1015 results about "Emotion recognition" patented technology

The invention discloses a Transform-based cross-modal fusion multi-modal emotion recognition method and device, which are used for solving the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in a multi-modal emotion recognition task, and the method takes the accuracy and robustness of emotion recognition as performance evaluation indexes. Firstly, feature information of three modes of vision, voice and text is obtained, feature extraction is performed on each mode through a deep learning model, then features of different modes are fused by using a cross-mode Transform module, and a complex dependency relationship between the modes is dynamically modeled through a multi-head self-attention mechanism, so that more accurate emotion recognition is realized, and the emotion recognition efficiency is improved. And finally, performing emotion prediction on the fused features based on time sequence modeling and an emotion classification module. According to the method, the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in multi-modal emotion recognition can be effectively solved.

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention discloses a robot anthropomorphic interaction method based on multi-modal emotion recognition and customized portrait generation. The method comprises the following steps: S1, dynamically fusing multi-modal emotions; the method comprises the following steps: S1, synchronously acquiring voice, visual and text signals through a multi-source heterogeneous sensor, capturing a user voice stream by a high-fidelity microphone array, and extracting acoustic characteristics such as intonation and speed, S2, performing cross-modal reasoning; s3, synchronously generating contents; step S4: style migration; step S5, anthropomorphic voice and expression generation; according to the method, man-machine interaction emotion is analyzed and generated by utilizing a large language model and multi-modal information fusion, the singleness of interaction emotion and the deficiency of emotional sharing ability are avoided, a strong emotion interaction characteristic is achieved, the image of the robot is obtained through a generative technology and can be migrated to any image, the limitation that a specific image is independently made is broken through, and the interaction effect of the robot is improved. The advantage that one robot can be suitable for different scenes is achieved.

The invention discloses a feature attention and bilinear gating fused speech emotion recognition method and device, and the method comprises the following steps: 1, collecting an audio file, obtaining corresponding label information, generating audio waveform and time frequency representation data through preprocessing, and constructing an audio waveform mask and a time frequency mask to mark an effective information region; 2, constructing a dual-path feature extraction module which comprises a time-frequency feature extraction module and a pre-training acoustic feature coding module; wherein the time-frequency feature extraction module models emotion correlation through local convolution and a multi-dimensional attention mechanism, and performs global time sequence modeling based on a bidirectional gated loop network; the pre-training acoustic feature coding module extracts high-level speech representation with high expression ability for emotion distinguishing by using a pre-training model; and step 3, constructing a feature fusion module and an emotion classification module, and combining with a dual-path feature extraction module to form a speech emotion recognition model.

The invention discloses a psychological accompanying method based on multi-modal emotion recognition, and belongs to the technical field of psychological health services, and the method comprises the steps: S1, synchronously collecting physiological signals, voice features, facial expressions and interactive behavior data of a user through a multi-modal sensor; s2, performing fusion analysis on the multi-modal data by using a deep learning model, and identifying a current emotional state and an emotional intensity level of the user; and S3, dynamically generating an adaptive psychological accompanying intervention scheme based on a preset emotion-intervention strategy mapping rule in combination with historical emotion data and personalized preferences of the user. According to the method, the physiological signals, the voice features, the facial expressions and the interactive behavior data are synchronously collected through the multi-modal sensor, the deep learning model is used for fusion analysis, and compared with single-modal recognition, the emotion state and the intensity level of the user can be judged more comprehensively and accurately, the emotion misjudgment risk is reduced, and a reliable basis is provided for subsequent intervention.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a context information-based voice emotion recognition method, device, equipment and medium, which comprises the following steps: receiving an original voice stream and generating an independent voice segment, recognizing a text and determining a speaker role type, and extracting an acoustic feature index; and generating a preliminary emotion label, generating context information in combination with the historical dialogue text, and inputting the context information, the preliminary emotion label, the speaker role type and the acoustic feature index into a multi-modal fusion module to generate an emotion judgment result. According to the method, multi-modal fusion is realized on the basis of context information by combining voice, text and role information, so that the emotion change of each role can be accurately recognized and understood in a complex dialogue scene, the problems of large single sentence emotion judgment error and neglect of the context information in a traditional method are avoided, and the accuracy and stability of emotion recognition are effectively improved.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The invention provides a digital human generation method based on a multi-modal large model. The method comprises the following steps: constructing a digital human basic model; generating a structured training set; generating a question and answer model supporting multi-channel interaction; semantic answers of the user questions are output, text emotional tendencies of the semantic answers are extracted, and emotional intensity parameters are output; generating facial muscle movement track data, and performing real-time rendering on the digital human basic model according to the facial muscle movement track data to output a digital human three-dimensional image with emotion expression. According to the embodiment of the invention, cross-modal alignment is carried out on text, image and audio data, and a multi-modal large model containing visual, voice and knowledge models is optimized by using a joint training method, so that more natural and smoother multi-channel interaction experience is realized; in addition, by introducing an emotion recognition model and a face interaction model, the emotion tendency contained in the semantic answer can be captured and reflected more accurately, so that a digital human three-dimensional image with real emotion expression is output.

