The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Abstract
In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.
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Cites
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Cited by
A review of emotion recognition using physiological signals
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018) · Sensors · doi:10.3390/s18072074 · Cited by 923
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
Yin, Y., Zheng, X., Hu, B., Zhang, Y., & Cui, X. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106954 · Cited by 466
Liu, W., Qiu, J.-L., Zheng, W.-L., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3071170 · Cited by 452
Self-citationTransfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434
Metadata
- Authors
- Song, T., Zheng, W., Song, P., & Cui, Z.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2020-07-01
- Volume
- 11
- Issue
- 3
- Pages
- 532-541
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2790404832
- Cited by
- 1,597
- References
- 10
- FWCI
- 74.04
- Type
- journal article
- Language
- en
- Open access
- closed
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- Song, T., Zheng, W., Song, P., & Cui, Z. (2020). EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2018.2817622
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