EEG-SGC: A Spatio-Temporal Graph-Attention Classifier for EEG Artifact Detection with Sample-Level Localization and Channel-Subset Generalization

This is the authors' abstract. We don't add key points for this paper.

Automated detection of multiple artifact types in scalp EEG is challenging because of diverse montages, inter-subject variability, and the need to achieve both reliable classification and within-window artifact presence indication with boundary estimation. This study proposes EEG-SGC, a compact spatio-temporal graph classifier that performs sample-level localization, integrating a per-electrode temporal encoder, a graph attention spatial encoder defined on the 10–20 topology, a cross-modal temporal spectral attention module, and a shared encoder timestep localization head. The experimental results illustrate that on the TUAR subject-level split, the proposed EEG-SGC achieves a five-seed macro F1 of 0.7754 ± 0.0050 and Cohen’s kappa of 0.662, compared with the baseline models. The combination of inference-time channel adapter with channel-dropout training maintained macro F1 values above 0.62, when only four out of nineteen 10–20 electrodes were available. In contrast, use of the adapter without channel-dropout training resulted in majority-class routing when fewer than approximately twelve electrodes were available. Moreover, the study recorded EEG data from fifteen volunteers using an eight-channel dry electrode acquisition system to evaluate the detector’s performance. These findings support the practical applications of compact EEG artifact detectors across channel subsets within the canonical 10–20 electrode configuration.