LEAD: An EEG Foundation Model for Alzheimer's Disease Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918316653674496 |
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| author | Wang, Yihe Huang, Nan Mammone, Nadia Cecchi, Marco Zhang, Xiang |
| author_facet | Wang, Yihe Huang, Nan Mammone, Nadia Cecchi, Marco Zhang, Xiang |
| contents | Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face three major challenges: 1) the lack of large-scale EEG-based AD datasets for robust representation learning; 2) limited generalizability across subjects; and 3) difficulty in adapting to highly heterogeneous data. To address these challenges, we curate the world's largest EEG-AD corpus to date, comprising 2,238 subjects. Leveraging this unique resource, we propose LEAD, the first large-scale foundation model for EEG-based AD detection. Specifically, we design a gated temporal-spatial Transformer that can adapt to EEG recordings with arbitrary lengths, channel configurations, and sampling rates. In addition, we introduce a subject-regularized training strategy to enhance subject-level feature learning. We further employ medical contrastive learning for pre-training on 13 datasets, including 4 AD datasets and 9 non-AD neurological disorder datasets, and fine-tune/test the model on the other 5 AD datasets. LEAD achieves the best average ranking across all 20 evaluations on 5 downstream datasets, substantially outperforming existing approaches, including state-of-the-art (SOTA) EEG foundation models. These results strongly demonstrate the effectiveness and practical potential of the proposed method for real-world EEG-based AD detection. Source code: https://github.com/DL4mHealth/LEAD |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01678 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LEAD: An EEG Foundation Model for Alzheimer's Disease Detection Wang, Yihe Huang, Nan Mammone, Nadia Cecchi, Marco Zhang, Xiang Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Signal Processing Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face three major challenges: 1) the lack of large-scale EEG-based AD datasets for robust representation learning; 2) limited generalizability across subjects; and 3) difficulty in adapting to highly heterogeneous data. To address these challenges, we curate the world's largest EEG-AD corpus to date, comprising 2,238 subjects. Leveraging this unique resource, we propose LEAD, the first large-scale foundation model for EEG-based AD detection. Specifically, we design a gated temporal-spatial Transformer that can adapt to EEG recordings with arbitrary lengths, channel configurations, and sampling rates. In addition, we introduce a subject-regularized training strategy to enhance subject-level feature learning. We further employ medical contrastive learning for pre-training on 13 datasets, including 4 AD datasets and 9 non-AD neurological disorder datasets, and fine-tune/test the model on the other 5 AD datasets. LEAD achieves the best average ranking across all 20 evaluations on 5 downstream datasets, substantially outperforming existing approaches, including state-of-the-art (SOTA) EEG foundation models. These results strongly demonstrate the effectiveness and practical potential of the proposed method for real-world EEG-based AD detection. Source code: https://github.com/DL4mHealth/LEAD |
| title | LEAD: An EEG Foundation Model for Alzheimer's Disease Detection |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Signal Processing |
| url | https://arxiv.org/abs/2502.01678 |