7623results about "Psychotechnic devices" patented technology

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

The invention relates to the technical field of data processing, and discloses a mental health monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring environmental parameters through a multi-source sensor network, and identifying scene features through an environmental context inference engine; according to the environmental features, extracting physiological features by adopting an adaptive algorithm; analyzing an environment and behavior relationship by using a Bayesian model; fusing the multi-dimensional features through a graph attention network; constructing an environment adaptive baseline based on transfer learning; and evaluating a psychological state and outputting an early warning in combination with multi-level anomaly detection. According to the method and the device, the psychological health state of the user can be accurately monitored in real time under the condition of considering the dynamic change of the environment, and the monitoring deviation caused by individual difference is overcome.

The invention discloses a multi-modal cognitive impairment evaluation system based on a multi-dimensional cognitive function hypergraph. The multi-modal cognitive impairment evaluation system comprises a multi-modal signal acquisition system, a cognitive function evaluation normal form design, a universal feature extraction framework of each modal and online hypergraph learning based on a multi-dimensional cognitive function. The system constructs a dimensionality evaluation normal form containing the cognitive function based on multi-modal physiological and behavior signals, so that comprehensive evaluation of the cognitive function of the elderly is realized. Firstly, physiological and behavior responses of a user under different tasks and situations are recorded and analyzed in real time by fusing multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions. And then, mining fusion representation among the multi-modal signals, generating unified feature representation, introducing an online hypergraph of clinical knowledge, capturing a complex multi-modal data relationship, and providing an analysis result containing cognitive function evaluation. Finally, in combination with the technical means, the system can realize early detection of cognitive impairment.

The invention provides a multi-modal sentiment analysis method and device based on multi-agent collaboration, and the method comprises the steps: carrying out the clock synchronization collection and preprocessing of multi-modal data, and obtaining a multi-modal data package with a time sequence mark; and performing feature extraction on the multi-modal data packet by using a plurality of intelligent agents, and mapping the extracted feature vector and a preset emotion ontology to obtain a multi-modal emotion vector. And detecting the difference degree between the vectors in the multi-modal emotion vectors, and starting a conflict detection algorithm based on feature credibility to perform evaluation when a significant difference is detected to obtain an evaluation result. And performing feature fusion operation on the multi-modal emotion vector according to the evaluation result and a preset weight coefficient to obtain an emotion recognition result, and performing timestamp association storage on the emotion recognition result and the corresponding multi-modal data. By introducing a difference detection and evaluation mechanism, the abnormal feature vector is effectively prevented from influencing the fusion result, and the reliability of modal sentiment analysis is improved.

The invention discloses an exercise and cognition ability assessment method, system and device, and the method comprises the steps: obtaining real-time exercise information, including real-time joint angle information, real-time gait information, real-time attention information and real-time decision speed information, of a user; constructing a physiological-behavior coupling model based on a hidden Markov model, and generating a joint evaluation index by inputting real-time motion information; performing feature extraction on the real-time motion information by using a hierarchical feature extraction model to obtain a motion feature vector and a cognitive feature vector; performing fusion processing on the feature vector and the joint evaluation index, and outputting abnormal state information including motion abnormality and cognitive abnormality; behavior abnormity is detected through the dynamic threshold model, a real-time feedback instruction is generated in combination with a preset feedback rule, and finally user training task parameters are dynamically adjusted according to the instruction. According to the method, through multi-dimensional data fusion and a dynamic evaluation mechanism, cooperative monitoring and self-adaptive training regulation and control of motion and cognitive competence are realized.

The invention discloses a comprehensive regulation and control method and system based on multi-mode electromagnetic and ultrasonic stimulation, and the method comprises the steps: collecting brain wave signals in real time through a multi-electrode EEG system, synchronously collecting skin electroreaction and heart rate variability signals, preprocessing the collected signals, and carrying out the feature extraction; the extracted signal features are subjected to multi-modal feature fusion through a CNN, and the multi-modal features are subjected to time sequence dependency analysis through a long-short-term memory network to generate a real-time emotional state quantization vector and identify a current emotional state category. Through synchronous acquisition and feature fusion of multi-electrode EEG, EDA and HRV signals, a real-time brain function connection network is constructed, accurate recognition and stimulation strategy making are performed on emotion regulation and control nodes based on graph theory analysis, the accuracy and reliability of emotion recognition are improved, personalized regulation and control of individual emotion states are realized, and the emotion regulation and control efficiency is improved. The cognitive function and language expression ability of an individual can be improved, and movement coordination disorder caused by emotional fluctuation is reduced.

