提升脑电图模型在高风险场景下的安全检测能力
OOD Detection for EEG-based Machine Learning in High-Risk Environments
- 构建首个脑电图异常数据检测基准，系统评估多种方法
- 发现现有方法在异常检测与不确定性估计上常被混淆
- 验证组合策略可显著增强实际临床应用的安全性
脑电图（EEG）机器学习模型在众多应用中展现巨大潜力，但在高风险领域部署时受限于对分布外数据的敏感性。遭遇分布外（OOD）数据可能导致灾难性且过度自信的预测失败。尽管异常检测方法可缓解此类风险，但其在脑电图领域的研究仍严重不足。此外，现有文献通常孤立评估异常检测性能，忽视其对下游任务的实际影响。为此，本文提出一个脑电图异常检测基准，评估多种方法，并进一步检验其在两项临床下游预测任务中的价值。结果揭示了异常检测与模型不确定性估计能力的差异，澄清了当前脑电图异常检测与不确定性估计的现状，并证明结合互补方法可为脑电图机器学习模型在真实场景部署提供稳健安全保障。
原文摘要 · Abstract (English)
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, and furthermore evaluate their value in two clinical downstream prediction task. Our results disentangle OOD detection and model uncertainty estimation capabilities, which are frequently conflated in the literature, provide actionable insights about the current state of the art for EEG OOD detection and model uncertainty estimation, and demonstrate how complementary methods for both aspects can be combined to form a robust safety net for the deployment of EEG-based machine learning models in real-world applications.
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