Abstract:Clinical EEG interpretation requires reasoning over full EEG sessions and integrating signal patterns with clinical context. Existing EEG foundation models are largely designed for short-window decoding and do not incorporate clinical context. We introduce CLEF, a clinically grounded long-context EEG foundation model. CLEF represents EEG sessions as 3D multitaper spectrogram tokens, enabling tractable Transformer modeling at session scale, and aligns embeddings with neurologist reports and structured EHR data through contrastive objectives. We evaluate CLEF on a new 234-task benchmark spanning disease phenotypes, medication exposures, and EEG findings, with more than 260k EEG sessions from over 108k patients. CLEF outperforms prior EEG foundation models on 229 of 234 tasks, improving mean AUROC from 0.65 to 0.74. Reconstruction-only pretraining surpasses prior EEG foundation models, while report and EHR alignment yields further gains. Held-out concept and external-cohort experiments suggest that these representations transfer beyond observed alignment targets. These results support session-scale, clinically grounded representation learning as a promising foundation-model paradigm for clinical EEG.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.10817 [cs.AI] |
| (or arXiv:2605.10817v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10817 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ali Mirzazadeh [view email]
[v1]
Mon, 11 May 2026 16:34:58 UTC (1,268 KB)