, which includes the confidence and intensity of different interference sources; processing the main channel EEG signal according to the confidence and intensity of different interference sources and a preset parameterized anti-interference algorithm library to obtain the final EEG data; quantifying the quality confidence of the final EEG data, storing the ternary mapping relationship between the current period, quality confidence, and the final EEG data, and sending it to a downstream application. Using this application, adaptive matching of the processing strategy with the real-time interference scenario is achieved, effectively avoiding overprocessing or underprocessing, and greatly improving the data robustness and accuracy of downstream applications.

This invention discloses an improved method for generating driver fatigue EEG data using a latent diffusion model, belonging to the field of EEG signal processing. The method includes: processing the original multi-channel driver fatigue EEG signal through multi-scale wavelet denoising, adaptive thresholding, and an improved soft thresholding function; then performing inter-channel covariance alignment and whitening to eliminate redundancy to obtain a preprocessed signal; performing short-time Fourier transform and logarithmic energy normalization to obtain normalized time-frequency features; inputting the time-frequency features into an encoder to obtain the mean and variance of latent variables, and obtaining latent variables through reparameterized sampling; reconstructing the time-frequency features using a decoder combined with fatigue state labels; training a conditional variational autoencoder by minimizing reconstruction loss and KL divergence loss; adding noise through forward diffusion in the latent space; training a denoising network to remove noise based on fatigue state labels during reverse denoising; inputting the denoised latent variables into the decoder, combining them with a specified fatigue state to generate new time-frequency features, and reconstructing them into the target EEG signal. This invention can enhance the training dataset and improve the accuracy of driver fatigue monitoring.

This application provides a cross-subject EEG emotion recognition method based on similarity-based dynamic cue routing, belonging to the field of EEG emotion recognition technology. The method includes: acquiring target domain samples and multiple source domain samples formed from subject EEG data and mapping them to target shared latent representation and multiple source domain shared latent representations, respectively; injecting corresponding source domain cue vectors into the source domain shared latent representation of each source domain to obtain enhanced source domain feature representations of multiple source domains; calculating the similarity between each source domain cue vector and the target shared latent representation, converting each vector similarity into sample-level source domain routing weights, and weighting and combining each source domain cue vector to generate dynamic cue; injecting the dynamic cue into the target shared latent representation to obtain enhanced target feature representations; and inputting the enhanced source domain feature representations and enhanced target feature representations of multiple source domains into a multi-branch neural network to train and construct an emotion recognition model.

The method for analyzing incomplete channel EEG signals based on adaptive orthogonal regression belongs to the field of EEG signal analysis and pattern recognition technology. This invention proposes an embedded feature selection framework for incomplete channel EEG data, enabling robust modeling directly without channel completion and effectively reducing the impact of missing channels on the analysis results. By introducing a channel missingness perception mechanism and an adaptive channel weighting strategy, this invention achieves dynamic modeling of the contribution of information from different channels, significantly improving the model's stability in multi-channel and high-missing-rate scenarios. The orthogonal regression-based feature selection mechanism can maintain the integrity of discriminative information while suppressing redundant features, making it suitable for high-dimensional EEG data analysis tasks. This method has good versatility and can be adapted to different channel scales (such as few channels and high-density channels) and single-modal or multi-modal fusion scenarios, possessing high engineering application value.

The application discloses an analysis system and an analysis method for automatically detecting semi-structured data quality problems, the analysis method takes JSON data in semi-structured data as a specific research object, and comprises the following steps: a parsing mode module is used for parsing semi-structured data into an aggregated schema tree; a data quality problem monitoring module is used for automatically detecting potential data quality problems of data according to the aggregated schema tree and a data quality space; a visualization generation module is used for visualizing the aggregated schema tree and the data quality problems, helping users to interactively find and view the data quality problems; and a data cleaning module is used for user configuration and solution of the data quality problems. The system can help users to quickly and effectively locate and clean the data quality problems of semi-structured data.

The invention discloses a cerebral infarction risk early warning method and system based on an image feature change trend, and relates to the technical field of cerebral infarction early warning. The cerebral infarction risk early warning method comprises the steps of feature extraction, wherein image data and modal data are processed through a feature analysis method, and image features, image feature change data, modal data features, modal data feature change data and regions where the features are located are obtained; the image data provides visual information of a brain structure and a focus, the modal data supplements key features such as hemodynamics, brain tissue electrical activity and electrical impedance from different physiological dimensions, the TCD data can monitor blood flow velocity and vascular resistance in real time, and the BI data is sensitive to change of pathological states such as encephaledema. The EEG data reflects the subtle change of the electrical activity of neurons, and the image feature change trend is corrected through feature extraction and analysis and combination of multi-source heterogeneous data, so that the accuracy of cerebral infarction risk early warning is ensured.

This application provides a method, system, electronic device, and storage medium for recording electroencephalogram (EEG) signal data, comprising: acquiring an EEG signal to be recorded from target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses; statistically analyzing the values ​​of the EEG signal to be recorded to obtain EEG signal characteristics of the target EEG data; classifying and labeling the EEG signal to be recorded according to the EEG signal characteristics, wherein the classification includes: normal EEG signals and abnormal EEG signals; and classifying and recording the normal EEG signals and / or abnormal EEG signals. This allows EEG signals to be divided into normal and abnormal EEG signals based on EEG waveforms, and records corresponding index addresses for different types of EEG signals, significantly shortening the retrieval time for EEG signals.

