Electrocardiogram Foundation Models

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Let EEG Models Learn EEG
May 20, 2026Yifan Wang, Yijia Ma, Wen Li +1Electroencephalography Generative Models
Stony Brook University · University of Texas Health Center at Houston.
Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
May 19, 2026Aditya R. Vaidya, Richard J. Antonello, Alexander G. HuthFunctional Magnetic Resonance Imaging Electrocardiogram Foundation Models
Department of Computer Science The University of Texas at Austin, USA · Zuckerman Mind Brain Behavior Institute Columbia University, USA · Departments of Neuroscience and Statistics University of California, Berkeley, USA
DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
May 18, 2026Yang Shao, Peiliang Gong, Qun Dai +1Electroencephalography Foundation Models Small-Sample Electroencephalogram Datasets
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics Nanjing, Jiangsu, China
Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
May 17, 2026Urban Širca, Maryam Alimardani, Stefanos Zafeiriou +1Electroencephalography Foundation Models Electroencephalography
Vrije Universiteit Amsterdam · Imperial College London
How Do Electrocardiogram Models Scale?
May 17, 2026Jiawei Li, Fabio Bonassi, Ming Jin +4Electrocardiogram Foundation Models Medical World Model
Uppsala University · Griffith University
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
May 16, 2026Wei Tang, Jinpei Han, Kangning Cui +10Electrocardiogram Foundation Models Medical World Model
1City University of Hong Kong · 2Imperial College London · 3Wake Forest University +3
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
May 14, 2026Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech +12Electroencephalography Foundation Models Brain-Computer Interface
KU Leuven · MIT
Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model
May 13, 2026Riccardo Cavarra, Lupo Lovatelli, Shaheim Ogbomo-Harmitt +4Electrocardiogram Foundation Models Cardiac
King’s College London, St Thomas’ Hospital, London, UK · North Bristol NHS Trust, Bristol, UK
Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis
May 13, 2026Bo Cui, Xiaowen Song, Yaowen Zhang +4Physiological Signals Electrocardiogram Foundation Models
Department of Biomedical Signals and Systems University of Twente · Department of Applied Mathematics University of Twente
Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
May 12, 2026M A Al-Masud, Nils StrodthoffElectrocardiogram Foundation Models Electrocardiography
AI4Health Division Carl von Ossietzky Universität Oldenburg Oldenburg, Lower Saxony, Germany
What Do EEG Foundation Models Capture from Human Brain Signals?
May 12, 2026Ling Tang, Qian Chen, Jilin Mei +6Electroencephalography Foundation Models Electroencephalography
Shanghai Artificial Intelligence Laboratory · Shanghai Jiao Tong University · Tongji University +2
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
Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model
May 10, 2026Zhangdaihong Liu, Chang Liu, Fenglin Liu +4Electrocardiogram Foundation Models Cardiac
Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK · Oxford Suzhou Centre for Advanced Research, Suzhou, 215123, China · School of Public Health, Shanghai Jiao Tong University, Shanghai, China
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
May 9, 2026Li Na, Yuanyun Zhang, Shi LiElectrocardiogram Foundation Models Arrhythmia Classification
University of Science and Technology of China · Columbia University
From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG
May 5, 2026Zhenghao Huang, Huilin Yao, Kaikai WangSurface Electromyography Musculoskeletal
South China University of Technology
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
Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
Apr 25, 2026Duc N. Do, Minh N. Do, Dang Nguyen +14Electrocardiogram Foundation Models Cardiac
1PASSIO Laboratory, North Carolina A&T State University, Greensboro, NC, USA · Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada · 3Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA +4
Towards Real-Time ECG and EMG Modeling on $μ$ NPUs
Apr 20, 2026Josh Millar, Ashok Samraj Thangarajan, Soumyajit Chatterjee +1Physiological Signals Neural Processing Units
Imperial College London London, United Kingdom · Nokia Bell Labs London, United Kingdom · Brave Software & University of Cambridge London, United Kingdom
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
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
Date pendingSarah Nassar, Nooshin Maghsoodi, Sophia Mannina +7Arrhythmia Classification Electrocardiogram Foundation Models
Department of Electrical and Computer Engineering at Queen’s University, Kingston, ON, Canada · School of Computing at Queen’s University, Kingston, ON, Canada · Smith School of Business at Queen’s University, Kingston, ON, Canada +5

May 20, 2026, cs.CVJ/K move · Enter open · S save

Let EEG Models Learn EEG

Yifan Wang, Yijia Ma, Wen Li, Chenyu You

Stony Brook University · University of Texas Health Center at Houston.

Electroencephalography Generative Models Neural Dynamics Electrocardiogram Foundation Models

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High-fidelity EEG generation is critical for alleviating data scarcity and addressing privacy constraints in large-scale neural modeling. Despite recent progress, most existing approaches formulate EEG generation via discrete denoising objectives, which inadequately reflect the inherently continuous temporal dynamics and spectral structure of neural activity. As a result, these methods often struggle to preserve long-range temporal dependencies and exhibit mismatches in the spectral and temporal structure of the generated signals. In this work, we argue that effective EEG generation requires models that operate directly on the continuous evolution of neural signals. We introduce Just EEG Transformer (JET), a generative framework based on conditional flow matching that models EEG as raw sequences evolving along continuous trajectories. By learning a smooth vector field that transports noise to the EEG data distribution, JET captures temporal continuity and transient dynamics without relying on discretized denoising schemes or domain-specific representations. To ensure that the learned dynamics remain consistent with key properties of EEG signals, we introduce principled constraints that preserve spectral structure, temporal stationarity, and signal-level statistics. Across three large-scale benchmarks, JET consistently achieves state-of-the-art performance, reducing TS-FID by over 40% compared to strong baselines. Extensive analyses show that JET captures key structural properties of neural dynamics, providing a scalable and principled approach to EEG generation. Project page: https://y-research-sbu.github.io/JET/ .

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