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EEG-based emotion recognition using capsule network with hybrid attention mechanism
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EEG-based emotion recognition using capsule network with hybrid attention mechanism
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Journal Article
EEG-based emotion recognition using capsule network with hybrid attention mechanism
2025
Overview
To fully extract the frequency information and spatial topological information of multi-channel EEG signals,this paper introduces an EEG-based emotion recognition model utilizing a Capsule Network with a Convolutional Block Attention Module(CBAM-CapsNet). Firstly,EEG signals from different frequency bands are acquired to extract their differential entropy features. Secondly,these features are mapped into a three-dimensional compact feature matrix according to spatial lead distribution. Finally,the three-dimensional feature matrix is processed through the proposed CBAM-CapsNet for training and prediction. Experimental results indicate that the high frequency band has a greater impact on emotion recognition than the low frequency bands,and the use of four-frequency band three-dimensional matrix can significantly enhance the accuracy of emotion recognition. The proposed CBAM-CapsNet achieves binary classification accuracies of 95. 42% and 95. 52% on the Arousal and Valence dimensions of the DEAP dataset,respecti
Publisher
Nanjing University of Information Science & Technology
Subject
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