773 results about "Eeg data" patented technology

The invention discloses an EEG-fNIRS deep fusion cognitive state classification method based on feature decoupling, and the method comprises the steps: firstly synchronously collecting EEG data and fNIRS data of a tested mental calculation task, and carrying out the preprocessing of the two collected data, so as to obtain EEG data and hemoglobin concentration change data; secondly, electroencephalogram signal data and hemoglobin concentration change data are taken, data enhancement is carried out through a time window, then the data are sent to a deep fusion network, and public features and private features of EEG and primary features of fNIRS are obtained; and an EEG classification result, an fNIRS classification result and an output fusion classification result are obtained. And finally, designing decoupling loss and cross entropy loss based on a label smoothing strategy, and carrying out training. According to the method, the overfitting problem in fNIRS modal deep learning is effectively relieved, and cognitive state classification can be accurately carried out.

The invention discloses a multi-modal signal fusion emotion recognition method based on an attention mechanism. The method comprises the following steps: firstly, preprocessing physiological signal data; constructing a channel attention module for the multi-channel electroencephalogram data; respectively extracting EEG and other physiological signal features by using the EEGNet; a shared-private encoder is introduced to decouple shared features among the multiple modes and private features of each mode; and finally, providing a cross-modal cross attention fusion mechanism to realize multi-modal feature interaction and effective fusion. According to the method, multi-modal signals are combined, the limitation of a single mode is overcome, and a sharing-private feature separation mechanism and a cross-modal cross fusion mechanism are introduced, so that modal collaborative modeling and effective integration are realized, and the accuracy and generalization ability of emotion recognition are improved.

The invention discloses an emotion recognition method based on a space-time multi-scale attention convolutional neural network, and the method comprises the steps: collecting electroencephalogram data of a subject, and carrying out the preprocessing of the electroencephalogram data, so as to obtain electroencephalogram (EEG) data containing a space dimension and a time dimension; constructing a lightweight convolutional neural network comprising a double-flow spatial-temporal feature construction layer, a mixed attention mechanism layer, a high-order fusion layer and a classification layer; wherein the double-flow spatial-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the mixed attention mechanism layer combines a channel attention mechanism, a space attention mechanism and a self-attention mechanism; the high-order fusion layer is used for carrying out re-learning from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; and recognizing the EEG data by using the trained lightweight convolutional neural network to obtain an emotion recognition result of the subject. The precision and efficiency of emotion recognition driven by electroencephalogram signals are improved by constructing a lightweight model with a small number of parameters.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The invention relates to the technical field of music feedback, in particular to a music feedback training interface interaction method and system based on electroencephalogram data, and the method comprises the following steps: collecting electroencephalogram data of a user based on an electroencephalogram collection device, and removing environmental noise and non-target signals through signal amplification and filtering processing to obtain a user electroencephalogram data set. According to the method, the emotion recognition accuracy and time sensitivity are improved by analyzing the brain wave frequency band intensity, judging the emotion state of the user and recording the emotion change time point, and the emotion recognition accuracy and time sensitivity are improved by matching music tracks of the emotion state, combining real-time monitoring of electroencephalogram reaction and adjusting the rhythm, volume and spatial sound effect setting of music. Dynamic adjustment of music output parameters is achieved, the emotion intervention requirement is optimized, emotion improvement and user satisfaction are evaluated according to brain wave changes of the user, and the improvement effect of music intervention on the psychological state of the user can be measured.

The invention discloses an attention deficit hyperactivity disorder recognition system and recognition method, and belongs to the technical field of attention deficit hyperactivity disorder. The data processing module is used for carrying out preprocessing and feature extraction on the acquired electroencephalogram data; a multi-source feature fusion mechanism is firstly used for the extracted original feature data, and then a data enhancement strategy is applied; a multi-source fusion feedback regulation network model is constructed, wherein the model is of a CNN-GRU parallel modeling structure; the training module is used for inputting the enhanced data into a model for training, key hyper-parameters are dynamically adjusted by a performance feedback adjusting mechanism in the training process, and the performance feedback adjusting mechanism is used for dynamically adjusting key training parameters according to the performance of the verification set; and the classification module is used for classifying to-be-detected samples through the trained multi-source fusion feedback regulation network model and outputting a final recognition result. The ADHD electroencephalogram recognition method effectively improves the accuracy, robustness and generalization performance of ADHD electroencephalogram recognition.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

