Cross-Subject Emotion Classification with Residual Pseudo-Label Distance-aware Dual-Classifier

Emotion plays a crucial role in information exchange and decision-making processes. Emotion recognition based on electroencephalography (EEG) captures and analyzes brain electrical activity, effectively reflecting emotional characteristics and providing unique advantages for constructing intelligent emotional systems. However, due to significant differences in feature distribution between the source and target domains, traditional models struggle with generalization in cross-subject tasks. To address this issue, this paper proposes a new model for EEG-based emotion analysis that employs the Pseudo-Label Distance-aware Dual-Classifier (PL-DDC) strategy, which relies on a Residual Temporal-Frequency Analysis (RTFA) structure. We name this model Residual Pseudo-Label Distance-aware Dual-Classifier (RPL-DDC). The RTFA module effectively captures mixed dependencies in complex time-frequency signals, while the PL-DDC strategy introduces a distance loss between the source and target domains. By combining classifier output discrepancies and a pseudo-label mechanism, it progressively reduces the feature distribution gap between the two domains, thereby enhancing the model’s classification performance. Experimental results on the SEED and SEED-IV datasets show that the proposed model achieves accuracy of 89.03% ± 5.03 on the SEED dataset and 75.49% ± 10.44 on the SEED-IV dataset. Compared to traditional baseline models, these results validate the effectiveness of the proposed model in cross-subject emotion recognition.