基于空-时-频多域交叉注意力学习的脑电情绪识别方法OA

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.