A speech brain-computer interface neural decoding system based on Chinese language, a method for controlling the speech brain-computer interface neural decoding system, and a method for implementing the speech brain-computer interface neural decoding system are disclosed. The speech brain-computer interface neural decoding system includes an electroencephalography (EEG) data acquisition module, a significance feature screening and verification module, a speech imagery EEG data decoding module, and an understandable speech synthesis module. The speech brain-computer interface neural decoding system integrates EEG data collection, EEG feature extraction, EEG feature screening, EEG signal decoding for reconstructing speech spectrum information and understandable speech synthesis. After obtaining reconstructed spectrogram features, a Pearson correlation analysis may be performed with original spectrogram features, with a correlation generally being ≥80%. The decoding performance of the speech brain-computer interface neural decoding system is superior to traditional decoding models, effectively improving communication between patients with speech disorders and the outside world.

The invention discloses a multi-path deep neural network-based ratchet wave superposition ripple detection method. The method comprises the following steps: 1, collecting electroencephalogram data of a real BECTS patient, preprocessing and slicing; 2, performing feature extraction and dynamic spectrum weighted fusion on the processed low-frequency and high-frequency electroencephalogram signals by using a deep neural network, and outputting a high-order feature map; 3, a multi-head attention mechanism and a coding layer are introduced, dynamic weight distribution and global dependency relationship modeling of electroencephalogram signal features are achieved, and a classification result is output; 4, dynamically processing a sample imbalance problem by adopting weighted cross entropy loss and differentiable AUC loss, and constructing and optimizing a classification model; 5, the optimized and adjusted RonS detection model is applied, and electroencephalogram RonS detection is achieved on the continuous EEG signals. According to the method, the multi-kernel causal convolution module is constructed to extract the time domain features of the ratchet waves, and the PSD weighting mechanism is combined to fuse the multi-band information, so that the detection precision and generalization ability of the RonS in the complex electroencephalogram signals are remarkably improved.

The invention belongs to the technical field of hearing testing, and relates to a self-adaptive hearing screening optimization method based on acoustic feature analysis, which comprises the following steps: generating audio data with different signal-to-noise ratios through a constructed audio library, measuring an acoustic transfer function of a user ear canal by using a probe microphone, and generating a personalized acoustic transfer function in combination with a head-related transfer function. Time domain and time frequency features are extracted by means of EEG data, and the auditory reaction ability and the spatial auditory ability of the user are quantified in combination with the spatial positioning features of the binaural simulation microphones; the method comprises the following steps: constructing a multi-modal auditory feature vector, weighting features in combination with a multi-head attention mechanism, predicting the hearing age of a user through a time sequence Transform encoder in combination with the age of the user and a standard threshold value, and evaluating hearing loss according to a prediction result; the individual difference of each user can be accurately adapted, the defect that individual difference and complex environmental noise are neglected in a traditional hearing screening method is overcome, and therefore the accuracy of a screening result is improved.

The invention discloses an interactive learning optimization method and system based on brain-eye fusion and transfer learning. The method comprises the following steps that electroencephalogram data and eye movement data of different subjects are collected; preprocessing the electroencephalogram data and the eye movement data to obtain an electroencephalogram sample and an eye movement sample, and performing data pairing; automatically extracting an electroencephalogram feature vector and an eye movement feature vector in the electroencephalogram sample and the eye movement sample by using a convolutional neural network in combination with a multi-head attention structure, and fusing the electroencephalogram feature vector and the eye movement feature vector into a final feature vector through a full connection layer; aiming at data differences among different subjects, constructing a transfer learning model; and according to the pupil size and the residence time of the user in combination with the electroencephalogram data, retraining and identifying to obtain sentiment classification for different contents. On the basis of multi-modal data fusion and transfer learning, various physiological data such as electroencephalogram signals and visual signals are fused, and the limitation of traditional single signal analysis in a complex scene is overcome.

