This research evaluates how foundation models perform when applied to electroencephalography (EEG) data analysis in medical settings with limited data availability.

• Foundation models show promising results as feature extractors for EEG data across multiple medical tasks
• Models demonstrate effectiveness in age prediction, seizure detection, and classification of clinical EEG events
• Performance varies across tasks and datasets, suggesting potential for targeted medical applications
• Results point to new directions for clinical biomarker development and EEG analysis with limited training data

This research matters for healthcare by potentially enabling more accurate neurological diagnostics and expanding the toolkit for brain signal analysis in clinical settings where data scarcity is common.

Are foundation models useful feature extractors for electroencephalography analysis?