91 results about "EEG feature" patented technology

The invention discloses a device for awareness level evaluation and a storage medium. The apparatus comprises: a processor; the device realizes the following operations: collecting clinical information of a person to be assessed and a task state electroencephalogram signal under a target stimulation normal form; extracting frequency domain characteristics of a specific frequency band and spatial-temporal characteristics of a target event related potential based on the task state electroencephalogram signal, and combining the frequency domain characteristics and the spatial-temporal characteristics into a corresponding electroencephalogram topographic map; inputting the corresponding electroencephalogram topographic map into a multi-modal large language model, and performing image feature extraction by using an image encoder to obtain electroencephalogram features; inputting clinical information into the multi-modal large language model, and performing text feature extraction by using a text encoder to obtain text features; and performing cross-modal attention calculation fusion on the electroencephalogram features and the text features by using a cross-modal fusion module to realize consciousness evaluation so as to output a consciousness evaluation result. By means of the scheme, the consciousness level of the patient can be automatically and accurately evaluated.

The invention discloses an electroencephalogram voice annotation calibration and scheduling system and method for individual differences. The system comprises an electroencephalogram signal collecting and preprocessing module, a voice instruction receiving and recognizing module, a rapid individual calibration module, a dynamic task scheduling module, an electroencephalogram feature online decoding and annotation module and an annotation storage and feedback module. Through cooperative work of the two core modules, namely the rapid individual calibration module and the dynamic task scheduling module, the problems that decoding performance is reduced during cross-subject and cross-task switching caused by individual differences, and multi-task labeling efficiency is low due to task scheduling strategy stiffness are solved accurately. Data persistent storage, model online updating and user interaction feedback are completed through storage, optimization and feedback links, periodic retraining is carried out by utilizing daily successful labeling data through a model continuous learning link, and finally, a zero-threshold individual rapid adaptation and high-efficiency multi-task collaborative labeling target is achieved.

The invention discloses an electroencephalogram feature recognition method for motion intention prediction in a lower limb alternate stepping process. The electroencephalogram feature recognition method comprises the following steps: acquiring an electroencephalogram signal of a subject before the subject executes an action under the requirement of a lower limb alternate stepping motion execution normal form; preprocessing the electroencephalogram signal to obtain an initial signal, performing label labeling on the initial signal according to an action type, and forming a data set by a label and the initial signal; constructing a neural network model, wherein the neural network model comprises a multi-scale grouping space-time convolution module, a self-attention mechanism module for updating external memory based on Top-k gating and a grouping causal time convolution module; and training the neural network model in a supervised manner by using the data set to obtain a prediction model for predicting the action type. According to the method provided by the invention, the movement intention of the patient can be recognized by the movement brain-computer interface before the lower limb alternate movement, the control of the rehabilitation robot on the specific lower limb movement by the brain intention is realized, and the method has an important value on the clinical application of a brain-controlled rehabilitation robot system.

The invention relates to the field of electroencephalogram signal processing and neural engineering, and discloses a multi-mode electroencephalogram feature fusion decoding method. The method comprises the following steps: carrying out noise reduction, calibration and standardization processing on an original electroencephalogram signal to generate a standard signal; extracting mu / beta rhythm time-frequency features and Hjorth time-domain features of the signals, and generating a fusion feature vector through PCA dimension reduction fusion; performing pre-classification and weight optimization by using an SVM classifier, performing space and time sequence feature extraction and classification through a CNN-LSTM network, and outputting a classification probability; model parameters are updated according to the probability and verified, and finally real-time control signals suitable for various communication interfaces are generated. According to the method, the accuracy and robustness of electroencephalogram signal decoding are improved, and efficient and self-adaptive motor imagery brain-computer interface control is realized.

The invention relates to a brain-computer interface robot control method and system based on specific electroencephalogram characteristics, and the method comprises the steps: obtaining an electroencephalogram signal of a user in real time through an electroencephalogram signal collection device; predefined interpretable physiological features are extracted from the electroencephalogram signals; the key parameters capable of explaining the physiological features are directly input into a pre-constructed real-time decoding model based on a rule engine, and a robot control instruction is generated through a preset deterministic mapping rule; and the robot is driven to execute corresponding actions according to the control instruction, so that the purpose of accurately and stably controlling the brain-controlled robot is achieved.

