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Electroencephalography Emotion Recognition Based on Rhythm Information Entropy Extraction

Year: 2024

Type: article

Abstract: Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person’s emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%.

Source: Journal of Advanced Computational Intelligence and Intelligent Informatics

DOI: 10.20965/jaciii.2024.p1095

Authors: Zhen-Tao Liu, Xin Xu, Jinhua She, Zhaohui Yang, Dan Chen

Institutions: China University of Geosciences, Tokyo University of Technology, Wuhan Union Hospital, Huazhong University of Science and Technology

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Higher Education Discipline Innovation Project, China University of Geosciences

Awards: G26576258, G5960086445, G6079930777, G7050647769, G8708917460