An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition. (September 2020)

• Record Type: Journal Article
• Authors: Yan, Mengmeng; Lv, Zhao; Sun, Wenhui; Bi, Ning
• Is Part Of: Medical engineering & physics. Volume 83(2020)
• Page Start: 130
• Page End: 141
• Publication Date: 2020-09
• Subjects: Common spatial pattern (CSP) -- Emotion recognition -- Joint approximation diagonalization (JAD) -- Channel selection; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2020.05.006
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Electroencephalographic spatial features are used for emotion recognition. Design automatic eigenvalue selection to improve the Common Spatial Pattern method. A channel optimization strategy is proposed to decrease the computation load. The proposed method achieves superior results than the traditional method. Abstract: Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalography (EEG)-based emotional HCI, this paper proposes an improved common spatial pattern combined with a channel-selection strategy (ICSPCS) for EEG-based emotion recognition. Specifically, we first use a common spatial pattern algorithm to design a spatial domain filter according to three different emotions (positive, neutral, and negative). The traditional joint approximation diagonalization method using the criterion of the "highest score eigenvalue" may be unable to solve multiple classifications of emotion representation. Therefore, we design three different eigenvalue selection methods in terms of the positions of the eigenvalues with the highest scores. Finally, to make the installation easier and reduce the computational load, we also develop a channel-selection strategy based on the weight values that individually reflect the degrees of influence of all the channels on emotion recognition. Experiments are conducted on a self-collected dataset and on the MAHNOB-HCI dataset. The average recognition accuracies for the three emotion tasks are found to be 85.85% and 94.13% on the self-collected and MAHNOB-HCI datasets, respectively, thus proving the effectiveness of the proposed ICSPCS method for emotion recognition.