Cross-Subject and Cross-Session EEG Emotion Recognition Based on Multisource Structural Deep Clustering

Individual fluctuations and temporal variability of electroencephalogram (EEG) data pose challenges in precisely identifying emotions. Although a model may perform well with data specific to a certain subject or session, the fluctuations in EEG data can significantly impair the model's performance on a different subject or session. To tackle this problem, current approaches synchronize the original and new subject or session feature distributions. Directly matching EEG data across individuals or sessions may undermine the inherent distinguishability due to the heterogeneity in data distribution. Instead of direct alignment, this work utilizes multisource structural deep clustering to identify the inherent structural knowledge of the target itself and regularize it through the distribution of source labels. Furthermore, the method was implemented on the intermediate output utilizing high-confidence features to improve pattern identification in the latent feature space. This led to more distinct differentiations across subdomains with varying labels. Comparative analyses were performed with state of-the-art (SOTA) models on SEED and SEED-IV datasets. The model proposed outperformed other baseline models, reaching an average accuracy of 90.69%/95.05% in a cross-subject/cross session experiment on SEED and 74.35%/78.56% in SEED-IV. This research provides a novel approach to align EEG features without the need for direct distance calculation.