GDDN: Graph Domain Disentanglement Network for Generalizable EEG Emotion Recognition

• Published in: IEEE transactions on affective computing Vol. 15; no. 3; pp. 1739 - 1753
• Main Authors: Chen, Bianna, Chen, C. L. Philip, Zhang, Tong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Brain modeling; Computational modeling; Disentanglement learning; EEG connectivity; Electroencephalography; Emotion recognition; Emotional factors; Emotions; generalizability; Graph representations; Graph theory; Graphical representations; inter-subject variability; Modules; Predictive models; Training

Cross-subject EEG emotion recognition suffers a major setback due to high inter-subject variability in emotional responses. Many prior studies have endeavored to alleviate the inter-subject discrepancies of EEG feature distributions, ignoring the variable EEG connectivity and prediction deviation ca...