广东工业大学学报2026，Vol.43Issue(1)：10-21,12.DOI:10.12052/gdutxb.250177
基于空-时-频多域交叉注意力学习的脑电情绪识别方法
EEG-based Emotion Recognition Using Spatio-temporal-spectral Cross-attention Learning
摘要
Abstract
Electroencephalogram(EEG)-based emotion recognition is an essential intelligent technique for health assessment and clinical intervention.However,EEG signals exhibit complex and complementary non-linear correlations across spatio-temporal-frequency domains,posing significant challenges to effective feature modeling and downstream emotion recognition performance.To address these challenges,an Emotional Spatio-Temporal-Spectral Cross-attention Network(ESTSCA-Net)is proposed.The proposed model adopts a dual-branch feature fusion architecture:in the spatio-temporal branch,a multi-scale 2D convolutional network is designed to sequentially process spatio-temporal information,adaptively capturing the contextual dependencies of neural activities;in the spatio-spectral branch,a 3D bottleneck residual network with channel-wise and cross-frequency attention mechanisms is developed to selectively encode critical spatio-spectral neural oscillations.Furthermore,a bidirectional multi-head cross-attention interaction strategy is introduced to achieve deep fusion of spatio-temporal-spectral features,forming an effective emotion representation classifier.Experimental results on the public DEAP and MEEG datasets demonstrate that ESTSCA-Net can comprehensively extract spatio-temporal-spectral EEG features across different emotional states and consistently outperforms state-of-the-art baseline models in both arousal and valence metrics.关键词
情绪识别/脑电图/空-时-频多域特征/多尺度卷积/3D瓶颈残差网络/交叉注意力Key words
emotion recognitions/electroencephalography(EEG)/spatio-temporal-frequency multi-domains feature/multi-scale convolution/3D deep residual network/cross-attention分类
生物科学引用本文复制引用
谢峰,杨俊杰,谢胜利,谢侃..基于空-时-频多域交叉注意力学习的脑电情绪识别方法[J].广东工业大学学报,2026,43(1):10-21,12.基金项目
国家自然科学基金青年基金资助项目(62003101) （62003101）
广东省自然科学基金资助面上项目(2023A1515011290,2024A1515011701) （2023A1515011290,2024A1515011701）
