Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models

The paper evaluates EEG foundation models (EEG‑FMs) beyond clean accuracy, testing six models and a supervised baseline across ten datasets. It finds that no single model excels in all robustness tests, that attribution methods reveal task‑appropriate brain region relevance, and that poor head‑only performance is largely due to pooling strategies rather than representation quality. The study also shows that fine‑tuning repurposes late blocks while early blocks already encode task information.

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• rag
• fine-tuning
• safety