English

EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications

Machine Learning 2025-12-11 v1 Artificial Intelligence

Abstract

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.

Keywords

Cite

@article{arxiv.2512.08959,
  title  = {EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications},
  author = {Ard Kastrati and Josua Bürki and Jonas Lauer and Cheng Xuan and Raffaele Iaquinto and Roger Wattenhofer},
  journal= {arXiv preprint arXiv:2512.08959},
  year   = {2025}
}

Comments

Foundation Models for the Brain and Body (BrainBodyFM@NeurIPS)

R2 v1 2026-07-01T08:17:39.291Z