AI RESEARCH
Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition
arXiv CS.LG
•
ArXi:2509.01135v2 Announce Type: replace Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deployment remains limited by inter-subject variability, reliance on target-domain data, and unavoidable label noise. To address these challenges, we propose a Multi-domain Aggregation Transfer learning framework with domain-class prototypes (MAT) for emotion recognition under completely unseen target domains.