Unsupervised Domain Adaptation With Pseudo-Label Propagation for Cross-Domain EEG Emotion Recognition

Abstract

Emotion recognition from electroencephalography (EEG) signals is increasingly emerging as a critical research focus in brain-computer interfaces (BCIs). However, challenges such as the scarcity of emotion labels and distribution discrepancies in EEG signals significantly hinder the practical application of EEG-based emotion recognition. To overcome these challenges, this article fully exploits the continuity of emotion-related EEG data and proposes an unsupervised domain adaptation (DA) with pseudo-label propagation (PLP), termed DA method combined with PLP (DAPLP), for cross-domain EEG emotion recognition. Specifically, we first perform global distribution alignment (GDA) between the source and target domains and utilize the source classifier to generate pseudo-labels for the target domain. From these predictions, reliable pseudo-labels are then selected to guide label propagation, and the propagation process is further optimized with correct and smooth techniques. Systematic experiments conducted on the SEED, SEED-IV, and SEED-V datasets reveal that the proposed DAPLP accomplishes competitive performance compared to advanced existing methods, reaching average accuracies of 89.44%/74.57%/69.15% in cross-subject evaluation and 96.41%/82.20%/84.70% in cross-session evaluation, respectively. Moreover, our proposed DAPLP exhibits strong practical potential and robust performance in unsupervised cross-domain emotion recognition.