LEAD: Large Foundation Model for EEG-Based Alzheimer’s Disease Detection

Yihe Wang, Nan Huang, Nadia Mammone, Marco Cecchi, Xiang Zhang

TL;DR: The world's first large foundation model for EEG-based Alzheimer’s Disease detection trained on the largest EEG-AD corpus to date.

Abstract: Electroencephalogram (EEG) provides a non-invasive, highly accessible, and cost-effective solution for Alzheimer’s Disease (AD) detection. However, existing methods, whether based on manual feature extraction or deep learning, face two major challenges: the lack of large-scale datasets for robust feature learning and evaluation, and poor detection performance due to inter-subject variations. To address these challenges, we curate an EEG-AD corpus containing 813 subjects, which forms the world’s largest EEG-AD dataset to the best of our knowledge. Using this unique dataset, we propose **LEAD**, the first large foundation model for EEG-based AD detection. Our method encompasses an entire pipeline, from data selection and preprocessing to self-supervised contrastive pretraining, fine-tuning, and key setups such as subject-independent evaluation and majority voting for subject-level detection. We pre-train the model on 11 EEG datasets (4 AD and 7 non-AD) and unified fine-tune it on 5 AD datasets. Our self-supervised pretraining design includes sample-level and subject-level contrastive learning to extract useful general EEG features. Fine-tuning is performed on 5 channel-aligned datasets together. The backbone encoder incorporates temporal and channel embeddings to capture features across both temporal and spatial dimensions. Our method demonstrates outstanding AD detection performance, achieving up to a 9.86% increase in F1 score at the sample level and up to a 9.31% improvement at the subject level compared to state-of-the-art methods. The results of our model strongly confirm the effectiveness of subject-level contrastive pretraining and channel-aligned multi-dataset fine-tuning for addressing inter-subject variation. The source code is at \url{https://anonymous.4open.science/r/LEAD-3B51}.

Primary Area: Applications->Neuroscience, Cognitive Science

Keywords: EEG, Alzheimer’s Disease, Foundation Model

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Submission Number: 9511