The invention relates to the field of mental health assessment, and discloses a depression risk prediction model construction method based on multi-modal sample information, and the method comprises the following steps: collecting and preprocessing physiological signals, voice features and micro-expression action data; performing phase-space reconstruction on the physiological signal; constructing a manifold model, and extracting local geometric features of the multi-modal signals; the short-term dynamic change and the long-term trend of the signals are separated through a multi-scale variational mode decomposition method; performing dynamic weighted fusion on the multi-modal features by adopting a self-attention mechanism to generate a unified high-quality feature vector; and constructing a nonlinear state space model based on the fusion features, optimizing a state transition and output relationship through extended Kalman filtering, and generating a real-time emotional state and depression risk score. The method can accurately capture the dynamic characteristics of the multi-modal signal, significantly improves the accuracy and robustness of depression risk prediction, and provides efficient technical support for mental health assessment and intervention.

The invention discloses a cognitive function rehabilitation training system and method based on virtual reality, and the system comprises a cognitive function test unit which is used for carrying out the cognitive impairment test evaluation of a patient based on a virtual reality module, and generating a cognitive function evaluation report of the patient; the cognitive training unit is used for generating a personalized cognitive training scheme based on the cognitive function evaluation report, carrying out targeted cognitive training in a virtual reality environment, and collecting physiological data and training performance data of the patient in the cognitive training process; the feedback unit is used for comprehensively analyzing the physiological data and the training performance data and generating a staged feedback report; and the rehabilitation effect tracking unit is used for analyzing rehabilitation effects of the patient in different time periods based on the periodic feedback report and optimizing a subsequent cognitive training scheme based on an analysis result. And the interestingness and interactivity of rehabilitation training are improved.

The invention discloses an adaptive processing method, device and equipment for electroencephalogram signals of attention glasses, and the method comprises the steps: obtaining original electroencephalogram signals collected by the attention glasses to generate a standardized signal sequence for interference detection, obtaining a change interval corresponding to signal fluctuation, and constructing an interference mode library; determining a processing interval according to the standardized signal sequence and the interference pattern library, generating a correction sequence according to the processing interval, and constructing an adaptive reference according to the correction sequence; reconstruction is carried out based on a self-adaptive reference, and an electroencephalogram feature sequence and a corresponding time-varying feature set are obtained; obtaining attention indexes corresponding to the time-varying features, and performing feature extraction on the attention indexes to obtain a core feature set; constructing a mapping rule according to the core feature set, generating state features according to the mapping rule, and performing mode construction on the state features; and performing adaptive processing according to a construction result corresponding to the mode construction, generating a state marking sequence, and outputting an adaptive signal according to the state marking sequence.

The invention discloses an ophthalmologic operation real-time navigation system and method based on dynamic visual field tracking, and relates to the technical field of medical systems. Comprising the following steps: matching operation data closest to a current operation from a database, and generating standard operation navigation of the current operation; the real-time image of the operation process is compared with the image of the standard operation navigation in real time, the step of the current operation is determined, and meanwhile subsequent image navigation of the operation of the patient is displayed; generating a spatial mapping matrix between an eyeball movement track and a target region by combining target region boundary data of preoperative OCT image segmentation and an intra-operative navigation coordinate system; the virtual navigation path of the surgical instrument is dynamically corrected according to the real-time eyeball movement track, so that the surgical instrument is aligned with the target area, the spatial relation between the instrument and the target area can be visually mastered through the overlapping display of the three-dimensional virtual path and the real-time image, unconscious displacement or physiological tremor of a patient in an operation is actively adapted, and the operation accuracy is improved. And the continuous accuracy of the navigation path is ensured.

The invention relates to the field of flight training data processing, in particular to a flight training regulation and control method and system, and the method comprises the steps: collecting a physiological signal, an electroencephalogram signal and a visual signal of a pilot, and generating comprehensive feature data through multi-modal feature fusion; time dependence analysis is carried out through a recurrent neural network, a comprehensive pressure trend is generated in combination with historical pressure data, the psychological pressure of the pilot is further evaluated through a deep learning model, and the training intensity is adjusted based on real-time feedback data; in the virtual training, the situation is dynamically adjusted based on reinforcement learning, and a personalized training scheme is generated in combination with personalized analysis, so that the training effect of the pilot is optimized; according to the invention, comprehensive monitoring and personalized training regulation and control of the physiological state and psychological stress of the pilot are realized, and the adaptability and safety of flight training are improved.

