Abstract
Emotional brain-computer interface based on electroencephalogram (EEG) is a hot issue in the field of human-computer interaction, and is also an important part of the field of emotional computing. Among them, the recognition of EEG induced by emotion is a key problem. Firstly, the preprocessed EEG is decomposed by tunable-Q wavelet transform. Secondly, the sample entropy, second-order differential mean, normalized second-order differential mean, and Hjorth parameter (mobility and complexity) of each sub-band are extracted. Then, the binary gray wolf optimization algorithm is used to optimize the feature matrix. Finally, support vector machine is used to train the classifier. The five types of emotion signal samples of 32 subjects in the database for emotion analysis using physiological signal dataset is identified by the proposed algorithm. After 6-fold cross-validation, the maximum recognition accuracy is 90.48%, the sensitivity is 70.25%, the specificity is 82.01%, and the Kappa coefficient is 0.603. The results show that the proposed method has good performance indicators in the recognition of multiple types of EEG emotion signals, and has a better performance improvement compared with the traditional methods.
Highlights
Emotion is a psychological phenomenon mediated by the subject’s needs and desires
This study uses the database for emotion analysis using physiological signals (DEAP) data set to verify the effectiveness of the algorithm for emotion recognition from five aspects: (1) The data of each subject is decomposed by tunable Q-factor wavelet transform (TQWT), and the features are extracted after fusing the sub-bands
The feature sets are classified by support vector machine (SVM) before and after binary gray wolf optimization (BGWO)
Summary
Emotion is a psychological phenomenon mediated by the subject’s needs and desires. It has three components: physiological arousal, subjective experience, and external manifestation (Peng, 2004). Emotions have an important impact on people’s production and life, physical and mental health, and interpersonal relationships. For patients with depression or schizophrenia, abnormal emotions are the main clinical manifestations. If negative emotions can be identified before the onset, medical staff can intervene and treat in time. For the field of humancomputer interaction, computer recognition can be realized, understand and adapt to human
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