Bai, Xuemei and Tan, Jiaqi and Hu, Hanping and Zhang, Chenjie and Gu, Dongbing (2023) SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition. Journal of Intelligent and Fuzzy Systems, 45 (6). pp. 10545-10561. DOI https://doi.org/10.3233/jifs-232465
Bai, Xuemei and Tan, Jiaqi and Hu, Hanping and Zhang, Chenjie and Gu, Dongbing (2023) SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition. Journal of Intelligent and Fuzzy Systems, 45 (6). pp. 10545-10561. DOI https://doi.org/10.3233/jifs-232465
Bai, Xuemei and Tan, Jiaqi and Hu, Hanping and Zhang, Chenjie and Gu, Dongbing (2023) SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition. Journal of Intelligent and Fuzzy Systems, 45 (6). pp. 10545-10561. DOI https://doi.org/10.3233/jifs-232465
Abstract
The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios.
Item Type: | Article |
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Uncontrolled Keywords: | Electroencephalogram, emotion recognition, chebyshev network gated recurrent units, spectral graph convolution recurrent neural network, adjacency matrix |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 09 Jul 2024 15:09 |
Last Modified: | 09 Jul 2024 15:09 |
URI: | http://repository.essex.ac.uk/id/eprint/37643 |
Available files
Filename: SGCRNN Model for EEG Emotion Recognition - v2.2.pdf