GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis
On the map: State-space sequence models (S4, Mamba) → Physiological signal modeling
Abstract (arXiv)
Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning methods are limited by small and imbalanced clinical datasets. Generative augmentation offers a solution, yet existing approaches rely on inefficient class-specific models or fail to capture complex spatial and temporal brain dynamics. To address this, we propose GraM-Diff, a unified classifier-guided Graph-Mamba diffusion framework for EEG synthesis. It embeds Graph Convolutional Networks within a diffusion U-Net to model inter-electrode connectivity and Bidirectional Mamba state-space blocks for linear-complexity long-range temporal modeling. Latent-space classifier guidance lets a single model generate both healthy and pathological EEG within a shared representation, avoiding fragmented per-cohort pipelines. Across four EEG-based AD benchmarks, synthetic augmentation improves classification, yields superior Context-FID and correlation scores over strong generative baselines, and enhances robustness in data-scarce settings.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 11 of its 24 references that are in the map. First posted on arXiv 30 Aug 2026.
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- 2024EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification20 Jul
Other references · 10
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- 2021EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophreniacited by 6
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- 2025Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
- 2026DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection
- 2025Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer's Disease Prediction
- 2023BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model
- 2025EEGDM: EEG Representation Learning via Generative Diffusion Model
- 2024Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models
- 2022Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
- 2024GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI