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

arXiv:2502.01678 · cs.LG, cs.AI, cs.CE, eess.SP · Submitted 2025-02-02 · Read on arXiv

cs.LG, cs.AI, cs.CE, eess.SP

Submitted: 2025-02-02

Updated: 2026-09-25

Code: https://github.com/DL4mHealth/LEAD

Terminology

• Foundation Model — 65× in this paper · explained in 3 episodes
• Transformer — 27× in this paper · explained in 2 episodes
• Accuracy — 22× in this paper · explained in 1 episode
• F1 Score — 22× in this paper · explained in 3 episodes
• Embeddings — 18× in this paper · explained in 2 episodes
• Contrastive Learning — 15× in this paper · explained in 5 episodes
• Representation Learning — 11× in this paper · explained in 2 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Feature Extraction — 9× in this paper · explained in 2 episodes
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Mild Cognitive Impairment — 9× in this paper
• Healthy Controls — 8× in this paper
• alpha — 7× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Data Augmentation — 4× in this paper · explained in 7 episodes
• Transformer Architecture — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Entrainment — 3× in this paper · explained in 2 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• Bispectrum — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Feature Engineering — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Independent Component Analysis — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Major Depressive Disorder — 2× in this paper
• Time Series Forecasting — 2× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• First-Episode Psychosis — 1× in this paper
• GNNs — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Multi-Layer Perceptrons — 1× in this paper
• Nadia — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Stimulus-Selective Response Modulation — 1× in this paper

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• NeurIPT: Foundation Model for Neural Interfaces
• NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals
• MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification
• REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
• ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
• BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training
• CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
• Eeg2vec: Self-Supervised Electroencephalographic Representation Learning

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