EEG-DGRN: dynamic graph representation network for subject-independent ERP detection
Objectives The inter-subject variability remains a formidable challenge in electroencephalogram (EEG) signal processing. Existing event-related potential (ERP) detection methods inadequately consider the dynamic connectivity of EEG signals and event response differences between subjects, limiting th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Brain-Apparatus Communication |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/27706710.2024.2447576 |
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| Summary: | Objectives The inter-subject variability remains a formidable challenge in electroencephalogram (EEG) signal processing. Existing event-related potential (ERP) detection methods inadequately consider the dynamic connectivity of EEG signals and event response differences between subjects, limiting the discriminability of task-related features.Methods In this article, we propose EEG-DGRN, a dynamic graph representation network designed for subject-independent ERP detection. Specifically, the dynamic graph mechanism is used to capture the task-relevant connectivity relationship between EEG channels over time. Then, considering the local and global topology structure, a dual-branch graph pooling module is employed to prune features from different granularity. After that, the temporal dynamic attention module enables the model to pay more attention to subject-invariant representations.Results Our EEG-DGRN model is evaluated on a publicly available rapid serial visual presentation dataset. It achieves a remarkable mean balanced classification accuracy of 87.05%, outperforming all other methods compared in this study.Conclusion Such performance demonstrates its ability to extract subject-invariant EEG features and generalize effectively to unseen subjects. Lastly, ablation studies confirm the effectiveness of each module in EEG-DGRN, highlighting their contributions to the overall performance. |
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| ISSN: | 2770-6710 |