Key result
The proposed EEG-DGRN model achieved a mean balanced classification accuracy of 87.05% for subject-independent ERP detection, outperforming other compared methods.
The proposed EEG-DGRN model effectively extracts subject-invariant EEG features, improving event-related potential detection accuracy across different subjects.
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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Jin et al. (2025) studied Event-related potential (ERP) detection. EEG-DGRN (dynamic graph representation network) vs. other methods was evaluated on mean balanced classification accuracy. The proposed EEG-DGRN model achieved a mean balanced classification accuracy of 87.05% for subject-independent ERP detection, outperforming other compared methods.
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