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

The invention provides a system platform for psychological assessment and emotion feedback. The system platform comprises a multi-source data acquisition module, an emotion data analysis module, a psychological state assessment module, an intervention scheme generation module, an emotion change feedback module, a scheme optimization module and a data security protection module. The emotion recognition accuracy is improved by synchronously acquiring physiological-behavior-environment data and combining with a dynamic feature fusion method, a personalized intervention strategy is generated based on a reinforcement learning algorithm, real-time feedback is realized, and a data security protection system is constructed by adopting federal learning and homomorphic encryption technologies, so that the data security is ensured.

The invention provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

The invention belongs to the technical field of biological calculation, and discloses an intelligent music regulation and control system and method based on electroencephalogram signal multi-modal feature recognition, electroencephalogram data are obtained through anti-interference collection and adaptive filtering processing, multi-modal feature vectors are constructed based on physiological feature extraction and information fusion, and the multi-modal feature vectors are extracted and subjected to multi-modal feature recognition. And accurate recognition of the emotional state and cognitive load evaluation are realized. A music-emotion mapping network is constructed through music feature analysis and emotion semantic matching, a personalized music regulation and control strategy is formulated, music parameter dynamic adjustment and audio stream reconstruction are realized, and a music output stream is generated. A closed-loop optimization feedback mechanism is formed by adopting electroencephalogram feedback collection and adjustment effect evaluation, user data security grading and privacy protection processing are implemented, a security specification database is constructed, the effect of a music output stream is continuously evaluated, and a comprehensive evaluation report is generated. Immediate emotion regulation is provided for the user, a healthier emotion management mode is established for the user through long-term data accumulation, and psychological toughness is improved.

The invention relates to the field of deep learning and emotion recognition, in particular to an EEG emotion recognition system and method based on multi-scale space-time diagram convolution and comparative learning, and the method comprises the steps: carrying out the preprocessing of an EEG signal; constructing positive and negative sample pairs based on the emotion categories and the identities of the subjects; extracting hidden space representation of the electroencephalogram signal by adopting a stack auto-encoder, and dividing the electroencephalogram signal into a plurality of functional brain regions to obtain a brain source signal after dimension reduction; constructing an undirected graph structure of the brain source signal; constructing a multi-scale space-time diagram convolutional network as a feature extraction network, inputting an undirected graph structure, dynamically learning and updating an adjacent matrix, expressing features of brain source signals in the undirected graph structure, and extracting emotion related features; based on a multi-scale space-time diagram convolution and comparative learning framework, an EEG emotion recognition model is built and trained to eliminate individual differences. According to the method, the multi-dimensional information of the EEG signals is fully utilized, and the accuracy, robustness and generalization performance of emotion recognition are remarkably improved.

The invention provides a dynamic calibration method and system for a vehicle-mounted emotion recognition system, and the method comprises the steps: S1, obtaining multi-source data which comprises a facial image, a voice signal and a physiological signal; the obtained multi-source data are preprocessed, and preprocessed multi-source data are obtained; s2, performing feature extraction based on the preprocessed facial image, the voice signal and the physiological signal to obtain a facial expression feature vector, an audio feature vector and a physiological state feature vector; s3, evaluating the current environment credibility based on an environment credibility evaluation function; s4, dynamically distributing the weight of the multi-source data according to the credibility of the current environment and the real-time scene; and S5, constructing a multi-modal fusion vector based on the dynamically distributed weight of the multi-source data, the facial expression feature vector, the audio feature vector and the physiological state feature vector, and performing emotion recognition by using the constructed emotion recognition model based on the multi-modal fusion vector.

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 relates to the technical field of psychological health monitoring, in particular to a self-injury behavior early warning and intervention method and system based on multi-modal physiological data and AI.The method comprises the steps that physiological signals, behavior data, text data and environment parameters of a user are collected in real time based on a contact sensor and non-contact sensing equipment, and the physiological signals, the behavior data, the text data and the environment parameters of the user are obtained; generating a multi-source original data set; performing time synchronization and spatial correlation analysis on the multi-source original data set to form a fusion feature vector; inputting the fusion feature vector into a preset multi-modal deep learning model, obtaining a self-injury behavior risk score value, and dividing risk levels based on the self-injury behavior risk score value; and triggering a hierarchical intervention strategy of AI psychological counseling, emergency contact notification and medical resource linkage according to the risk level. The method has the effect of improving the precision and quality of teenager mental health assessment and self-injury behavior early warning results.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to the field of man-machine interaction training, virtual reality and intelligent evaluation, and particularly discloses a psychological exercise ability training method and system based on mixed reality and behavior analysis. The method comprises the steps that a multi-scene psychological training environment is constructed and presented in mixed reality equipment, action behavior data, eye movement path data and operation response time data of a pilot in the training process are collected, multi-modal original behavior characteristics are constructed, and then a psychological exercise ability label set is constructed; and realizing capability-task modeling through matching of a capability label and a task structure, generating a personalized training path and a capability difference atlas based on difference analysis with a preset capability standard, adjusting task parameter configuration data, dynamically updating a training scene, generating a capability evolution trajectory, and outputting a training analysis result. In this way, accurate ability modeling, task dynamic adaptation and visual tracking of the training effect can be achieved, and the individuation and intelligence level of psychological exercise ability training is remarkably improved.

