Fail closed trust gated synthetic augmentation governs tail risk under subject shift in EEG

Abstract

Synthetic augmentation can silently harm subject-disjoint EEG generalization. We propose trustgated augmentation (TGA), a control layer that scores synthetic windows with a teacher trained on real data for label consistency and confidence; only samples above a confidence quantile q are eligible. A fail-closed selector injects synthetic data only if validation AUROC exceeds real-only by a margin, otherwise reverting to real-only. In PainMunich chronic-pain EEG (n = 189) at 5% subject scarcity, ungated augmentation harmed 56% of paired runs ({Delta}AUROC< -0.01), whereas TGA at q = 0.99 reduced harm to 24% with comparable mean AUROC. In BCI IV-2a motor imagery (n = 9) at 25% scarcity, strict gating improved AUROC (0.679 vs 0.627) and reduced harm (0.16 vs 0.44). A covariance-manifold audit showed synthetic windows were strongly off-manifold (mean distance ratio 2.39 x 104), motivating explicit governance.