Electroencephalography Foundation Models

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17 papers in the last four weeks, up 325% on the four weeks before. 0.2% of all new papers.

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CLEF: EEG Foundation Model for Learning Clinical Semantics
May 11, 2026Peng Cao, Ali Mirzazadeh, Jong Woo Lee +2Electroencephalography Foundation Models Medical World Model
MIT CSAIL · Brigham and Women’s Hospital, Harvard Medical School · Massachusetts General Hospital, Harvard Medical School
CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings
May 11, 2026Liuyin Yang, Qiang Sun, Bob Van Dyck +2Electroencephalography Foundation Models Brain-Computer Interface
Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven
Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
May 4, 2026William Lehn-Schiøler, Magnus Ruud Kjær, Phillip Hempel +7Sleep Electroencephalography Foundation Models
BrainCapture ApS, Kongens Lyngby, Denmark · DTU Health Tech, Technical University of Denmark, Kongens Lyngby, Denmark · DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark +4
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Apr 29, 2026Taida Li, Yujun Yan, Fei Dou +2Electroencephalography Decoding Electroencephalography Foundation Models
Department of Computer Science, University of North Carolina at Charlotte, Charlotte, United States · Department of Computer Science, Dartmouth College, Hanover, United States · School of Computing, University of Georgia, Athens, United States +1
Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
Apr 25, 2026Kuntal Kokate, Bruno Aristimunha, Dung Truong +1Electroencephalography Foundation Models Parameter-Efficient Adaptation
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA · Yneuro, Paris, France · Centre de Recherche Cerveau et Cognition, CNRS, Université Paul Sabatier, Toulouse, France
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Apr 21, 2026Peiliang Gong, Han Zhang, Zhen Jiang +5Electroencephalography Foundation Models Electroencephalography Decoding
College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore · College of Artifical Intelligence and Automation, Hohai University, Changzhou 213022, China · College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China +2
Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
Apr 18, 2026Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng SunElectroencephalography Foundation Models Test-Time Adaptation
University of Illinois Urbana-Champaign, Urbana, IL, USA
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
Apr 16, 2026Jingyuan Wang, Zhihao Jia, Chenyu Liu +7Electroencephalography Foundation Models Small-Sample Electroencephalogram Datasets
1Xiamen University · 2Nanyang Technological University · Institute of Automation, Chinese Academy of Sciences +1
SingLEM: Single-Channel Large EEG Model
Sep 22, 2025Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue +5Electroencephalography Foundation Models Electroencephalography
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Koganei-Shi 184–8588, Japan · Department of Electronics and Communication Engineering, National University of Mongolia, Ulaanbaatar, 14200 Mongolia
Segment Anything for Dendrites from Electron Microscopy
Nov 4, 2024Zewen Zhuo, Ilya Belevich, Ville Leinonen +4Multimodal Neuroimaging Electroencephalography Foundation Models
A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio, Finland · Electron Microscopy Unit Institute of Biotechnology University of Helsinki Helsinki, Finland · Department of Medicine Faculty of Health Sciences University of Eastern Finland Kuopio, Finland

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CLEF: EEG Foundation Model for Learning Clinical Semantics

Peng Cao, Ali Mirzazadeh, Jong Woo Lee, Aleksandar Videnovic+1

MIT CSAIL · Brigham and Women’s Hospital, Harvard Medical School · Massachusetts General Hospital, Harvard Medical School

Electroencephalography Foundation Models Medical World Model Representation Learning Spectral Features Beethoven

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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.

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