Dual pseudo-labeling based adversarial domain adaptation for EEG-based emotion recognition

In recent years, unsupervised domain adaptation (UDA) has emerged as a promising approach for constructing cross-subject emotion recognition models. However, most existing UDA methods do not fully exploit class information in the target domain, resulting in a relatively coarse-grained process of domain alignment and a higher risk of incorrect class matching. To address this issue, this paper proposes a novel adversarial training-based domain adaptation framework. The proposed method leverages emotion class prototypes to enhance intra-class correlation between the source and target feature distributions. Meanwhile, soft pseudo-labels generated by prototype clustering are utilized to further improve the inter-class discriminability within each domain. In order to enhance the robustness and quality of hard pseudo-labels in the target domain, a dual pseudo-labeling strategy is introduced. Finally, adversarial training is conducted to achieve a more fine-grained alignment of data distributions across domains. We conduct cross-subject and cross-session evaluations on the SEED and SEED-IV datasets, respectively. Experimental results demonstrate the effectiveness of our method and its advantages over several state-of-the-art UDA approaches. By introducing dual pseudo-labels, our study incorporates additional supervision, enabling a more refined domain adaptation process and significantly improving the generalization capability of EEG-based emotion recognition models.