GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis

2026-08-30

AI Summary

为解决阿尔茨海默病EEG数据集小且不平衡的问题，提出GraM-Diff框架，通过图卷积网络和双向Mamba状态空间块生成EEG数据，提高分类性能。

Abstract

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.

Problem

• Electroencephalography
• Alzheimer's disease
• Data Imbalance
• Generative Augmentation
• Brain Dynamics

Innovation

• Graph Convolutional Networks
• Bidirectional Mamba state-space blocks
• classifier guidance
• diffusion U-Net