The invention relates to the technical field of biomedical engineering, in particular to a waking-up system based on multi-modal electroencephalogram characteristic dynamic evaluation and closed-loop regulation, which comprises a multi-modal electroencephalogram acquisition module for acquiring multi-modal original electroencephalogram signals; the multi-dimensional awakening related electroencephalogram feature fusion extraction module is used for extracting a multi-dimensional fusion feature vector; the consciousness level and waking-up response dynamic evaluation module is used for loading an off-line constructed and completed deep learning model, carrying out real-time dynamic evaluation and generating a waking-up response dynamic evaluation result; the personalized closed-loop awakening regulation and control decision module is used for generating and dynamically updating a closed-loop awakening regulation and control scheme by adopting an awakening personalized self-adaptive regulation and control algorithm; and the multi-modal awakening regulation and control execution and man-machine interaction module is used for executing multi-modal awakening nerve regulation and control output and providing a visual dynamic evaluation result, awakening regulation and control parameters and a man-machine interaction interface for medical personnel and family members of the patient. Therefore, the problems of single electroencephalogram acquisition mode, one-sided electroencephalogram feature extraction and the like in the prior art are solved.

The invention provides a brain wave epilepsy detection method based on Transform. The method comprises the following steps: acquiring EDF data of a multi-channel electroencephalogram from a database, processing the EDF data, generating a CSV file, and performing data preprocessing on the generated CSV file; defining a plurality of machine learning models, and independently training the models on the EEG feature data to obtain the classification performance of the models; predicting a probability vector by using a machine learning model to construct Transform model input data; the attention of the Transform model is used for dynamic training, and a trained Transform fusion model is obtained; and outputting a prediction result, and storing the trained Transform fusion model. According to the method, more efficient model fusion is realized through dynamic fusion, and the accuracy and generalization ability of epilepsy detection are improved.

The invention discloses a motor imagery electroencephalogram signal classification method and device, a terminal and a storage medium, and relates to the field of biomedical signal processing.The method comprises the steps that electroencephalogram signals based on user motor imagery are obtained and preprocessed, and electroencephalogram features are determined; performing multi-scale spatio-temporal feature extraction and space and channel decoupling reconstruction on the electroencephalogram features, and determining target spatio-temporal enhancement features; and classifying the target space-time enhancement features through a classification output layer, and determining a classification result. Due to the fact that space and channel decoupling reconstruction is carried out on the features, redundant correlation of the cross-electrode electroencephalogram signals is systematically eliminated, and the problems that in the prior art, space and channel information are jointly processed, information redundancy is caused, and calculation burden is increased can be effectively solved.

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 multi-task behavioral-EEG synchronous monitoring method for schizophrenia screening, relating to the field of schizophrenia screening. The method includes: designing a multi-task temporal loading paradigm based on clinical screening needs; simultaneously collecting EEG signals and behavioral data of subjects during the execution of the multi-task temporal loading paradigm, and preprocessing the collected raw data; extracting multidimensional features from the preprocessed data, wherein the multidimensional features include EEG feature groups, behavioral feature groups, and time-varying feature groups; constructing a multimodal fusion discriminant model, inputting the multidimensional features into the multimodal fusion discriminant model, and outputting a baseline risk probability and a schizophrenia risk index. The advantages of this invention are: achieving efficient, accurate, and objective screening of the core cognitive dimensions of schizophrenia, effectively improving the sensitivity and specificity of screening.

This invention relates to the field of music signal recognition technology, and more particularly to a method for recognizing the degree of music enjoyment based on music-EEG feature fusion. The method includes: extracting the Mel-frequency cepstral coefficients of the music signal; preprocessing and performing Fast Fourier Transform on the Mel-frequency cepstral coefficients to obtain the spectral feature values ​​of the music signal; and sequentially filtering, reducing dimensionality, and decorrelating the spectral feature values ​​of the music signal; extracting the Mel-frequency cepstral coefficients of the EEG signal; optimizing and updating the order of the Mel-frequency cepstral coefficients of the EEG signal so that the comprehensive error value between the EEG feature reconstruction signal and the original EEG signal meets a preset condition; aligning the music feature coefficients and the EEG feature reconstruction signal in the feature dimension and time axis using the FastDTW algorithm; and recognizing the degree of music enjoyment of the subject based on the alignment result. This invention effectively improves the robustness and accuracy of music recognition.

