The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Open accessAbstract
Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this article, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED, and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
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Cited by
A systematic review on affective computing: emotion models, databases, and recent advances
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Metadata
- Authors
- Zhong, P., Wang, D., & Miao, C.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2022-07-01
- Volume
- 13
- Issue
- 3
- Pages
- 1290-1301
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W3024961463
- Cited by
- 910
- References
- 58
- FWCI
- 73.56
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- Funders
- Alibaba Innovative Research Program
- Alibaba-NTU Singapore Joint Research Institute (Alibaba-NTU-AIR2019B1)
- National Innovation Challenge on Active and Confident Ageing (MOH/NIC/COG04/2017, MOH/NIC/HAIG03/2017)
- National Research Foundation Singapore
- NRF Investigatorship Programme (NRF-NRFI05-2019-0002)
- AI Singapore Programme (AISG-GC-2019-003)
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- Zhong, P., Wang, D., & Miao, C. (2022). EEG-Based Emotion Recognition Using Regularized Graph Neural Networks. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2020.2994159
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