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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Brain-Apparatus Communication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27706710.2024.2447576 |
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| author | Jiabin Zhu Xuanyu Jin Yuhang Ming Wanzeng Kong |
| author_facet | Jiabin Zhu Xuanyu Jin Yuhang Ming Wanzeng Kong |
| author_sort | Jiabin Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-477e2523ead34b6d83c4e320511d3d8b |
| institution | Kabale University |
| issn | 2770-6710 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Brain-Apparatus Communication |
| spelling | doaj-art-477e2523ead34b6d83c4e320511d3d8b2025-01-08T19:13:19ZengTaylor & Francis GroupBrain-Apparatus Communication2770-67102025-12-014110.1080/27706710.2024.2447576EEG-DGRN: dynamic graph representation network for subject-independent ERP detectionJiabin Zhu0Xuanyu Jin1Yuhang Ming2Wanzeng Kong3School of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaObjectives 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.https://www.tandfonline.com/doi/10.1080/27706710.2024.2447576Brain–computer interfacedynamic graph neural networkelectroencephalogramevent-related potentialsubject-invariant representation |
| spellingShingle | Jiabin Zhu Xuanyu Jin Yuhang Ming Wanzeng Kong EEG-DGRN: dynamic graph representation network for subject-independent ERP detection Brain-Apparatus Communication Brain–computer interface dynamic graph neural network electroencephalogram event-related potential subject-invariant representation |
| title | EEG-DGRN: dynamic graph representation network for subject-independent ERP detection |
| title_full | EEG-DGRN: dynamic graph representation network for subject-independent ERP detection |
| title_fullStr | EEG-DGRN: dynamic graph representation network for subject-independent ERP detection |
| title_full_unstemmed | EEG-DGRN: dynamic graph representation network for subject-independent ERP detection |
| title_short | EEG-DGRN: dynamic graph representation network for subject-independent ERP detection |
| title_sort | eeg dgrn dynamic graph representation network for subject independent erp detection |
| topic | Brain–computer interface dynamic graph neural network electroencephalogram event-related potential subject-invariant representation |
| url | https://www.tandfonline.com/doi/10.1080/27706710.2024.2447576 |
| work_keys_str_mv | AT jiabinzhu eegdgrndynamicgraphrepresentationnetworkforsubjectindependenterpdetection AT xuanyujin eegdgrndynamicgraphrepresentationnetworkforsubjectindependenterpdetection AT yuhangming eegdgrndynamicgraphrepresentationnetworkforsubjectindependenterpdetection AT wanzengkong eegdgrndynamicgraphrepresentationnetworkforsubjectindependenterpdetection |