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Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization

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 i...

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Autores principales: Li, Siyu, Lyu, Xiaotong, Zhao, Lei, Chen, Zhuangfei, Gong, Anmin, Fu, Yunfa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455931/
https://www.ncbi.nlm.nih.gov/pubmed/34566614
http://dx.doi.org/10.3389/fncom.2021.732763
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author Li, Siyu
Lyu, Xiaotong
Zhao, Lei
Chen, Zhuangfei
Gong, Anmin
Fu, Yunfa
author_facet Li, Siyu
Lyu, Xiaotong
Zhao, Lei
Chen, Zhuangfei
Gong, Anmin
Fu, Yunfa
author_sort Li, Siyu
collection PubMed
description 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.
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spelling pubmed-84559312021-09-23 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 Front Comput Neurosci Neuroscience 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. Frontiers Media S.A. 2021-09-08 /pmc/articles/PMC8455931/ /pubmed/34566614 http://dx.doi.org/10.3389/fncom.2021.732763 Text en Copyright © 2021 Li, Lyu, Zhao, Chen, Gong and Fu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Siyu
Lyu, Xiaotong
Zhao, Lei
Chen, Zhuangfei
Gong, Anmin
Fu, Yunfa
Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
title Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
title_full Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
title_fullStr Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
title_full_unstemmed Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
title_short Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
title_sort identification of emotion using electroencephalogram by tunable q-factor wavelet transform and binary gray wolf optimization
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455931/
https://www.ncbi.nlm.nih.gov/pubmed/34566614
http://dx.doi.org/10.3389/fncom.2021.732763
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