The invention discloses a portable state analysis method and system based on multi-mode electroencephalogram and electrocardio, and belongs to the field of state analysis. The portable state analysis method comprises the steps that electroencephalogram signals and electrocardio signals of a subject are synchronously collected; performing preprocessing and feature extraction on the signals to obtain electroencephalogram features and electrocardio features; according to the values of the electrocardio characteristics in different physiological states, constructing difference value characteristics and ratio characteristics representing differences between the states; the electroencephalogram features, the electrocardio features and the new construction features are fused, and multi-modal features are obtained; and finally, inputting the fusion features into a pre-trained table priori data fitting network model, and outputting a classification result or a quantitative prediction value for representing the state of the subject. According to the method, through systematic state comparison feature engineering and an efficient table data model, the accuracy, generalization ability and automation level of multi-modal physiological signal analysis are improved, and the whole scheme is realized based on portable equipment and is suitable for state evaluation of various non-laboratory scenes.

The present application relates to a kind of automatic sleep staging method, its steps include: the EEG original data is processed to obtain EEG feature map, while the EOG original data is processed to obtain EOG feature map;EEG feature map and EOG feature map are multiplied and fused, and highlight feature map is obtained;Highlight feature map is directly added with EEG feature map and EOG feature map and fused to obtain initial fusion feature map;Initial fusion feature map is processed to obtain the most weight feature map by weight adjustment;Initial fusion feature map and the most weight feature map are multiplied and fused to obtain the final fusion feature map with weight information;According to the weight information of final fusion feature map, each sleep stage prediction probability is output.The automatic sleep staging method described in the present application is further trained and optimized, does not need too much artificial intervention, and can be simply and effectively applied in sleep staging of healthy individuals and patients with consciousness disorders.

The invention particularly relates to a neural decoding method based on multi-scale electroencephalogram signals and heterogeneous visual stimulation alignment, which comprises the following steps: firstly, performing frequency domain transformation, processing and inverse mapping on original EEG signals through an FADE module to effectively integrate frequency specific information; then, the signals are input into an ACC module, and the module achieves self-adaptive multi-channel relation modeling based on a functional relation instead of a fixed anatomical position through a dynamic clustering center and a cross attention mechanism; then, the encoded EEG features and CLIP visual features are subjected to alignment training under a joint loss function, and the loss function optimizes the semantic fidelity and discrimination of the features at the same time; finally, the EVA frame obtained through training can be used for heterogeneous visual stimulation decoding tasks such as zero-sample image retrieval, video classification and high-quality image reconstruction. According to the method, the capture capability of the EEG features on visual semantic information and the generalization of the model can be effectively improved, and the challenge of multi-scale and multi-modal neural decoding is solved to a certain extent.

The invention provides a brain-computer interface experiment method and device for multi-task teaching demonstration, edge computing equipment and a storage medium, and relates to the technical field of brain-computer interfaces, and the method comprises the steps: loading a brain-computer interface decoding model corresponding to a teaching normal form based on a currently selected teaching normal form; the obtained original electroencephalogram signals are input into a brain-computer interface decoding model for decoding processing, and real-time electroencephalogram data are generated; when it is judged that the similarity between the real-time electroencephalogram data and the preset electroencephalogram feature template is larger than or equal to a similarity threshold value, teaching commentary content corresponding to the preset electroencephalogram feature template is triggered; and the teaching explanation content is visually demonstrated. By adopting the method, the original electroencephalogram signal decoding process and the typical electroencephalogram characteristic teaching explanation can be combined, the visual presentation of the brain-computer interface key processing process is realized, and the intuition of teaching demonstration and the learning understanding effect can be improved.

