The authors audit what EEG foundation models learn by probing their internal representations for 63 hand-crafted clinical features across 6 families (frequency, connectivity, complexity, etc.). They test three models on five clinical tasks and find that 68.6% of features are "representation-causal" (the model uses them for prediction) and 21.1% are "encoded-only" (present but not used). Fifty features emerge as universal candidates across tasks, and frequency-domain features dominate but all six families contribute. Confirmed features recover 79.3% of the foundation model's advantage over random baselines, with task-dependent coverage (near-perfect for easy tasks, ~56% for hard ones).
Main takeaways:
- Foundation models trained on raw EEG signals encode a substantial portion of the hand-crafted feature catalog refined over decades.
- Layer-wise probing and subspace erasure reveal that 68.6% of 945 (model, task, feature) units are causally used for prediction.
- Frequency-domain features are most prominent, but connectivity, complexity, and other families each contribute causal information.
- Fifty features qualify as "universal"—causally represented across multiple architectures and tasks.
- The hand-crafted lexicon recovers 79.3% of model performance on average, but harder tasks leave a residual that points to undiscovered features.