The embodiment of the invention provides a digital human interaction system and method based on multi-modal emotion recognition, and belongs to the technical field of digital human interaction. The system comprises a multi-modal sensing module used for collecting multi-modal data and preprocessing the multi-modal data to generate a standardized data stream; the cross-modal fusion and emotion recognition module is used for carrying out interactive modeling on the multi-modal features and outputting a current emotion label and emotion intensity; the reaction planning module is used for generating a composite reaction strategy; and the digital human rendering module is used for mapping the composite reaction strategy into control signals corresponding to the voice, the facial expression and the action respectively, and driving a digital human to execute corresponding voice output, facial expression change and limb action through the control signals so as to realize interaction. According to the method, multi-modal data are deeply fused through the cross-modal graph neural network and comparative learning, the weight is dynamically adjusted in combination with the modal confidence, and the emotion recognition accuracy and robustness are improved.

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

The invention discloses a multi-modal emotion fusion analysis method and system, and the method comprises the steps: carrying out the feature extraction of multi-modal emotion data modal by modal through a feature extraction module, and generating a text original feature, an audio original feature and a visual original feature; performing cross-modal alignment interactive fusion on the original text features, the original audio features and the original visual features based on a unified semantic alignment module, and constructing collaborative fusion features; performing mode and channel double-layer dynamic fusion optimization by adopting a dynamic fusion regulation and control module according to the text original feature, the audio original feature, the visual original feature and the collaborative fusion feature, and determining a unified fusion feature; performing hierarchical residual semantic gating enhancement based on the unified fusion features according to a high-order semantic abstraction module to generate semantic enhancement features; and inputting the semantic enhancement features into an emotion prediction module, and outputting an emotion analysis result. Based on the above scheme, a more stable, accurate and reliable emotion recognition result can be provided.

The invention relates to the technical field of natural language processing, in particular to a multi-modal dialogue emotion recognition method and system based on cross-modal fusion and comparative learning. According to the method, learnable residual scaling and pre-normalization are introduced through a cross-modal encoder, deep interaction of texts, voices and visual modals is stabilized, and gradient explosion is inhibited; a dialogue graph fusing semantic similarity and time proximity is constructed online through a semantic-time sequence graph enhancement module, and a graph attention network is used for explicitly modeling long-distance round dependence and cross-speaker emotion transmission; through an adaptive comparison and alignment module, a dynamic scheduling comparison loss and index moving average updated mode-emotion prototype library is adopted to realize data distribution adaptive cross-mode alignment; through cooperative work of the modules, the problems that in the prior art, cross-modal fusion is unstable, long-distance and cross-speaker dependence modeling is insufficient, and cross-dataset alignment capacity is weak are solved.

The embodiment of the invention provides an elderly emotion recognition method and device based on multi-modal perception, and the method and device achieve the optimization and enhancement of the signal quality through innovatively constructing a multi-modal data preprocessing mechanism and integrating the facial expression, voice and posture features. And designing a personalized feature mapping model based on historical emotion expression data, and establishing an adaptive feature fusion strategy for intelligent matching in combination with a cross-modal attention network. A hierarchical time sequence classification mechanism is introduced, dynamic modeling of the emotional development trend is realized through a long-short term memory network, and accurate prediction of the emotional state is supported. According to the method, the defects of the traditional technology in the aspects of multi-modal processing, personalized modeling, time sequence analysis and the like are effectively overcome, and the accuracy and reliability of sentiment recognition of the old people are remarkably improved.

The invention relates to the technical field of computers, in particular to a multi-modal model fusion-based water decision-making system, which comprises a virtual digital human interaction module, an intelligent agent business middle table and a large model technology base, and is characterized in that the virtual digital human interaction module is used for monitoring a voice instruction of a user; the method comprises the following steps: acquiring a voice instruction of a user, acquiring biological characteristic data of the user by using a multi-modal sensor, identifying relevance characteristics between the voice instruction and the biological characteristic data through a water conservancy emergency scene emotion identification model, acquiring indication information, and sending the indication information to an intelligent agent service middle station; the agent business middle platform performs task chain analysis on the indication information based on a water conservancy professional term intention recognition model to obtain different task requirements, and calls a large model technology base to make a decision based on the different task requirements to obtain decision information; a water conservancy large model trained based on a domain mechanism model library is integrated on the large model technology base. According to the invention, more efficient and intelligent services can be provided.

