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

Authors :
Zhentao Huang
Yahong Ma
Jianyun Su
Hangyu Shi
Shanshan Jia
Baoxi Yuan
Weisu Li
Jingzhi Geng
Tingting Yang
Source :
Frontiers in Physiology, Vol 14 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

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.

Details

Language :
English
ISSN :
1664042X
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Frontiers in Physiology
Publication Type :
Academic Journal
Accession number :
edsdoj.552b13f0bffe471188d88061b8b258c2
Document Type :
article
Full Text :
https://doi.org/10.3389/fphys.2023.1200656