The invention provides an electroencephalogram model training method and device and a storage medium, and the method comprises the steps: segmenting a first original EEG data sample into a first original EEG fragment set, and covering a part of original EEG fragments in the first original EEG fragment set to obtain a corresponding visible EEG fragment subset and a covered EEG fragment subset; corresponding visible EEG simulation frequency domain coding features are determined through coding; extracting original signal frequency domain features, and reconstructing each visible EEG fragment according to visible EEG simulation frequency domain coding features to calculate frequency domain reconstruction loss; decoding the visible EEG simulation frequency domain coding features to predict a codebook basic class corresponding to each masked EEG fragment, and calculating classification loss by combining a codebook basic class label of each masked EEG fragment; and performing training optimization on the electroencephalogram model according to the frequency domain reconstruction loss and the classification loss. Therefore, double optimization of the frequency domain reconstruction task and the classification task of the electroencephalogram model is realized.

The invention discloses a cross-domain electroencephalogram emotion recognition method for progressive target tested sample mining, which comprises the following steps of: firstly, collecting electroencephalogram data in different emotion inducing scenes, preprocessing, extracting characteristics of different channels and frequency bands, and generating source domain and target domain data; secondly, by aligning marginal distribution and conditional distribution of a source domain and a target domain, a target domain characteristic label matrix is determined based on similarity, and a sample confidence coefficient weight matrix is obtained; secondly, measuring an error between a sample and a characteristic label by using cross entropy, and constructing a target function by combining a sample confidence coefficient weight matrix, marginal distribution and a conditional distribution alignment error; and finally, according to the target function, carrying out joint iterative optimization, and according to the target domain characteristic tag matrix after iterative optimization, judging the emotion category of the testee during electroencephalogram data acquisition. According to the method, the overall performance of the model is effectively optimized, the global optimality of the result is ensured, and more accurate electroencephalogram emotion recognition is obtained.

The invention discloses an intelligent asthenopia control method and system based on eye movement and electroencephalogram data. The method comprises the steps that eye movement time sequence data and electroencephalogram rhythm signals of a user are synchronously obtained through a multi-mode sensing unit; inputting the eye movement time sequence data and the electroencephalogram rhythm signal into a multi-modal fusion decision model, and generating a fatigue level through feature weighting based on an attention mechanism; at least one intervention mode is dynamically selected according to the asthenopia level, and the intervention modes comprise the first mode, the second mode and the third mode; in the first mode, a display interface adjusting instruction containing natural light simulation parameters is generated, and the display interface adjusting instruction is integrated with a vegetation color system compensation algorithm; and mode 2: starting a visual training program with ecological images, and generating a dynamic guide mark simulating natural motion on an interface. According to the invention, through a data-environment-physiology multi-dimensional collaborative innovative architecture, the spanning of asthenopia regulation from passive response to ecological active restoration is realized.

The invention discloses an electroencephalogram signal processing method and system based on motion artifact prediction. The method comprises the following steps: acquiring experimental electroencephalogram data when a testee executes a motion imagination task; discrete wavelet transform is carried out on experimental electroencephalogram data, and signals are decomposed into low-frequency components and high-frequency components through a low-pass filter and a high-pass filter; inputting the low-frequency component into a pre-constructed and trained ARIMA model, and predicting to obtain a linear artifact; inputting the high-frequency component into a pre-constructed and trained XGBoost regression model, and predicting to obtain a nonlinear artifact; combining the linear artifacts and the nonlinear artifacts to generate a complete artifact prediction signal; real electroencephalogram signals are separated through difference value calculation of the experimental electroencephalogram data and the artifact prediction signals. According to the method, the time sequence change of the motion artifacts is predicted by utilizing the ARIMA model, and the nonlinear artifact features are captured in combination with the XGBoost regression model, so that the motion artifacts can be effectively removed, and purer electroencephalogram signals can be recovered.

