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EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare
Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic match...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819891/ https://www.ncbi.nlm.nih.gov/pubmed/36612700 http://dx.doi.org/10.3390/ijerph20010378 |
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author | Zhou, Tie Hua Liang, Wenlong Liu, Hangyu Wang, Ling Ryu, Keun Ho Nam, Kwang Woo |
author_facet | Zhou, Tie Hua Liang, Wenlong Liu, Hangyu Wang, Ling Ryu, Keun Ho Nam, Kwang Woo |
author_sort | Zhou, Tie Hua |
collection | PubMed |
description | Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions. Our proposed EER model (EEG-based Emotion Recognition Model) could identify 20 types of emotions based on 32 EEG channels, and the average recognition accuracy was above 90% and 80%, respectively. Our proposed music-based emotion classification model (MEC model) could classify eight typical emotion types of music based on nine music feature combinations, and the average classification accuracy was above 90%. In addition, the semantic mapping was analyzed according to the influence of different music types on emotional changes from different perspectives based on the two models, and the results showed that the joy type of music video could improve fear, disgust, mania, and trust emotions into surprise or intimacy emotions, while the sad type of music video could reduce intimacy to the fear emotion. |
format | Online Article Text |
id | pubmed-9819891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98198912023-01-07 EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare Zhou, Tie Hua Liang, Wenlong Liu, Hangyu Wang, Ling Ryu, Keun Ho Nam, Kwang Woo Int J Environ Res Public Health Article Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions. Our proposed EER model (EEG-based Emotion Recognition Model) could identify 20 types of emotions based on 32 EEG channels, and the average recognition accuracy was above 90% and 80%, respectively. Our proposed music-based emotion classification model (MEC model) could classify eight typical emotion types of music based on nine music feature combinations, and the average classification accuracy was above 90%. In addition, the semantic mapping was analyzed according to the influence of different music types on emotional changes from different perspectives based on the two models, and the results showed that the joy type of music video could improve fear, disgust, mania, and trust emotions into surprise or intimacy emotions, while the sad type of music video could reduce intimacy to the fear emotion. MDPI 2022-12-26 /pmc/articles/PMC9819891/ /pubmed/36612700 http://dx.doi.org/10.3390/ijerph20010378 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhou, Tie Hua Liang, Wenlong Liu, Hangyu Wang, Ling Ryu, Keun Ho Nam, Kwang Woo EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare |
title | EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare |
title_full | EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare |
title_fullStr | EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare |
title_full_unstemmed | EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare |
title_short | EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare |
title_sort | eeg emotion recognition applied to the effect analysis of music on emotion changes in psychological healthcare |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819891/ https://www.ncbi.nlm.nih.gov/pubmed/36612700 http://dx.doi.org/10.3390/ijerph20010378 |
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