EEG-Based Emotion Recognition of Deaf Subjects by Integrated Genetic Firefly Algorithm

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 11
• Main Authors: Tian, Zekun, Li, Dahua, Song, Yu, Gao, Qiang, Kang, Qiaoju, Yang, Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain modeling; Classification; Classification algorithms; Deaf subjects; Dynamical systems; electroencephalogram (EEG); Electroencephalography; Emotion recognition; Emotions; Entropy; Feature extraction; firefly algorithm (FA); genetic algorithm; Heuristic methods; Lobes; Multiple objective analysis; Nonlinear dynamics; Protocols; Reduction; Subgroups
• DOI: 10.1109/TIM.2021.3121473
• ISSN: 0018-9456

In recent years, many researchers have explored different methods to obtain discriminative features for electroencephalogram-based (EEG-based) emotion recognition, but a few studies have been investigated on deaf subjects. In this study, we have established a deaf EEG emotion dataset, which contains three kinds of emotion (positive, neutral, and negative) with 15 subjects. Ten kinds of time-frequency domain features and eleven kinds of nonlinear dynamic system features were extracted from the EEG signals. To obtain the optimal feature combination and optimal classifier, an integrated genetic firefly algorithm (IGFA) was proposed. The multi-objective function with variable weight was utilized to balance the classification accuracy and the feature reduction ratio that are contradictory goals to find brighter fireflies in each generation. To retain the historical optimal solution and reduce the feature dimension, an optimal population protection scheme and subgroups generation scheme was carried out. The experimental results show that the averaged feature reduction rate of the proposed method is 0.959, and the averaged classification accuracy is 0.961. By investigating important brain regions, deaf subjects have common areas in the frontal and temporal lobes for EEG emotion recognition, while individual areas occur in the occipital and parietal lobes.