The invention belongs to the field of brain-computer interface emotion recognition, and particularly relates to a cross-domain EEG (electroencephalogram) signal emotion recognition method based on multi-level alignment, which comprises the following steps: acquiring an electroencephalogram signal, and inputting the electroencephalogram signal into a trained emotion recognition model to obtain an emotion classification result; the training process of the emotion recognition model comprises the steps that electroencephalogram signals are obtained and preprocessed, and preprocessed electroencephalogram data are divided into source domain data and target domain data; performing feature extraction on the source domain data and the target domain data to obtain shallow layer features and deep layer features; updating model parameters according to the unconditional MMD loss of the shallow features and the conditional MMD loss of the deep features until a trained emotion recognition model is obtained; according to the method, the universal feature difference between the domains is effectively reduced through the unconditional MMD loss of the shallow-layer features, the consistency of different types of features is further enhanced through the conditional MMD loss of the deep-layer features, and fine feature alignment between the source domain and the target domain is achieved.

The invention relates to an electroencephalogram and voice-based depression auxiliary diagnosis system and a data analysis method thereof, and the auxiliary diagnosis system comprises a data processing module, a detailed diagnosis mode module, a simple diagnosis mode module and a patient information management module. The data processing module comprises a data preprocessing unit and a fixed electroencephalogram feature extraction and visualization unit; the detailed diagnosis mode module is used for carrying out personalized feature extraction and mode recognition on electroencephalogram data by scientific researchers and checking the recognition accuracy rate, a visual graph and an evaluation index statistical table of a selected machine learning model, and the simple diagnosis mode module is used for rapid diagnosis. Comprising a single-mode electroencephalogram data processing unit, a single-mode voice data processing unit and an electroencephalogram voice fusion data processing unit, the simple diagnosis mode module generates a diagnosis result, and the result is in a healthy or depression character form. The result obtained by the auxiliary diagnosis system is objective, meticulous and reliable.

The invention discloses a learner cognitive state recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining electroencephalogram data of a learner in a plurality of learning behavior processes; dividing data of each electrode channel in the preprocessed electroencephalogram data into frequency band data of a plurality of different frequency bands, segmenting the plurality of frequency band data corresponding to each electrode channel according to a preset time window to obtain a plurality of time sequence frequency band data, and extracting a plurality of signal features from all the time sequence frequency band data; constructing four-dimensional time sequence feature data based on the extracted signal features; performing fusion feature representation on the four-dimensional time sequence feature data to obtain a fusion feature vector; and inputting the fusion feature vector into a pre-trained cognitive state recognition model to obtain a cognitive state recognition result of the learner. According to the learner cognitive state recognition method and device, the electronic equipment and the storage medium, the cognitive state of the learner can be accurately recognized.

A method of enabling a user to visualize, review and analyze a patient's EEG data, pre-surgical data and post-surgical data, includes generating a unified viewing environment, wherein the unified viewing environment has at least a first view area and a second view area; importing, using the unified viewing environment, pre-surgical data and post-surgical data; acquiring EEG data of the patient; displaying, simultaneously, the EEG data in the first view area and the post-surgical data in the second view area of the unified viewing environment, wherein the EEG data is displayed as a vertical stack of a plurality of EEG traces, and wherein the post-surgical data is displayed without a need to pre-process and co-register with a three-dimensional coordinate system; and automatically associating visual representation of each of one or more electrodes and contacts in the post-surgical data with an EEG trace of the plurality of EEG traces.

The invention relates to the technical field of image processing, and particularly discloses a high-quality video reconstruction method based on electroencephalogram signals, which comprises the following steps: preprocessing electroencephalogram data, down-sampling video frames, and segmenting the electroencephalogram data by using a sliding window technology; selecting a channel with high correlation with video reconstruction; electroencephalogram time features are extracted; electroencephalogram space-time features are extracted; electroencephalogram time-frequency features are extracted; the electroencephalogram time-space-frequency features are fused; extracting the category and semantic information of the electroencephalogram; adopting a comparative learning method to align electroencephalogram-text-picture features; generating a reference video frame by using a stable diffusion model; and generating a high-quality video by using the video diffusion model. According to the method, the information of electroencephalogram features is further deeply extracted, the semantic content of the video is remarkably enhanced, meanwhile, the introduced video diffusion model gradually constructs a clear and natural picture through a step-by-step denoising process, the consistency between video frames is remarkably improved, and therefore the high-quality video rich in details is generated.

