Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

• Published in: IEEE transactions on affective computing pp. 1 - 13
• Main Authors: Liu, Ke, Luo, Xin, Zhu, Wenrui, Yu, Zhuliang, Yu, Hong, Xiao, Bin, Wu, Wei
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Adaptation models; adaptive source selection; Adaptive systems; Affective computing; Brain modeling; cross-subject; Decoding; domain adaptation; EEG emotion recognition; Electroencephalography; Emotion recognition; Feature extraction; Target recognition; Transfer learning
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2024.3514635

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://github.com/Pam098/ASJDA .