This invention provides a method, electronic device, and storage medium for emotion recognition using electroencephalogram (EEG) signals. The method includes: acquiring the raw EEG signal of a subject; inputting the raw EEG signal into a trained EEG signal emotion recognition model to obtain the subject's emotion recognition result. The EEG signal emotion recognition model includes a mask injection module, a feature extraction module, a feature reconstruction module, and an emotion classification module. The mask injection module dynamically injects a mask into the raw EEG signal to obtain missing EEG signals; the feature extraction module extracts features from the missing EEG signals to obtain missing EEG features; the feature reconstruction module includes inter-modal guidance mechanisms and intra-modal guidance mechanisms; and the emotion classification module predicts the emotion category based on the missing EEG features to obtain the emotion recognition result. Based on this, this invention can improve the accuracy of EEG emotion recognition for subjects in scenarios where the EEG channel is partially missing.

The application provides a driving intention recognition method based on functional connectivity and a graph neural network, comprising the following steps: S1, collecting electroencephalogram signals of a driver during driving, and preprocessing original electroencephalogram data; S2, calculating power spectral densities of each frequency band of the preprocessed electroencephalogram signals as electroencephalogram signal frequency domain features; S3, constructing an adjacency matrix as an initial graph structure based on electrode spatial proximity and functional connectivity; and S4, inputting the frequency domain features in S2 and the adjacency matrix obtained in S3 into a graph attention network for feature aggregation, inputting the extracted feature expression into a classifier to classify driving intentions and outputting results. The method can solve the problems of single electroencephalogram feature extraction and poor expression ability of the classification model in the existing driving intention prediction method, improve the accuracy of driving intention classification, improve the interpretability of the classification results, and better apply the method to driving state perception and auxiliary decision-making of a human-machine co-driving system.

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.

A multi-source domain EEG emotion recognition method based on self-organizing sparse directed graph convolution includes: constructing EEG feature vectors based on EEG signals; constructing a sparse adjacency matrix based on the EEG feature tensor; performing directed graph convolution operations on the forward and backward adjacency matrices of the sparse adjacency matrix respectively to capture the asymmetric information propagation characteristics between EEG channels and generate domain-invariant EEG emotion features; constructing multiple domain feature extraction branches for multiple source domain samples and target domain samples; inputting the domain-invariant EEG emotion features into the multiple domain feature extraction branches respectively; constructing independent feature representation spaces for each source domain and target domain, separating domain-shared features and domain-specific features; classifying samples based on domain-shared features and domain-specific features; fusing the prediction results of each source domain classifier for the target domain samples to output the target domain EEG emotion recognition result; and performing cross-domain optimization of the model using the weighted result of the overall loss function of each source domain.

The application relates to the technical field of biomedical information processing, in particular to an autism classification system based on resting-state electroencephalogram signals, which comprises an EEG signal acquisition and preprocessing unit and an EEG signal classification unit, the EEG signal classification unit is used for classification by using a trained Rest-HGCN network model; the Rest-HGCN network model comprises a resting-state mixed graph network module, an attention learning module and a classification module; the resting-state mixed graph network module comprises a cognitive graph branch and a data-driven graph branch and is used for extracting corresponding feature mappings; the attention learning module is used for fusing the feature mappings extracted by the resting-state mixed graph network module to obtain a final feature mapping; and the classification module is used for classifying the final feature mapping to obtain a classification result. Through the classification system, the problems of ASD patient EEG feature extraction difficulty and low recognition rate in the prior art can be effectively solved, and only a small amount of features are needed to achieve the purpose of more efficient ASD classification recognition.

The invention discloses an electroencephalogram signal enhanced intelligent video abstraction method and system, relates to the technical field of video content understanding and electroencephalogram emotion analysis, and aims to solve the technical problems that in the prior art, video abstraction cannot reflect subjective emotion experience of viewers, electroencephalogram and video time axes are difficult to align, and heterogeneous data are difficult to fuse. Acquiring an electroencephalogram signal of a viewer in a video watching process, synchronizing the electroencephalogram signal with a video clip, and extracting and generating a structured video abstract based on multi-modal content; frequency domain features, time domain features, time frequency features and spatial correlation features are obtained according to the electroencephalogram signals, and electroencephalogram feature visual representation is generated; and fusing the video content abstract, the video key event timestamp and the electroencephalogram feature visual representation, deducing the emotional state and aligning the emotional state with the video event, and outputting a personalized video abstract reflecting the real emotional experience of a viewer. The method can be used for application scenes such as intelligent video abstraction, video platform recommendation, immersive content understanding and emotion calculation.

