发表机构
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对脑电图机器学习在高风险环境中易受分布偏移影响的问题，引入EEG OOD检测基准，评估相关方法并结合互补方法构建稳健安全网。
AI 中文摘要
用于脑电图（EEG）分析的机器学习模型在众多应用中展现出巨大潜力，但其在高风险领域的部署因对分布偏移的脆弱性而受阻。遇到分布外（OOD）数据会引发灾难性、过度自信的预测失效。尽管分布外检测方法可缓解此类风险，但针对脑电图（EEG）的相关研究仍严重不足。此外，现有文献中的评估通常孤立地评估分布外检测性能，忽略了其对下游应用的实际影响。为填补这一空白，我们引入了一个针对脑电图（EEG）分布外检测的基准，评估了广泛的方法，并进一步评估了它们在两项临床下游预测任务中的价值。我们的结果区分了分布外检测与模型不确定性估计能力（这两者在文献中常被混淆），提供了关于脑电图（EEG）分布外检测和模型不确定性估计当前技术水平的可行见解，并展示了如何将这两方面的互补方法相结合，为基于脑电图（EEG）的机器学习模型在实际应用中的部署构建一个稳健的安全网。
英文摘要
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.