The invention provides a motor imagery training and online decoding method and device. The method comprises the steps of dynamically determining a task difficulty threshold value of a current trial according to a task completion degree of a subject in a previous trial; obtaining a prediction result of a corresponding motor imagery category by using a decoding model for motor imagery category classification on the basis of electroencephalogram data generated by the subject and collected in the current test; according to a comparison result between the prediction result and a task difficulty threshold value, determining whether the current trial corresponds to the effective motor imagery or not; in response to determining that the current trial corresponds to the valid motor imagery, providing feedback corresponding to the valid motor imagery; after the current trial is finished, updating the decoding model; and repeatedly executing the above steps to complete a current training session comprising a plurality of trials, and executing trials updating on the decoding model based on the collected corresponding electroencephalogram data. According to the method disclosed by the embodiment of the invention, performance fluctuation and disastrous forgetting caused by blind updating can be avoided.

A kind of motor imagination EEG signal decoding method based on multiscale filtering and attention mechanism, comprising: the original EEG data of subject is divided into single experiment electroencephalogram data and normalized;Training data is handled with time domain data enhancement strategy;EEG signal is input as four parallel networks, through a multiscale convolutional neural network including time convolution layer, spatial convolution layer and average pooling layer;All feature channels at each time point are used as token for Transform processing.The first residual sub-block of Transform is composed of layer normalization and attention mechanism, and the second residual sub-block is composed of layer normalization and channel multilayer perception structure;The space-time features output by four parallel networks are spliced along the channel dimension, and effective channel attention mechanism ECA is used for channel fusion;Two-dimensional convolution is used to classify test signal.The present application can improve the classification accuracy of motor imagination brain-computer interface and promote its practical application.

The invention relates to an electroencephalogram acquisition system supporting visual monitoring and real-time brain control interaction. The electroencephalogram acquisition system is used for overcoming the defects in the prior art in the aspects of high-quality acquisition, real-time processing, data visualization, external interaction control and the like of electroencephalogram signals. According to an acquisition module of the system, 16-channel electroencephalogram signals are synchronously acquired through cascade connection of two ADS1299 chips, the chips are connected in a daisy chain and standard cascade connection combined mode, the number of digital signal interface lines is reduced, and independent configuration and synchronous control capacity of each chip are reserved. The micro-control module is realized by using an embedded program based on a FreeRTOS real-time operating system, communicates with the acquisition module, receives and caches electroencephalogram data, packages the electroencephalogram data and wirelessly sends the electroencephalogram data to the upper computer module. And the upper computer module carries out real-time analysis, reconstruction and visual display on the received electroencephalogram data. And the interaction control module extracts and identifies electroencephalogram characteristics based on analysis and reconstruction results of the upper computer module, and sends a control instruction to external equipment to realize closed-loop brain control interaction.

This invention provides a real-time control system and method for a humanoid robot that integrates electromyography (EMG) and monocular vision, relating to the field of multimodal humanoid robot control technology. The system includes a posture data acquisition system, an EMG data acquisition system, an EEG data acquisition system, a computer system, a data transmission module, and a humanoid robot. The posture data acquisition system acquires two-dimensional motion videos of the user's entire body; the EMG data acquisition system acquires EMG signals from the user's arm movements; the EEG data acquisition system acquires EEG signals from the user's motor imagery; the computer system converts the received two-dimensional motion videos, arm movement EMG signals, and motor imagery EEG signals into corresponding control commands to control the humanoid robot to perform corresponding actions. This system and method integrate multiple information sources, enabling precise control of the humanoid robot's major joint movements using monocular vision information and precise control of the humanoid robot's hand and wrist movements using EMG information.

The application discloses a cross-subject electroencephalogram signal classification method based on online test time domain adaptation, and steps of the method comprise the following steps: 1, pre-processing original EEG data, including removing noise, segmenting, extracting time-frequency features by using short-time Fourier transform, and obtaining source domain data and target domain data; 2, constructing a source model, a student model and a teacher model based on a CNN network, and training the source model by input data to obtain a pre-training source model; 3, initializing the student model and the teacher model by using the pre-training source model; 4, online optimizing the student model and the teacher model based on a mutual learning strategy on target data flow and realizing classification of electroencephalogram signals. The application can realize rapid classification of electroencephalogram signals under the condition of protecting the privacy of patients, so that the real-time demand of the classification system of electroencephalogram signals in an actual scene can be met.

The present application relates to electroencephalogram image processing and deep learning technical field, disclose the method for constructing the connection between electroencephalogram data and emotional state by hierarchical feature fusion, HSFS-Transformer model is designed, aims at through layered extraction global and local features fully excavates the space-time characteristics of EEG signal, improves the performance of emotional classification identification task, then collects the EEG data of sensory member to different citrus flavor for the offline training of HSFS-Transformer model, so as to obtain the emotional value binary classification model and four classification model that can be carried out to different citrus flavor.The present application models the overall spatial relationship of electroencephalogram signal and the fine-grained feature in specific brain area respectively, realizes the multi-granularity hierarchical feature representation, overcomes the shortcomings such as strong subjectivity, weak practicability and poor repeatability of conventional sensory analysis, and simultaneously introduces the emotional value evaluation concept in sensory evaluation.