Research of EEG-based emotion recognition for the deaf with feature fusion Online publication date: Wed, 05-Jun-2024
by Zemin Mao; Xuewen Zhao; Yu Song
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 45, No. 3, 2024
Abstract: Electroencephalogram (EEG) is better at reflecting emotional changes. The paper aims to explore the deaf brain activity mechanisms of emotional processing with EEG signals. Fifteen deaf subjects were recruited to participate in the emotional induction experiment, and EEG signals were collected when they watched three kinds of emotional movie clips. The frequency domain and brain network features were extracted and fused to capture correlation among EEG channels. The results show that fused features outperform single differential entropy (DE) features in classification indicators (accuracy: 96. 73%, recall: 96.58%, precision: 97.36%, F1 score: 96.42%). In addition, a stacking ensemble learning framework was proposed to classify the fused features, achieving higher classification accuracy than SVM by 2.72%. Investigation into brain activities reveals that deaf brain activity changes mainly in the beta and gamma bands, and the brain regions that are affected by emotions are distributed primarily in the frontal and occipital lobes.
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