用统一模型生成和诊断阿尔茨海默病脑电图，提升小样本数据表现。
GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis
- 融合图卷积与双向Mamba，建模脑电极间连接和长时序动态。
- 单模型生成健康与病理脑电，合成数据使分类性能提升12.3%。
- 适合脑电分析、医疗生成模型研究者使用。
脑电图（EEG）是检测阿尔茨海默病（AD）的有前景、非侵入且低成本的手段，但深度学习方法受限于临床数据集小且不平衡。生成式数据增强可缓解此问题，但现有方法依赖效率低下的类别特定模型，或无法捕捉复杂的时空脑活动。为此，我们提出GraM-Diff：一种统一的分类器引导型图-马尔萨模型扩散框架，用于脑电合成。该框架将图卷积网络嵌入扩散U-Net以建模电极间连接，采用双向Mamba状态空间块实现线性复杂度的长程时序建模。通过潜在空间分类器引导，单一模型可在共享表征中生成健康与病理脑电信号，避免了分段的按队列处理流程。在四个基于EEG的阿尔茨海默病基准测试中，合成数据增强提升了分类性能，优于强基线模型的上下文FID和相关性评分，并在数据稀缺场景下增强了鲁棒性。
原文摘要 · Abstract (English)
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.
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