The invention relates to the technical field of emotion recognition, in particular to an odor-induced EEG emotion recognition method and system based on a space-time stratified attention mechanism, and the method comprises the steps: presenting odor stimulation to a testee through an olfactory stimulation device, and synchronously collecting an EEG signal and a free association voice text of the testee; performing semantic analysis on the free association voice text, and extracting keywords and corresponding emotion dimension tags; carrying out space-time hierarchical feature extraction on the EEG signal; constructing a space-time hierarchical attention mechanism model; and on the basis of the weighted spatial-temporal features and semantic tags, node connection weights of the individualized knowledge graph are updated through a graph neural network, and an emotion recognition result is generated. According to the method, spatial attention and time attention are combined, key spatio-temporal characteristics are dynamically focused, the limitation of a fixed weight of a traditional method is avoided, and the pertinence and integrity of neural signal representation are remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal emotion recognition and interaction adjusting system and method based on uncertainty evaluation, and solves the defects of uncertainty processing, robustness of interaction strategies, complementary information mining degree among modals and the like in the man-machine interaction process in the prior art. Depth application finiteness is caused by lack of modeling for feature uncertainty after fusion. The uncertainty of an emotion recognition result is quantified through technologies such as multi-modal feature fusion and Bayesian neural network / model integration, an interaction strategy is dynamically adjusted according to an uncertainty score, emotion clarification or conservative response is triggered in a high-uncertainty scene, the robustness of human-computer interaction and the user experience are improved, and the user experience is improved. The method is suitable for intelligent customer service, government affair consultation, medical inquiry and other scenes with high requirements for emotion interaction accuracy.

The invention discloses a psychotherapy and healing robot based on high human emotion fitting degree simulation analysis, and the robot comprises a multi-mode perception layer which is used for collecting the interaction data of physiology, movement and environment; the multi-modal sensing layer comprises a heterogeneous data acquisition module, a spatial-temporal feature extraction network and an attention fusion mechanism module; the dynamic decision-making layer is used for generating an intervention strategy based on the interaction data; the dynamic decision-making layer comprises a reinforcement learning strategy engine and a hierarchical intervention selection tree; the generative interaction layer is used for generating a co-estrus response conforming to ethical specifications based on the intervention strategy; the generative interaction layer comprises an ethical constraint system and an emotional response generator; the brain science verification layer is used for monitoring neural feedback in real time through EEG and adjusting an intervention strategy; and the brain science verification layer comprises a neural feedback regulation module and a multi-mode feedback design module. Therefore, a precise and personalized psychological intervention decision closed loop is provided, and the defects of an existing AI psychological product in the aspects of emotion recognition, intervention strategies and effect quantification are overcome.

Disclosed are a multi-mode emotion recognition method, a system, an electronic device, and a storage medium. The method includes obtaining a spectrogram of a voice to be recognized and a corresponding text and inputting the spectrogram and the text into a multi-mode emotion recognition model to obtain an emotion recognition result output by the multi-mode emotion recognition model. The multi-mode emotion recognition model is trained based on a sample spectrogram, and a corresponding sample text, and a sample emotion recognition result, and is configured to extract a feature from the spectrogram and the text by a self-attention mechanism to obtain the voice features and the text feature, fuse the text feature and voice feature to obtain a multi-mode fusion feature, and make an emotion classification decision to obtain an emotion recognition result based on the text feature, the voice feature, and the multi-mode fusion feature.

The invention discloses an emotion recognition method, system and device based on multi-modal adaptive fusion and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: selecting a pre-training model, respectively extracting the original features of an audio and a video, carrying out the preliminary extraction of the audio through a convolution layer, carrying out the multi-module processing of the video, and keeping the time sequence information. Constructing an attention module to generate an attention matrix and interaction features, and adjusting the original features by using the matrix; and inputting the weighted and fused features into a convolutional network to extract advanced time sequence features, performing pooling compression on the advanced time sequence features in a time dimension, splicing audio and video features, and finally sending the spliced audio and video features into a full-connection layer classifier to obtain an emotion classification result. According to the method, the weights of different features can be dynamically adjusted, so that the audio and visual features are effectively fused, the accuracy and robustness of emotion recognition are improved, the weighted recall rate and the unweighted recall rate are remarkably improved, and the method has high calculation efficiency and expandability.

