EEG-based Cross Subject Emotion Recognition based on collaborative learning and dynamic distribution adaptation

EEG-based emotion recognition has advanced rapidly due to its objectivity and reliability, but individual differences present challenges: subject-specific models underperform on new subjects, and general models lack accuracy. While domain adaptation (DA) algorithms reduce distribution differences between source and target EEG domains, single-source methods struggle with knowledge transfer, and multi-source methods neglect source domain differences. To address this, we propose a collaborative learning and dynamic distributed adaptation algorithm (CL-DDA) for EEG emotion recognition. By dividing the model into two sub-networks, collaborative learning improves generalization. Additionally, local subdomain alignment addresses inter-subject emotion differences, while global domain alignment minimizes marginal distribution disparities. Our model achieved 90.08% and 77.55% accuracy on SEED and SEED-IV datasets, respectively, in cross-subject emotion recognition.