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EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres
EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hem...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9491730/ https://www.ncbi.nlm.nih.gov/pubmed/36161187 http://dx.doi.org/10.3389/fnins.2022.974673 |
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author | Zhang, Jing Zhang, Xueying Chen, Guijun Huang, Lixia Sun, Ying |
author_facet | Zhang, Jing Zhang, Xueying Chen, Guijun Huang, Lixia Sun, Ying |
author_sort | Zhang, Jing |
collection | PubMed |
description | EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hemispheres to emotional response, this paper proposes an EEG emotion recognition scheme based on cross-frequency GC feature extraction and fusion in the left and right hemispheres. Firstly, we calculate the GC relationship of EEG signals according to the frequencies and hemispheres, and mainly focus on the causality of the cross-frequency EEG signals in left and right hemispheres. Then, to remove the redundant connections of the GC brain network, an adaptive two-stage decorrelation feature extraction scheme is proposed under the condition of maintaining the best emotion recognition performance. Finally, a multi-GC feature fusion scheme is designed to balance the recognition accuracy and feature number of each GC feature, which comprehensively considers the influence of the recognition accuracy and computational complexity. Experimental results on the DEAP emotion dataset show that the proposed scheme can achieve an average accuracy of 84.91% for four classifications, which improved the classification accuracy by up to 8.43% compared with that of the traditional same-frequency band GC features. |
format | Online Article Text |
id | pubmed-9491730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94917302022-09-22 EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres Zhang, Jing Zhang, Xueying Chen, Guijun Huang, Lixia Sun, Ying Front Neurosci Neuroscience EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hemispheres to emotional response, this paper proposes an EEG emotion recognition scheme based on cross-frequency GC feature extraction and fusion in the left and right hemispheres. Firstly, we calculate the GC relationship of EEG signals according to the frequencies and hemispheres, and mainly focus on the causality of the cross-frequency EEG signals in left and right hemispheres. Then, to remove the redundant connections of the GC brain network, an adaptive two-stage decorrelation feature extraction scheme is proposed under the condition of maintaining the best emotion recognition performance. Finally, a multi-GC feature fusion scheme is designed to balance the recognition accuracy and feature number of each GC feature, which comprehensively considers the influence of the recognition accuracy and computational complexity. Experimental results on the DEAP emotion dataset show that the proposed scheme can achieve an average accuracy of 84.91% for four classifications, which improved the classification accuracy by up to 8.43% compared with that of the traditional same-frequency band GC features. Frontiers Media S.A. 2022-09-07 /pmc/articles/PMC9491730/ /pubmed/36161187 http://dx.doi.org/10.3389/fnins.2022.974673 Text en Copyright © 2022 Zhang, Zhang, Chen, Huang and Sun. 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 Zhang, Jing Zhang, Xueying Chen, Guijun Huang, Lixia Sun, Ying EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
title | EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
title_full | EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
title_fullStr | EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
title_full_unstemmed | EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
title_short | EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
title_sort | eeg emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9491730/ https://www.ncbi.nlm.nih.gov/pubmed/36161187 http://dx.doi.org/10.3389/fnins.2022.974673 |
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