Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 1637 - 1646
• Main Authors: Pang, Miaoqi, Wang, Hongtao, Huang, Jiayang, Vong, Chi-Man, Zeng, Zhiqiang, Chen, Chuangquan
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Brain modeling; Brain-Computer Interfaces; cross-session; Data collection; Data mining; Data models; EEG; EEG-based emotion recognition; Electroencephalography; Electroencephalography - methods; Emotion recognition; Emotional factors; Emotions; Emotions - physiology; Feature extraction; Female; Human-computer interface; Humans; Invariants; Machine Learning; Male; Neural Networks, Computer; Representations; Robustness; self-supervised learning; Task analysis; transformer
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3389037

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary char...