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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...

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Autores principales: Zhong, Qinghua, Zhu, Yongsheng, Cai, Dongli, Xiao, Luwei, Zhang, Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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.
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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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