OOD Detection for EEG-based Machine Learning in High-Risk Environments
Machine learning models for electroencephalography (EEG) analysis show great promise across a wide range of applications, but their deployment in high-risk domains is hindered by their vulnerability to distribution shifts. Encountering out-of-distribution (OOD) data can lead to catastrophic, overconfident predictive failures. While OOD detection methods can mitigate these risks, they remain heavily under-explored for EEG. Moreover, evaluations in the broader literature typically evaluate OOD detection performance in isolation, ignoring their practical impact on downstream applications. To bridge this gap, we introduce a benchmark for EEG OOD detection, evaluate a broad range of methods, a...
| Authors | Philipp Bomatter, Henry Gouk |
|---|---|
| Primary category | cs.LG |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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