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<p>Synthetic augmentation can silently harm subject-disjoint EEG generalization. We propose trust-gated augmentation (TGA), a control layer that scores synthetic windows using a teacher trained on real data to ensure label consistency and confidence; only samples above a confidence quantile <i>q</i> are eligible. A fail-closed selector injects synthetic data only if the validation AUROC exceeds the real-only AUROC by a margin; otherwise, it reverts to real-only. In PainMunich chronic-pain EEG (<i>n</i> = 189; 101 chronic pain/88 controls) at 5% subject scarcity, ungated augmentation harmed 56% of paired runs (ΔAUROC &lt; − 0.01), whereas TGA at <i>q</i> = 0.99 reduced harm to 24% with comparable mean AUROC. In BCI IV-2a motor imagery (<i>n</i> = 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 × 10<sup>4</sup>), motivating explicit governance.</p>

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Trust-gated synthetic EEG augmentation reduces performance drops when generalizing to new patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月25日

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  • Daniel Choi,
  • Cordelia Yip,
  • Andrew Choi,
  • Junho Park

摘要

Synthetic augmentation can silently harm subject-disjoint EEG generalization. We propose trust-gated augmentation (TGA), a control layer that scores synthetic windows using a teacher trained on real data to ensure label consistency and confidence; only samples above a confidence quantile q are eligible. A fail-closed selector injects synthetic data only if the validation AUROC exceeds the real-only AUROC by a margin; otherwise, it reverts to real-only. In PainMunich chronic-pain EEG (n = 189; 101 chronic pain/88 controls) at 5% subject scarcity, ungated augmentation harmed 56% of paired runs (ΔAUROC < − 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 × 104), motivating explicit governance.