An approach to emotion recognition using brain rhythm sequencing and asymmetric features.

Li, Jia Wen; Chen, Rong Jun; Barma, Shovan; Chen, Fei; Pun, Sio Hang; Mak, Peng Un; Wang, Lei Jun; Zeng, Xian Xian; Ren, Jin Chang; Zhao, Hui Min

Abstract

Emotion can be influenced during self-isolation, and to avoid severe mood swings, emotional regulation is meaningful. To achieve this, efficiently recognizing emotion is a vital step, which can be realized by electroencephalography signals. Previously, inspired by the knowledge of sequencing in bioinformatics, a method termed brain rhythm sequencing that analyzes electroencephalography as the sequence consisting of the dominant rhythm has been proposed for seizure detection. In this work, with the help of similarity measure methods, the asymmetric features are extracted from the sequences generated by different channel data. After evaluating all asymmetric features for emotion recognition, the optimal feature that yields remarkable accuracy is identified. Therefore, the classification task can be accomplished through a small amount of channel data. From a music emotion recognition experiment and a public DEAP dataset, the classification accuracies of various test sets are approximately 80–85% when employing an optimal feature extracted from one pair of symmetrical channels. Such performances are impressive when using fewer resources is a concern. Further investigation revealed that emotion recognition shows strongly individual characteristics, so an appropriate solution is to include the subject-dependent properties. Compared to the existing works, this method benefits from the design of a portable emotion-aware device used during self-isolation, as fewer scalp sensors are needed. Hence, it would provide a novel way to realize emotional applications in the future.

Citation

LI, J.W., CHEN, R.J., BARMA, S., CHEN, F., PUN, S.H., MAK, P.U., WANG, L.J., ZENG, X.X., REN, J.C. and ZHAO, H.M. 2022. An approach to emotion recognition using brain rhythm sequencing and asymmetric features. Cognitive computation [online], 14(6), pages 2260-2273. Available from: https://doi.org/10.1007/s12559-022-10053-z

• Journal Article Type: Article
• Acceptance Date: Aug 14, 2022
• Online Publication Date: Aug 26, 2022
• Publication Date: Nov 30, 2022
• Deposit Date: Oct 26, 2022
• Publicly Available Date: Aug 27, 2023
• Journal: Cognitive computation
• Print ISSN: 1866-9956
• Electronic ISSN: 1866-9964
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 6
• Pages: 2260-2273
• DOI: https://doi.org/10.1007/s12559-022-10053-z
• Keywords: Brain rhythm sequencing; Electroencephalography; Emotion recognition; Asymmetric features; Symmetrical channels
• Public URL: https://rgu-repository.worktribe.com/output/1753162
• Additional Information: The source codes with an example have been uploaded to the IEEE DataPort (https://doi.org/10.21227/dzsq-b842).