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| license: cc-by-nc-sa-4.0 |
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| This is the pre-trained checkpoint for the paper **LEAD: An EEG Foundation Model for Alzheimer's Disease Detection** (Accepted by Transactions on Machine Learning Research 2026). |
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| Authors: [Yihe Wang](https://github.com/YiheWang), [Nan Huang](https://github.com/TeddyHuang-00), [Nadia Mammone](https://unirc.unifind.cineca.it/get/person/001110), [Marco Cecchi](https://www.linkedin.com/in/marco-cecchi-81234724/), [Xiang Zhang](http://xiangzhang.info/) |
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| Paper link: [TMLR](https://openreview.net/forum?id=AigNTyxcvH&referrer=%5BAuthor%20Console%5D%28%2Fgroup%3Fid%3DTMLR%2FAuthors%23your-submissions%29), [Arxiv](https://arxiv.org/abs/2502.01678) |
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| We train the world's first EEG foundation model for Alzheimer's Disease (AD) detection, |
| named LEAD, by leveraging the largest EEG-based AD detection dataset to date, |
| which includes 2,238 subjects and 427.81 hours of EEG recordings. |
| The LEAD model is adaptable to EEG data of any length, channel topologies, and diverse sampling rates, enabling flexible end-to-end subject-level AD detection. |
| The motivation for this work is to provide a comprehensive work for EEG-based AD detection, |
| including existing challenges, EEG-AD datasets, robust model design, |
| a training pipeline, post-hoc processing, correct evaluation protocols, current progress, and future directions. |
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