The invention relates to an instant denoising method for electroencephalogram signals of students in a classroom scene. The method comprises the following steps: based on electroencephalogram acquisition equipment and a Daisy expansion board, acquiring electroencephalogram signal data of a student through a dry electrode device; and carrying out sliding mean filtering on the electroencephalogram signal data to remove baseline drift, eliminating noise by adopting a band-pass filter to obtain preprocessed electroencephalogram signal data, and inputting the data into a lightweight neural network model to carry out channel-by-channel denoising processing to obtain a denoised electroencephalogram signal. The model comprises a multi-scale feature extraction layer, a time sequence modeling module and a residual connection structure. According to the method, through lightweight neural network design such as multi-scale feature extraction, time sequence modeling and a residual connection structure, the denoising effect is improved, the real-time performance and high efficiency of electroencephalogram denoising processing are guaranteed, and an electroencephalogram data basis with a high signal-to-noise ratio is provided for subsequent teaching quality evaluation.

The invention relates to an obsessive-compulsive disorder diagnosis and symptom degree evaluation system, and belongs to the technical field of medical instruments. The evaluation system comprises a knowledge base module used for storing evaluation questions and used for responding to an evaluation request of a testee and randomly calling the evaluation questions stored in the knowledge base module, the question and answer evaluation module is used for acquiring face video data and electroencephalogram data of the testee in a detection time period and calling a next round of questions according to each round of voice or character answer information of the testee; the acquisition module is used for acquiring face video data and electroencephalogram data of the testee in the detection time period; and the analysis and evaluation module is used for analyzing the compulsive behavior and the compulsive thinking degree of the detected person in the detection time period. According to the invention, optical flow analysis and 3D face reconstruction sentiment analysis and evaluation are carried out based on face video data, the analysis and evaluation process is more efficient, and the analysis and evaluation result is more accurate; according to the method, multi-modal data such as electroencephalogram signals and 3D reconstruction are combined, and the emotion recognition effect can be further improved.

The invention relates to the technical field of electroencephalogram monitoring, in particular to a non-contact electroencephalogram monitoring method and system, and the method comprises the steps: obtaining a corresponding head image of a target user at a current moment; determining a corresponding head pose of the target user at the current moment based on the head image corresponding to the current moment; based on the head pose at the current moment and the head pose corresponding to the previous moment, the pose of an electroencephalogram cap is determined, the electroencephalogram cap comprises a plurality of brain electrodes, and the brain electrodes monitor electroencephalogram data of the target user under the pose corresponding to the electroencephalogram cap. The problems that in the non-contact electroencephalogram collecting process, when the head moves, the collecting position deviates, and signals are not stable can be solved.

The invention provides a cerebral palsy child gastrocnemius spasm curative effect evaluation method and system. According to the system, electroencephalogram data, MR I images, ultrasonic elastography and gastrocnemius spasm curative effect grades are utilized to construct a cross-modal gastrocnemius spasm curative effect evaluation model; the constructed cross-modal gastrocnemius spasm curative effect evaluation model generates the current gastrocnemius spasm curative effect grade of the user according to the real-time electroencephalogram data collected by the server, the MR I images in the selected time period and the corresponding ultrasonic elastography; and according to the current gastrocnemius spasm curative effect grade of the user, flexible rehabilitation adjustment is performed on the user so as to realize a humanized treatment target. According to the invention, the doctor can monitor the rehabilitation training curative effect in real time, the intelligent curative effect evaluation effect is realized, and the life quality of the user is ensured.