The invention discloses a multi-modal emotion recognition method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring text, video and audio data of a user and respectively performing feature extraction to obtain text features, audio features and facial features; the three features are input into a pre-trained multi-modal emotion recognition model, the multi-modal emotion recognition model comprises a first fusion module, a second fusion module, a third fusion module, a fourth fusion module and a classification module, the audio features and the text features are fused through the first fusion module, and audio text features are obtained; fusing the facial features and the text features by using a second fusion module to obtain facial text features; performing feature enhancement on the text features by using a third fusion module to obtain enhanced text features; fusing the audio text features, the face text features and the enhanced text features by using a fourth fusion module to obtain multi-modal features; and classifying the multi-modal features by using a classification module to obtain a sentiment classification result of the user.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention discloses a multi-modal sentiment analysis method based on a gating circulation hierarchical fusion network, which comprises the following steps: S1, multi-modal data preprocessing: collecting text, audio and video data, carrying out preprocessing and time sequence alignment, and constructing an annotation data set; s2, constructing a gating circulation hierarchical fusion network: designing a three-level network architecture comprising a modal feature extraction layer, a gating fusion layer and an emotion recognition layer; s3, model training and optimization: adopting an Adam optimizer, taking a cross entropy loss function as a target, carrying out iterative training on the labeled data set, and inhibiting overfitting through Dropout regularization and learning rate attenuation; s4, emotion analysis: inputting data acquired in real time into the trained model, analyzing the emotion state of the user in real time through a feature extraction layer, a gating fusion layer and an emotion recognition layer, and outputting an emotion analysis result; according to the method, information of different modes can be fully utilized in the sentiment analysis task, and the sentiment recognition accuracy is improved.

A method of emotion recognition in cross-subject EEG signals, belonging to technical field of deep learning, includes the following steps: S1, constructing the extracted DE features into positive and negative samples by using a positive and negative sample generator; S2, sending the DE features of an anchor and the positive and negative samples into the encoder for coding, mapping the DE features to a latent space, performing regression prediction on the encoded anchor samples in the latent space by using an autoregressive model, training the encoder by using a probability supervision contrastive loss function; and S3, connecting the trained encoder to the classifier for fine tuning, and training the classifier through the cross entropy loss function; in this process, the encoder does not perform gradient propagation to complete cross-subject emotion recognition.

The invention discloses a multi-scene self-adaptive man-machine interaction system and method based on emotion recognition, relates to the technical field of man-machine interaction, and solves the technical problems of realizing fusion perception of multi-modal emotion features and improving the accuracy of emotion judgment in complex scenes. According to the method, facial, voice and text emotion features are extracted by adopting a multi-modal fusion technology, the limitation of single-modal recognition is solved, an emotion-scene association rule base and a user portrait are constructed, real-time scene classification is combined, accurate mapping of emotions, scenes and demands is realized, one-step interaction of strategies is avoided, and the user experience is improved. Language interaction adaptation is designed from the form, content and style three-dimensional degree, it is ensured that languages are natural and fit scenes, functional response adaptation improves efficiency through priority ranking and execution mode optimization, environment linkage adaptation is combined with user emotion dynamic adjustment directions, collaborative linkage of languages, functions and environments is achieved, and strategy splitting is avoided.

The invention discloses a voice quality inspection method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to voice quality inspection scenes in the fields of finance, health medical care, old-age care and the like. The multi-modal feature fusion technology is introduced, the context semantic features of the text and the acoustic features of the voice are extracted, the emotion features are obtained by combining the pre-trained emotion recognition model, comprehensive understanding of the voice data from the three dimensions of semantics, acoustics and emotions is achieved, the deep fusion of the three feature vectors is carried out, and the voice recognition efficiency is improved. Compared with a traditional method which only depends on text or acoustic features, the method has the advantages that multi-modal features are realized by combining emotional features on the basis of the text or acoustic features, and information contained in voice content can be reflected more comprehensively and meticulously, so that the accuracy and practicability of voice quality inspection are improved, and the voice quality inspection efficiency is improved. And the requirements of application scenes such as intelligent customer service and voice auditing on high-quality automatic quality inspection are met.

