A deep subdomain associate adaptation network for cross-session and cross-subject EEG emotion recognition. (September 2022)

• Record Type: Journal Article
• Authors: Meng, Ming; Hu, Jiahao; Gao, Yunyuan; Kong, Wanzeng; Luo, Zhizeng
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Emotion recognition -- EEG -- Subdomain associate loop -- Transfer learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103873
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The predicted pseudo-labels of samples were used to obtain subdomains in the target domain. Differential Entropy (DE) features extracted from various frequency bands were represented as a set of characteristic matrixes. Subdomain Associate Loop (SAL) was proposed as a domain adaptation loss criterion. Abstract: Developing robust cross-subject or cross-session EEG-based affective models is a key issue in affective brain-computer interfaces, which often suffer from the individual differences and non-stationarity of EEG. Aiming at generalizing the affective model across subjects and sessions, this paper proposes a novel transfer learning strategy with Deep Subdomain Associate Adaptation Network (DSAAN) for EEG emotion recognition. Domain was divided into subdomains according to the sample labels, and the source domain use the true sample labels while the target domain use the predicted pseudo-labels. DSAAN was established as a transfer network by aligning the relevant subdomain distributions based on Subdomain Associate Loop (SAL). The adaptation of networks was achieved by minimizing the summation of source domain classification loss and SAL loss. For the purpose of verifying the generalization of DSAAN, we carried out the cross-session and cross-subject EEG emotion recognition experiments on benchmark SEED and DEAP. Compared with existing domain adaptation methods, the DSAAN achieved outstanding classification results.