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CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input...

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Autores principales: Huang, Zhentao, Ma, Yahong, Su, Jianyun, Shi, Hangyu, Jia, Shanshan, Yuan, Baoxi, Li, Weisu, Geng, Jingzhi, Yang, Tingting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399240/
https://www.ncbi.nlm.nih.gov/pubmed/37546532
http://dx.doi.org/10.3389/fphys.2023.1200656
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author Huang, Zhentao
Ma, Yahong
Su, Jianyun
Shi, Hangyu
Jia, Shanshan
Yuan, Baoxi
Li, Weisu
Geng, Jingzhi
Yang, Tingting
author_facet Huang, Zhentao
Ma, Yahong
Su, Jianyun
Shi, Hangyu
Jia, Shanshan
Yuan, Baoxi
Li, Weisu
Geng, Jingzhi
Yang, Tingting
author_sort Huang, Zhentao
collection PubMed
description EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.
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spelling pubmed-103992402023-08-04 CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition Huang, Zhentao Ma, Yahong Su, Jianyun Shi, Hangyu Jia, Shanshan Yuan, Baoxi Li, Weisu Geng, Jingzhi Yang, Tingting Front Physiol Physiology EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10399240/ /pubmed/37546532 http://dx.doi.org/10.3389/fphys.2023.1200656 Text en Copyright © 2023 Huang, Ma, Su, Shi, Jia, Yuan, Li, Geng and Yang. 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 Physiology
Huang, Zhentao
Ma, Yahong
Su, Jianyun
Shi, Hangyu
Jia, Shanshan
Yuan, Baoxi
Li, Weisu
Geng, Jingzhi
Yang, Tingting
CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
title CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
title_full CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
title_fullStr CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
title_full_unstemmed CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
title_short CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
title_sort cdba: a novel multi-branch feature fusion model for eeg-based emotion recognition
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399240/
https://www.ncbi.nlm.nih.gov/pubmed/37546532
http://dx.doi.org/10.3389/fphys.2023.1200656
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