@article{Xie2026, 
author = {Feng Xie and Junjie Yang and Shengli Xie and Kan Xie},
title = {EEG-based Emotion Recognition Using Spatio-temporal-spectral Cross-attention Learning},
year = {2026},
journal = {Journal of Guangdong University of Technology},
volume = {43},
number = {1},
pages = {10-21},
keywords = {emotion recognitions, electroencephalography (EEG), spatio-temporal-frequency multi-domains feature, multi-scale convolution, 3D deep residual network, cross-attention.},
url = {https://www.sciopen.com/article/10.12052/gdutxb.250177},
doi = {10.12052/gdutxb.250177},
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.}
}