The invention relates to the technical field of game development and artificial intelligence, and discloses a Qt interactive game role action response control method fused with AI emotion recognition, which comprises the following steps: S1, acquiring multi-modal data such as facial expression, voice intonation and limb action of a player by using sensors such as a camera and a microphone on game equipment; s2, the collected multi-modal data are transmitted to an AI emotion recognition module, a deep learning algorithm is adopted to analyze and process the data, and the current emotion state of the player is recognized; and S3, obtaining current game scene information and other operation instructions input by the player through the Qt framework. According to the Qt interactive game role action response control method and system fused with AI emotion recognition, through the AI emotion recognition technology, a game role can sense the emotion state of a player and make the emotion interaction action matched with the game role, the emotion resonance between the game role and the player is enhanced, and the interaction experience and immersion of a game are greatly improved.

The invention provides a missing modal emotion recognition method and system based on cross-modal attention fusion, and belongs to the technical field of emotion recognition, and the method comprises the steps: obtaining multi-modal data of a to-be-recognized emotion; marking a missing mode indication for the missing mode of the multi-mode data, and carrying out feature preprocessing to obtain a primary feature and a preprocessing feature; performing feature extraction on the primary features of each mode to obtain advanced features; a cross-modal attention fusion layer is utilized to calculate attention scores between the modals for the advanced features of all the modals, weighted summation is carried out on the advanced features according to the attention scores, meanwhile, the fusion degree of the features of all the modals is dynamically adjusted through a gating fusion mechanism, and preliminary fusion features are obtained; fusing the preliminary fusion features and the pre-processed features through a residual connection module to obtain full-modal features; and performing emotion recognition on the full-modal features by adopting a deep classification network. According to the method, the mutual relationship among different modal data is learned to realize effective identification of multi-modal emotion.

The invention discloses a self-adaptive multi-expert cooperative multi-mode emotion recognition method and related equipment, and the system comprises a feature extraction module which is used for extracting the primary feature representation of three modes of images, voices and characters in the emotion; the uncertainty perception self-adaptive expert distribution mechanism module is used for quantifying the uncertainty level of each modal and obtaining the stable representation of each modal feature; the reliability level emotion decoding module is used for realizing self-adaptive modal fusion based on uncertainty and fusing multi-modal features in sequence according to a reliability priority; and the emotion intensity prediction module is used for performing intensity prediction on the emotion by using a multi-layer perceptron. According to the method, uncertainty in an emotion modal sample is mined and quantified, so that self-adaptive multi-expert cooperative multi-modal emotion recognition based on uncertainty driving is realized; and more stable emotion feature representation is extracted in combination with uncertainty, and a more efficient multi-modal fusion strategy is proposed, so that the accuracy of multi-modal emotion recognition is improved.

The invention discloses a multi-language emotion recognition method based on semantic comprehension, which comprises the following steps: realizing cross-language emotion analysis through a three-stage innovation framework, and separating semantic and emotion features by adopting improved relative position coding; the method comprises the following steps of: constructing an anchor point vector by utilizing a Hofstep culture dimension, and realizing culture sensitive vector space alignment through a loss function; the emotional features are dynamically adjusted in combination with the power distance and personal difference, and the cross-culture adaptability is enhanced in cooperation with an emotional intensity quantification formula. In a SemEval-2023 task, the accuracy of the system is 21.6% ahead of the baseline at 87.3%, the recognition rate of the small language reaches 78.2%, and the culture misjudgment rate is reduced by 43%. And through a multi-language parallel corpus and a mixed data enhancement strategy, the reasoning speed is increased by three times while the model parameter quantity is reduced by 40%, and an efficient solution is provided for a cross-culture NLP task.

The invention belongs to the field of artificial intelligence and electroencephalogram emotion recognition, and provides a double-branch electroencephalogram emotion recognition method and system based on brain region topology and time-space, and the method comprises the steps: preprocessing a to-be-recognized electroencephalogram signal to obtain a plurality of electroencephalogram fragments, and extracting a difference entropy sequence of each electroencephalogram fragment and a Spearman correlation coefficient matrix between channels; based on the Spearman correlation coefficient matrix, utilizing a bridging dynamic graph attention network module to extract topological features of a brain region; processing the differential entropy sequence by using a multi-scale space-time mixed attention module to obtain multi-scale space-time features; carrying out residual mutual cross attention fusion on the topological features of the brain region and the multi-scale spatial-temporal features to obtain fusion features; and performing classification based on the fusion features, and determining an emotion recognition result corresponding to the electroencephalogram signal. According to the method, the accuracy and robustness of emotion recognition are improved by utilizing the spatial topology characteristics and the multi-topology time dynamic characteristics of the electroencephalogram signals, and the defects of modeling spatial dependence and time dynamic are overcome.