Prompt-Guided Domain Generalization for EEG Emotion Recognition

Cross-subject electroencephalogram (EEG) emotion recognition is an important task in affective computing, which aims to learn discriminative and generalizable EEG features to reveal emotions across individuals. To achieve robust generalization, existing methods primarily focus on developing domain alignment constraints to learn features that remain consistent across all source domains, while ignoring the potential benefits of domain-specific information in enhancing model discriminability. As a result, these approaches struggle to utilize relevant source domain information to improve predictions for unseen domains. To address this limitation, we propose a novel prompt-guided domain generalization (PGDG) framework that extends the invariance perspective by incorporating domain-specific information through prompt learning. Specifically, the variational autoencoder is first used to extract domain-invariant features, and then the prompt of each source domain is learned by guiding the classifier in the optimal direction. Finally, a multi-head cross-attention mechanism adaptively integrates these domain prompts from multiple source domains to improve the model's generalization ability in target domains. Experimental results on the SEED and SEED-IV datasets show that PGDG outperforms state-of-the-art methods, demonstrating the potential of prompt-guided generalization in improving cross-subject EEG emotion recognition performance.