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Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network
In the human-computer interaction (HCI), electroencephalogram (EEG) access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hy...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772146/ https://www.ncbi.nlm.nih.gov/pubmed/33390918 http://dx.doi.org/10.3389/fnhum.2020.589001 |
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author | Zhong, Qinghua Zhu, Yongsheng Cai, Dongli Xiao, Luwei Zhang, Han |
author_facet | Zhong, Qinghua Zhu, Yongsheng Cai, Dongli Xiao, Luwei Zhang, Han |
author_sort | Zhong, Qinghua |
collection | PubMed |
description | In the human-computer interaction (HCI), electroencephalogram (EEG) access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy (MSE) features of each frequency band were extracted. Secondly, the constructed 3D MSE feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network (CNN) and hidden Markov models (HMMs). Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-7772146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77721462020-12-31 Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network Zhong, Qinghua Zhu, Yongsheng Cai, Dongli Xiao, Luwei Zhang, Han Front Hum Neurosci Human Neuroscience In the human-computer interaction (HCI), electroencephalogram (EEG) access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy (MSE) features of each frequency band were extracted. Secondly, the constructed 3D MSE feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network (CNN) and hidden Markov models (HMMs). Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method. Frontiers Media S.A. 2020-12-16 /pmc/articles/PMC7772146/ /pubmed/33390918 http://dx.doi.org/10.3389/fnhum.2020.589001 Text en Copyright © 2020 Zhong, Zhu, Cai, Xiao and Zhang. http://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 | Human Neuroscience Zhong, Qinghua Zhu, Yongsheng Cai, Dongli Xiao, Luwei Zhang, Han Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network |
title | Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network |
title_full | Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network |
title_fullStr | Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network |
title_full_unstemmed | Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network |
title_short | Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network |
title_sort | electroencephalogram access for emotion recognition based on a deep hybrid network |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772146/ https://www.ncbi.nlm.nih.gov/pubmed/33390918 http://dx.doi.org/10.3389/fnhum.2020.589001 |
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