EEG-Based Emotion Recognition with Combined Fuzzy Inference via Integrating Weighted Fuzzy Rule Inference and Interpolation

Fangyi Li (), Fusheng Yu, Liang Shen (), Hexi Li, Xiaonan Yang and Qiang Shen
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Fangyi Li: School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China
Fusheng Yu: School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China
Liang Shen: School of Information Engineering, Fujian Business University, Fuzhou 350506, China
Hexi Li: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Xiaonan Yang: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Qiang Shen: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK

Mathematics, 2025, vol. 13, issue 1, 1-21

Abstract: Emotions play a significant role in shaping psychological activities, behaviour, and interpersonal communication. Reflecting this importance, automated emotion classification has become a vital research area in artificial intelligence. Electroencephalogram (EEG)-based emotion recognition is particularly promising due to its high temporal resolution and resistance to manipulation. This study introduces an advanced fuzzy inference algorithm for EEG data-driven emotion recognition, effectively addressing the ambiguity of emotional states. By combining adaptive fuzzy rule generation, feature evaluation, and weighted fuzzy rule interpolation, the proposed approach achieves accurate emotion classification while handling incomplete knowledge. Experimental results demonstrate that the integrated fuzzy system outperforms state-of-the-art techniques, offering improved recognition accuracy and robustness under uncertainty.

Keywords: EEG data; emotion recognition; fuzzy inference; fuzzy rule interpolation; fuzzy systems integration (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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