Bending data is used to facilitate tracking operations of an extended reality (XR) device, such as hand tracking or other object tracking operations. The XR device obtains bending data indicative of bending of the XR device to accommodate a body part of a user wearing the XR device. The XR device determines, based on the bending data, whether to use previously identified biometric data in a tracking operation. A mode of the XR device is selected responsive to determining whether to use the previously identified biometric data. The selected mode is used to initialize the tracking operation. The selected mode may be a first mode in which the previously identified biometric data is used in the tracking operation or a second mode in which the previously identified biometric data is not used in the tracking operation.

The invention discloses a nerve regulation system for cognitive impairment, and the system comprises a cognitive behavior evaluation module which is used for recognizing a cognitive defect field which needs to be improved by a target object; the image data acquisition module is used for acquiring and preprocessing task state nuclear magnetic data and T1 weighted structure images of a target object; the brain region activation analysis module is used for determining a neural regulation potential target spot according to the preprocessed task state nuclear magnetic data; the brain network analysis module is used for identifying key nodes related to cognitive defects; the stimulation target screening module is used for determining an individualized stimulation target of the target object according to the nerve regulation potential target and the key node of the target object; the time domain interference electrical stimulation simulation and implementation module is used for performing electrical stimulation on the individualized stimulation target spot; and the dynamic feedback adjustment module is used for dynamically adjusting the stimulation parameters and enhancing the personalized regulation and control effect. By adopting the method and the device, the specific brain region directly related to the cognitive impairment can be determined, so that the targeting and the accuracy of treatment are enhanced.

The invention relates to the technical field of health monitoring, in particular to a health monitoring system based on wearable equipment, which comprises a data acquisition module, a health data analysis module, an emotion and sound analysis module and a health prediction feedback module. According to the health monitoring system, the prediction accuracy of the health monitoring system is effectively improved by combining time sequence analysis with a processing strategy of environmental factors, and more accurate health condition early warning, disease prevention and management strategy optimization and emotion and sound analysis integration are provided by comprehensively analyzing long-term health data and real-time environmental changes of the user, so that the user experience is improved. The emotion change is clarified through acoustic feature analysis, the understanding of the emotion state of the user is improved, the health monitoring dimension is enhanced, the participation degree and satisfaction degree of the user are improved through personalized health suggestion and real-time feedback, the practicability and effectiveness of the system are enhanced, and health management is more accurate and has interactivity.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

Methods, systems, and computer programs are described for using a using an LLM-based conversation engine to treat a mental health disorder. The method includes obtaining data corresponding to a communication from a user device, determining a current user context based on the obtained data, determining based on the determined user context, whether to invoke the LLM, and based on a determination to invoke the LLM: determining a prompt that is to be provided to the LLM based on the determined user context, providing input data to the LLM, the input data comprising (i) the obtained data and (ii) the determined prompt, obtaining output data, generated by the LLM based on the LLM processing the provided input data, indicating a classification of the provided input data, and using the obtained output data to determine a therapeutic treatment for the user.

The invention discloses an emotion recognition method based on EEG space-time frequency feature fusion, which comprises the following steps: S1, carrying out a series of preprocessing steps on an original EEG signal, including baseline correction, standardization and time window addition; s2, performing form conversion on the electroencephalogram signals processed in the S1 to obtain spatial information and time-frequency information of the electroencephalogram signals; s3, multi-aspect feature extraction is carried out; s4, fusing the extracted emotion features, and performing multi-feature emotion recognition; s5, further extracting time features of the electroencephalogram signals by using BILSTM added with a channel attention mechanism, and performing deep fusion of the features; and S6, obtaining an emotion classification result. According to the emotion recognition method based on EEG space-time-frequency feature fusion, feature information of the time dimension, the frequency dimension and the space dimension of the EEG signals is fully considered, the highest recognition accuracy is achieved on the awakening dimension and the titer dimension, the emotion recognition accuracy and robustness are improved, and the effectiveness of the method is verified.