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Review on Emotion Recognition Based on Electroencephalography

Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction...

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Detalles Bibliográficos
Autores principales: Liu, Haoran, Zhang, Ying, Li, Yujun, Kong, Xiangyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518715/
https://www.ncbi.nlm.nih.gov/pubmed/34658828
http://dx.doi.org/10.3389/fncom.2021.758212
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author Liu, Haoran
Zhang, Ying
Li, Yujun
Kong, Xiangyi
author_facet Liu, Haoran
Zhang, Ying
Li, Yujun
Kong, Xiangyi
author_sort Liu, Haoran
collection PubMed
description Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.
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spelling pubmed-85187152021-10-16 Review on Emotion Recognition Based on Electroencephalography Liu, Haoran Zhang, Ying Li, Yujun Kong, Xiangyi Front Comput Neurosci Neuroscience Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology. Frontiers Media S.A. 2021-10-01 /pmc/articles/PMC8518715/ /pubmed/34658828 http://dx.doi.org/10.3389/fncom.2021.758212 Text en Copyright © 2021 Liu, Zhang, Li and Kong. 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
Liu, Haoran
Zhang, Ying
Li, Yujun
Kong, Xiangyi
Review on Emotion Recognition Based on Electroencephalography
title Review on Emotion Recognition Based on Electroencephalography
title_full Review on Emotion Recognition Based on Electroencephalography
title_fullStr Review on Emotion Recognition Based on Electroencephalography
title_full_unstemmed Review on Emotion Recognition Based on Electroencephalography
title_short Review on Emotion Recognition Based on Electroencephalography
title_sort review on emotion recognition based on electroencephalography
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518715/
https://www.ncbi.nlm.nih.gov/pubmed/34658828
http://dx.doi.org/10.3389/fncom.2021.758212
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