EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications
This provides a standardized evaluation tool for researchers and clinicians working on EEG-based diagnostic applications, though it is incremental as it focuses on benchmarking rather than new model development.
The authors introduced EEG-Bench, a unified benchmarking framework for evaluating EEG-based foundation models across 11 diagnostic tasks and 14 public datasets, finding that foundation models achieve strong performance in some settings but simpler models often remain competitive, especially under clinical distribution shifts.
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.