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arXiv Machine Learning By Philipp Bomatter, Henry Gouk

OOD Detection for EEG-based Machine Learning in High-Risk Environments

Read the original on arXiv Machine Learning →

The paper introduces a benchmark for out‑of‑distribution (OOD) detection in electroencephalography (EEG) machine learning, evaluates a wide range of OOD methods, and assesses their impact on two clinical downstream prediction tasks. It distinguishes between OOD detection and model uncertainty estimation, which are often conflated, and shows how combining complementary methods can create a robust safety net for deploying EEG‑based models in high‑risk settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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