Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization - Li, Siyu; Lyu, Xiaotong; Zhao, Lei; Chen, Zhuangfei; Gong, Anmin; Fu, Yunfa (Frontiers in Computational Neuroscience, 2021)
Emotional brain-computer interface based on EEG is a hot issue in the field of human-computer 17 interaction, and is also an important part of the field of emotional computing. Among them, the 18 recognition of electroencephalogram (EEG) induced by emotion is a key problem. Firstly, the 19 preprocessed EEG is decomposed by tunable-Q wavelet transform (TQWT). Secondly, the sample 20 entropy, second-order differential mean, normalized second-order differential mean, and Hjorth 21 parameter (mobility and complexity) of each sub-band are extracted. Then, the binary gray wolf 22 optimization (BGWO) algorithm is used to optimize the feature matrix. Finally, support vector 23 machine (SVM) is used to train the classifier. The 5 types of emotion signal samples of 32 subjects in 24 the DEAP dataset is identified by the proposed algorithm. After 6-fold cross-validation, the 25 maximum recognition accuracy is 90.48%, the sensitivity is 70.25%, the specificity is 82.01%, and 26 the Kappa coefficient is 0.603. The results show that the proposed method has good performance 27 indicators in the recognition of multiple types of EEG emotion signals, and has a better performance 28 improvement compared with the traditional methods.