首个大规模脑电图阿尔茨海默病检测基础模型，性能超越现有方法。
LEAD: An EEG Foundation Model for Alzheimer's Disease Detection
- 设计可适应任意长度和采样率的门控时空变压器，提升数据兼容性。
- 在2238名受试者的大规模数据集上训练，跨受试者泛化能力显著增强。
- 适用于真实世界临床场景，尤其适合医疗影像分析与神经疾病筛查研究者。
脑电图（EEG）为阿尔茨海默病（AD）检测提供了非侵入性、高可及性和低成本的途径。然而，现有方法——无论是基于手工特征工程还是标准深度学习——面临三大挑战：1）缺乏大规模用于鲁棒表征学习的EEG-AD数据集；2）跨受试者的泛化能力有限；3）难以适应高度异质的数据。为此，我们构建了迄今全球最大的EEG-AD数据集，包含2,238名受试者。基于此资源，我们提出LEAD，首个面向EEG-AD检测的大规模基础模型。具体而言，设计了一种门控时-空变压器，可适应任意长度、通道配置和采样率的EEG记录；引入受试者正则化训练策略以增强个体特征学习；并在13个数据集（含4个AD数据集和9个非AD神经系统疾病数据集）上采用医学对比学习进行预训练，随后在其余5个AD数据集上微调与测试。LEAD在5个下游数据集的全部20项评估中均取得最佳平均排名，显著优于现有方法，包括最先进的EEG基础模型。结果充分证明该方法在真实世界EEG-AD检测中的有效性和实用潜力。源代码：https://github.com/DL4mHealth/LEAD
原文摘要 · Abstract (English)
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
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