Positive and Negative Emotion Classification Based on Multi-channel

Fangfang Long; Shanguang Zhao; Xin Wei; Siew-Cheok Ng; Xiaoli Ni; Aiping Chi; Peng Fang; Weigang Zeng; Bokun Wei

doi:10.3389/fnbeh.2021.720451

Positive and Negative Emotion Classification Based on Multi-channel

Front Behav Neurosci. 2021 Aug 26:15:720451. doi: 10.3389/fnbeh.2021.720451. eCollection 2021.

Authors

Fangfang Long¹, Shanguang Zhao², Xin Wei^{3

4}, Siew-Cheok Ng⁵, Xiaoli Ni³, Aiping Chi⁶, Peng Fang⁷, Weigang Zeng⁴, Bokun Wei⁸

Affiliations

¹ Department of Psychology, Nanjing University, Nanjing, China.
² Centre for Sport and Exercise Sciences, University of Malaya, Kuala Lumpur, Malaysia.
³ Institute of Social Psychology, School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, China.
⁴ Key & Core Technology Innovation Institute of the Greater Bay Area, Guangdong, China.
⁵ Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
⁶ School of Sports, Shaanxi Normal University, Xi'an, China.
⁷ Department of the Psychology of Military Medicine, Air Force Medical University, Xi'an, China.
⁸ Xi'an Middle School of Shaanxi Province, Xi'an, China.

Abstract

The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.

Keywords: EEG; back propagation neural network; decision tree; emotion classification; k-nearest neighbor; support vector machine.