The invention discloses a visual lip auxiliary evaluation method based on voice perception in a noise environment of electroencephalogram. The visual lip auxiliary evaluation method comprises the following steps: step 1, implementing a stimulation experiment to collect electroencephalogram data; 2, preprocessing the electroencephalogram data collected in the step 1, and grouping according to noise types, signal-to-noise ratio levels and visual conditions to obtain an electroencephalogram data set; 3, electroencephalogram features are extracted based on the electroencephalogram data set obtained in the step 2, wherein the electroencephalogram features comprise PLV and COH; and 4, analyzing an auxiliary function of visual lip information on voice perception by combining the correlation between the electroencephalogram characteristics obtained in the step 3 and the voice recognition accuracy obtained in the step 1. According to the method, the limitation of traditional behavioral detection is broken through, and evaluation upgrading from perception result description to neural mechanism analysis is realized.

This application relates to the field of brain-computer interface technology, and particularly to a visual electroencephalogram (VEG) analysis method based on a phase-amplitude coupling-guided attention mechanism. The method includes: acquiring and encoding multi-source VEG signals to obtain at least two EEG feature sequences from different visual stimulus paradigms; calculating the phase and amplitude coupling index between different EEG features for the at least two EEG feature sequences; dynamically modulating the at least two EEG feature sequences based on the phase and amplitude coupling index to perform neurally synchronized feature fusion, outputting multi-level fused features; and generating a unified neural representation for downstream tasks based on the multi-level fused features. This method enables deep collaborative fusion and efficient analysis of multi-source VEG signals.

The invention discloses a cross-domain electroencephalogram emotion recognition method based on two-stage domain alignment and dual-classifier collaborative confrontation. The method comprises the steps that multi-subject and multi-session electroencephalogram emotion data are collected, and preprocessing operations such as filtering and denoising, artifact elimination and feature extraction are completed; constructing a feature extractor module comprising a common feature extractor and a domain specific feature extractor, and a dual-classifier module; performing global domain alignment and sub-domain alignment to complete two-stage domain alignment; meanwhile, dual-classifier consistency-certainty constraint loss is proposed, and the loss constructs a confrontation mechanism of'minimization loss of a dual-classifier module-maximization loss of a feature extractor module 'by calculating a joint consistency item and a local classification certainty item of a dual-classifier output probability. Guiding the model to generate electroencephalogram features with domain invariance and high classification certainty; and finally, completing model optimization through multiple rounds of iterative training and realizing accurate recognition of electroencephalogram emotion. And the cross-domain generalization ability of electroencephalogram emotion recognition is effectively improved.

The steps of the EEG emotion recognition system based on contrast learning and implicit emotion regulation mechanism are as follows: first, the preprocessed EEG feature matrix is divided into left and right brain two-dimensional EEG feature matrices according to the left and right electrode distribution respectively; the obtained and a randomly initialized adjacency matrix are input into a dynamic connection EEG representation extraction module to obtain left and right brain shallow emotion representations and left and right brain deep emotion representations respectively, and an emotion classification loss is calculated. Then, the obtained and are input into an automatic reverse regulation module to calculate a contrast loss; the obtained and are input into a brain lateralization mutual learning module to calculate a KL loss. The total loss obtained from and is used to constrain the system, and EEG emotion recognition network parameters γ are obtained. Finally, the EEG data to be tested is input into the EEG emotion recognition network, and the final emotion recognition result is obtained using γ. The present application further improves the EEG emotion recognition accuracy from the perspective of biological mechanism.

