The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition
Abstract
In recent years, emotion recognition has become a research focus in the area of artificial intelligence. Due to its irregular structure, EEG data can be analyzed by applying graphical based algorithms or models much more efficiently. In this work, a Graph Convolutional Broad Network (GCB-net) was designed for exploring the deeper-level information of graph-structured data. It used the graph convolutional layer to extract features of graph-structured input and stacks multiple regular convolutional layers to extract relatively abstract features. The final concatenation utilized the broad concept, which preserves the outputs of all hierarchical layers, allowing the model to search features in broad spaces. To improve the performance of the proposed GCB-net, the broad learning system (BLS) was applied to enhance its features. For comparison, two individual experiments were conducted to examine the efficiency of the proposed GCB-net based on the SJTU emotion EEG dataset (SEED) and DREAMER dataset respectively. In SEED, compared with other state-of-art methods, the GCB-net could better promote the accuracy (reaching 94.24 percent) on the DE feature of the all-frequency band. In DREAMER dataset, GCB-net performed better than other models with the same setting. Furthermore, the GCB-net reached high accuracies of 86.99, 89.32 and 89.20 percent on dimensions of Valence, Arousal and Dominance respectively. The experimental results showed the robust classifying ability of the GCB-net and BLS in EEG emotion recognition.
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Cites
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Self-citationEEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
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Cited by
Emotion recognition in EEG signals using deep learning methods: A review
Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240
Li, Y., Liu, Y., Guo, Y.-Z., Liao, X.-F., Hu, B., & Yu, T. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3071860 · Cited by 196
Jia, Z., Lin, Y., Cai, X., Chen, H., Gou, H., & Wang, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413724 · Cited by 167
B2-ViT net: broad vision transformer network with broad attention for seizure prediction
Shi, S., & Liu, W. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3346955 · Cited by 124
Multimodal emotion recognition: A comprehensive review, trends, and challenges
Ramaswamy, M. P. A., & Palaniswamy, S. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1563 · Cited by 70
Metadata
- Authors
- Zhang, T., Wang, X., Xu, X., & Chen, C. L. P.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2022-01-01
- Volume
- 13
- Issue
- 1
- Pages
- 379-388
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2970007912
- Cited by
- 396
- References
- 46
- FWCI
- 48.40
- Type
- journal article
- Language
- en
- Open access
- closed
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