Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 41; no. 1; p. 1
• Main Authors: Xie, Songyun, Lei, Lingjun, Sun, Jiang, Xu, Jian
• Format: Journal Article
• Language: Chinese
• Published: China Sichuan Society for Biomedical Engineering 25.02.2024
• Subjects: Algorithms; Animals; Artificial neural networks; Commonality; Control methods; EEG; Electroencephalography; Electroencephalography - methods; Emotion recognition; Emotions; Emotions - physiology; Humans; Learning; Machine learning; Optimization; Optimization algorithms; Training; Whales; Emotion recognition; Feature selection; Whale optimization algorithm; Electroencephalogram; Extreme learning machine
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.202303010

Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shor