The invention provides a brain wave anomaly detection and classification method based on a convolutional neural network, and relates to the cross technical field of artificial intelligence and medical signal processing, and the method comprises the steps: carrying out the preprocessing of an original brain wave signal obtained by a brain wave collection device, and forming a standardized brain wave data set; the preprocessing comprises band-pass filtering processing, baseline drift correction processing and artifact elimination processing. Multi-scale convolution modeling processing is conducted on the electroencephalogram feature input to form multi-layer feature representation, a convolution kernel parallel structure is adopted in the multi-scale convolution modeling processing, and a residual coupling mode is adopted in the convolution kernel parallel structure. According to the electroencephalogram anomaly detection and classification method based on the convolutional neural network, electroencephalogram features can be effectively extracted through electroencephalogram signal preprocessing, spatial topology conversion processing and multi-scale convolutional modeling, then electroencephalogram anomaly events are accurately recognized through multi-task classification, the probability of anomaly occurrence is predicted, and the accuracy of electroencephalogram anomaly detection and classification is improved. Therefore, accurate electroencephalogram anomaly detection is realized.

This invention relates to a method, apparatus, device, and medium for classifying cognitive states based on multimodal neural signals. The classification method includes: acquiring electroencephalogram (EEG) information from a subject and preprocessing it to obtain a standardized EEG feature matrix; mapping the matrix using an EEG encoder from a pre-trained variational autoencoder to generate an enhanced feature representation containing high-resolution neural image information; inputting the matrix into a multi-task expert hybrid model decoder, dynamically allocating weights through a routing network, and calling multiple functionally specialized expert networks for processing to obtain a weighted integrated expert output; mapping the matrix to a classification space to output the classification result of the subject's cognitive state. This method achieves endogeneous feature enhancement of EEG signals through cross-modal deep fusion and simultaneously constructs an expert hybrid decoding architecture that conforms to the principle of brain functional partitioning, significantly improving the accuracy of cognitive state classification.

This invention relates to a real-time assessment and prediction method for mental fatigue based on multimodal signals and deep learning, addressing the problem that existing mental fatigue monitoring technologies rely heavily on static feature analysis of single physiological signals. First, it integrates dynamic brain function network features from electroencephalography (EEG) with time-frequency domain features of EEG to achieve feature fusion driven by multi-domain EEG features. Further, it automatically associates PPG signals with fatigue stages based on weighted K-means clustering, using the center value of heart rate to calibrate the fatigue level, eliminating the need for subjective labeling and achieving objective recalibration of mental fatigue levels. Finally, it uses a temporal deep convolutional network model to achieve real-time assessment and prediction of mental fatigue levels. This invention can accurately assess and predict mental fatigue levels in real time, and issue warning signals to operators with high levels of mental fatigue, providing technical support for real-time monitoring and warning of mental fatigue, and further reducing the rate of operational errors caused by mental fatigue.

The application relates to a brain-computer interface signal enhancement and evaluation method, system, device and storage medium fusing data and knowledge. The method comprises the following steps: acquiring general EEG semantic representation, baseline EEG features, differential equation driven features and geometric prior features of original electroencephalogram signals respectively; using a multi-scale brain physiological feature fusion strategy to dynamically fuse the general EEG semantic representation, baseline EEG features, differential equation driven features and geometric prior features, to generate electroencephalogram fusion features; taking the electroencephalogram fusion features as brain physiological consistency guiding features, and embedding the iEEG diffusion generation module to guide the diffusion generation of the iEEG signal, to obtain an electroencephalogram enhanced signal. Under the premise of not relying on invasive recording, the application realizes end-to-end generation from non-invasive scalp EEG to high-fidelity iEEG-like signals, and significantly improves the performance of the electroencephalogram enhanced signal in terms of time domain fidelity, spectral consistency and rationality of cortical spatial distribution.

This invention provides a motor imagery training system based on body ownership, comprising: a parameter generation module that extracts baseline individual alpha frequencies and generates temporal regulation parameters; a body ownership construction module that, based on the temporal regulation parameters, temporally pairs virtual limb movements with multimodal feedback to induce body ownership construction; a reference template construction module that extracts feedback-locked EEG features to establish a stable reference template for ownership; a multimodal feedback module that decodes motor imagery intentions to drive virtual limbs and couples out multimodal feedback, while simultaneously collecting the current task-state individual alpha frequencies and feedback-locked EEG features; and an adaptive regulation module that calculates the deviation of the above two features and updates the temporal regulation parameters or reconstructs body ownership and the reference template based on the deviation. This invention achieves stable binding and individualized closed-loop intervention between motor intentions and affected limb representations, improving training stability.