A Supervised Contrastive Variational Autoencoder with Probabilistic Latent Alignment for Cross-Domain EEG Emotion Recognition

Cross-domain emotion recognition based on electroencephalogram (EEG) is a challenging task, as EEG signals collected from different subjects or at different moments exhibit significant differences in distribution. How to enable deep learning model to learn the common feature space and reduce the distribution differences between the source and target domains is an important research direction. For this problem, we propose a Supervised Contrastive Variational AutoEncoder Network (SCVAE-Net), which possesses enhanced abilities for extracting consistent features across source and target domains, thereby improving cross-domain EEG emotion recognition performance. Specifically, this method utilizes the reconstruction mechanism and latent space probabilization of VAE to obtain intermediate features that are more consistent and transferable. Furthermore, the maximum mean discrepancy loss is employed to further reduce the distribution discrepancy of these features. To alleviate the degradation of discriminative ability during domain alignment, we introduce multi-view supervised contrastive learning in multi-source domains to enhance the intra-class consistency and inter-class separability of latent features. Under the cross-subject and cross-session settings, SCVAE-Net achieves accuracies of 95.01%/96.84% on SEED and 74.94%/79.44% on SEED-IV, respectively. These experimental results demonstrate the effectiveness of the proposed method in cross-domain EEG emotion recognition.