Joint EEG Feature Transfer and Semisupervised Cross-Subject Emotion Recognition

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 7; pp. 8104 - 8115
• Main Authors: Peng, Yong, Liu, Honggang, Kong, Wanzeng, Nie, Feiping, Lu, Bao-Liang, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Brain modeling; Data models; Domains; Electroencephalogram (EEG); Electroencephalography; Emotion recognition; Emotions; Frequencies; Informatics; joint optimization; Learning; semisupervised regression; Transfer learning
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3217120

Due to the weak and nonstationary properties, electroencephalogram (EEG) data present significant individual differences. To align data distributions of different subjects, transfer learning showed promising performance in cross-subject EEG emotion recognition. However, most of the existing models sequentially learned the domain-invariant features and estimated the target domain label information. Such a two-stage strategy breaks the inner connections of both processes, inevitably causing the suboptimality. In this article, we propose a joint EEG feature transfer and semisupervised cross-subject emotion recognition model in which the shared subspace projection matrix and target label are jointly optimized toward the optimum. Extensive experiments are conducted on SEED-IV and SEED, and the results show that the emotion recognition performance is significantly enhanced by the joint learning mode and the spatial-frequency activation patterns of critical EEG frequency bands and brain regions in cross-subject emotion expression are quantitatively identified by analyzing the learned shared subspace.