Comprehensive Multisource Learning Network for Cross-Subject Multimodal Emotion Recognition

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 1; pp. 365 - 380
• Main Authors: Chen, Chuangquan, Li, Zhencheng, Kou, Kit Ian, Du, Jie, Li, Chen, Wang, Hongtao, Vong, Chi-Man
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; Correlation; cross-subject; Datasets; domain generalization; Electroencephalography; Emotion recognition; Emotions; Eye movements; Feature extraction; Invariants; Learning; low-rank multimodal fusion; Measurement; multimodal emotion recognition; Multisource learning; Physiological responses; Physiology; Representations; Robustness

Electroencephalography (EEG) signals and eye movement signals, which represent internal physiological responses and external subconscious behaviors, respectively, have been shown to be reliable indicators for recognizing emotions. However, integrating these two modalities across multiple subjects pr...