The invention provides a teenager depression auxiliary diagnosis model training method and system based on multi-modal data, and relates to the technical field of mental health. According to the method, firstly, a VR intelligent interaction scene is constructed, and physiological data of juvenile depression and physiological data of a normal contrast subject in the VR intelligent interaction scene are synchronously collected; data preprocessing and feature extraction are conducted on the electroencephalogram data, the heart rate variability data and the eye movement data, a single-mode depression recognition model is obtained through classifier training, and then a multi-mode depression recognition model and a cross-mode depression recognition model are obtained. The teenager depression auxiliary diagnosis model obtained through machine learning is helpful for early discovery and timely treatment of teenager depression.

The invention discloses an electroencephalogram data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: preprocessing an original electroencephalogram signal; performing micro-state division on the preprocessed electroencephalogram signals on the five frequency bands respectively; the five frequency bands comprise a delta frequency band, a theta frequency band, an alpha frequency band, a beta frequency band and a gamma frequency band; the micro-states comprise four types of micro-states; performing feature extraction on the micro-state of the electroencephalogram signal under each frequency band; the features comprise a time feature, an occurrence frequency feature and an occupancy feature of each micro-state category, and a transition probability feature of each micro-state; and performing feature screening by using an SVM-RFE model to obtain target features. According to the technical scheme provided by the invention, the airspace information of the electroencephalogram signals in different frequency ranges is considered, the feature classification is more comprehensive, the SVM model and the RFE algorithm are combined, the performance of the model is improved, the calculation time is shortened, the calculation cost is reduced, and the electroencephalogram data recognition effect is improved on the whole.

The invention discloses a disturbance of consciousness electroencephalogram discrimination method and system adaptive to channel deletion. According to the method, on the basis of an electroencephalogram classification model of a deep network, automatic discrimination of disturbance of consciousness (MCS and UWS) is carried out with high accuracy according to resting-state electroencephalogram energy of a patient. The model utilizes a time domain double-branch CNN module and a space-time Transform module to learn features in electroencephalogram data hierarchically; and a model architecture with variable channel dimensions is introduced, an inter-channel association learning method fusing electrode position information and a training strategy of channel random discarding are introduced, so that the model can adapt to a scene in which part of channels are missing, and the clinical applicability of the method is improved.

The invention relates to the technical field of fatigue detection, in particular to a pilot pre-post fatigue accumulation prediction method based on a GCN-T algorithm, and the method comprises the steps: obtaining an original EEG signal of a pilot based on a Stroop fatigue stimulation experiment before the pilot is on the post; the method comprises the following steps: preprocessing an original EEG signal to extract EEG data, inputting the EEG data into a GCN-Transform model for classification, and obtaining the state of a pilot before the pilot is on duty; wherein the state comprises a normal state, general fatigue or severe fatigue; according to the method, the fatigue state of the pilot is predicted by introducing short-time fatigue stimulation and combining the EEG, and the future fatigue risk of the pilot can be evaluated under the condition that normal work tasks of the pilot are not interfered.

The invention discloses a cardiac flow experience closed-loop regulation and control method and system based on sensory movement synchronization. The method comprises the following steps: acquiring initial electroencephalogram data of a user, extracting frequency domain periodic and non-periodic electroencephalogram characteristics related to cardiac flow experience to obtain an initial electroencephalogram characteristic value, randomly presenting rhythmic sensory stimulation at a preset rhythm, and executing corresponding behavior response by the user; calculating a sensory motion synchronization index; according to the sensory motion synchronism index and a preset interval, the sensory motion synchronism index is stabilized in the preset interval by adjusting the rhythm; extracting frequency domain periodic and non-periodic electroencephalogram features again, and calculating the variable quantity of the relative initial value; judging whether the sensory stimulation presentation rhythm is adjusted or not in combination with the variable quantity and a preset standard; and if the rhythm needs to be adjusted, repeatedly executing the previous steps until the variable quantity of each electroencephalogram characteristic value is adjusted to a preset standard. The system can adaptively adjust the interaction content, optimizes the heart stream experience according to the electroencephalogram characteristics of the user, and improves the immersion and pleasure of the user.