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| EEG Stress Emotion Analysis Based on Variable-Scale Symbolic Compensation Transfer Entropy |
| Gao Yunyuan1,2*, Wang Xiangkun1, Tian Yuping1*, She Qingshan1, Dong Hua3 |
1(College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China)
2(Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China)
3(Institute of Safety, China Academy of Information and Communications Technology, Beijing 100191, China) |
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Abstract Emotion recognition based on EEG signals has important clinical and scientific significance for the diagnosis and treatment of related emotional diseases. How to effectively extract features, improve recognition rate and reduce calculation time is the focus of this paper. From the perspective of studying the directional information interaction between brain channels, this paper combined the compensation algorithm for instantaneous causal effects and proposed an emotional analysis method of Variable-Scale Symbolic Compensation Transfer Entropy. This method was used to construct an emotional causal effect brain network, the network measurement and ReliefF feature optimization selection algorithm were used for channel selection. The results showed that the feature extraction method of VSSCTE improved the accuracy of emotion classification by about 15% to 96.74% over the conventional binary transfer entropy method when using data from the DAEP dataset of 127 stresses and 125 calms. After optimization of EEG channels, when the number of channels was reduced from 32 to 15, the classification accuracy rate only droped by about 2% (the classification accuracy rate was 94.36%), but the calculation time was reduced by about 110%. Overall, the VSSCTE method proposed in this paper was able to effectively analyze the information interaction between brain regions of different emotional states, providing a new method and ideas for emotional analysis.
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Received: 08 August 2020
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