Positive and negative emotion classification based on multi-channel
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 c...
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my.um.eprints.342212022-09-08T04:08:26Z http://eprints.um.edu.my/34221/ Positive and negative emotion classification based on multi-channel Long, Fangfang Zhao, Shanguang Wei, Xin Ng, Siew Cheok Ni, Xiaoli Chi, Aiping Fang, Peng Zeng, Weigang Wei, Bokun R Medicine 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%. Frontiers Media SA 2021-08-26 Article PeerReviewed Long, Fangfang and Zhao, Shanguang and Wei, Xin and Ng, Siew Cheok and Ni, Xiaoli and Chi, Aiping and Fang, Peng and Zeng, Weigang and Wei, Bokun (2021) Positive and negative emotion classification based on multi-channel. Frontiers In Behavioral Neuroscience, 15. ISSN 1662-5153, DOI https://doi.org/10.3389/fnbeh.2021.720451 <https://doi.org/10.3389/fnbeh.2021.720451>. 10.3389/fnbeh.2021.720451 |
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R Medicine Long, Fangfang Zhao, Shanguang Wei, Xin Ng, Siew Cheok Ni, Xiaoli Chi, Aiping Fang, Peng Zeng, Weigang Wei, Bokun Positive and negative emotion classification based on multi-channel |
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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%. |
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Article |
author |
Long, Fangfang Zhao, Shanguang Wei, Xin Ng, Siew Cheok Ni, Xiaoli Chi, Aiping Fang, Peng Zeng, Weigang Wei, Bokun |
author_facet |
Long, Fangfang Zhao, Shanguang Wei, Xin Ng, Siew Cheok Ni, Xiaoli Chi, Aiping Fang, Peng Zeng, Weigang Wei, Bokun |
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Long, Fangfang |
title |
Positive and negative emotion classification based on multi-channel |
title_short |
Positive and negative emotion classification based on multi-channel |
title_full |
Positive and negative emotion classification based on multi-channel |
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Positive and negative emotion classification based on multi-channel |
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Positive and negative emotion classification based on multi-channel |
title_sort |
positive and negative emotion classification based on multi-channel |
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Frontiers Media SA |
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2021 |
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http://eprints.um.edu.my/34221/ |
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