Gusa: Graph-Based Unsupervised Subdomain Adaptation for Cross-Subject EEG Emotion Recognition

• Published in: IEEE transactions on affective computing Vol. 15; no. 3; pp. 1451 - 1462
• Main Authors: Li, Xiaojun, Chen, C. L. Philip, Chen, Bianna, Zhang, Tong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Brain modeling; Constraints; Convolution; EEG emotion recognition; Electroencephalography; Emotion recognition; Emotions; graph convolutional neural network; Measurement; metric learning; Modules; Representations; subdomain adaptation; Task analysis
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2024.3349770

EEG emotion recognition has been hampered by the clear individual differences in the electroencephalogram (EEG). Nowadays, domain adaptation is a good way to deal with this issue because it aligns the distribution of data across subjects. However, the performance for EEG emotion recognition is limited by the existing research, which mainly focuses on the global alignment between the source domain and the target domain and ignores much fine-grained information. In this study, we propose a method called Graph-based Unsupervised Subdomain Adaptation (Gusa), which simultaneously aligns the distribution between the source and target domains in a fine-grained way from both the channel and emotion subdomains. Gusa employs three modules, such as the Node-wise Domain Constraints Module to align each EEG channel and obtain a domain-variant representation, the Class-level Distribution Constraints Module, and the Emotion-wise Domain Constraints Module, to collect more fine-grained information, create more discriminative representations for each emotion, and lessen the impact of noisy emotion labels. The studies on the SEED, SEED-IV, and MPED datasets demonstrate that Gusa significantly improves the ability of EEG to recognize emotions and can extract more granular and discriminative representations for EEG.