构建首个面向临床的脑电基础模型统一评测基准
EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications
- 覆盖14个公开数据集的11项诊断任务，标准化评估流程
- 基础模型表现优异但简单模型在分布偏移下仍具竞争力
- 开源完整数据与代码，支持复现与扩展
我们提出一个面向临床应用的脑电图（EEG）基础模型统一评测框架。该基准涵盖14个公开的EEG数据集，覆盖癫痫、精神分裂症、帕金森病、强迫症及轻度创伤性脑损伤等11项明确诊断任务。框架采用最少预处理、标准化评估协议，支持经典基线模型与现代基础模型的直接对比。实验结果表明，尽管基础模型在特定场景下表现强劲，但更简单的模型在临床分布偏移条件下仍具竞争力。为促进可复现性与实际应用，我们以可访问且可扩展的格式公开所有准备好的数据和代码。
原文摘要 · Abstract (English)
We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks across 14 publicly available EEG datasets, including epilepsy, schizophrenia, Parkinson's disease, OCD, and mild traumatic brain injury. It features minimal preprocessing, standardized evaluation protocols, and enables side-by-side comparisons of classical baselines and modern foundation models. Our results show that while foundation models achieve strong performance in certain settings, simpler models often remain competitive, particularly under clinical distribution shifts. To facilitate reproducibility and adoption, we release all prepared data and code in an accessible and extensible format.
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