LEAD: An EEG Foundation Model for Alzheimer's Disease Detection

תקציר מקורי באנגליתarXiv:2502.01678v5 Announce Type: replace Abstract: 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 and evaluation; 2) limited cross-subject generalizability; 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 foundation model for EEG-based AD detection. Specifically, we design a gated temporal-spatial Transform