Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition

• Published in: IEEE transactions on affective computing Vol. 14; no. 3; pp. 2496 - 2511
• Main Authors: Shen, Xinke, Liu, Xianggen, Hu, Xin, Zhang, Dan, Song, Sen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brain modeling; brain-computer interface; contrastive learning; cross-subject; Datasets; EEG; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Learning; Neuroscience; Representations; Stimuli; Testing; Training

EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience...