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

arXiv:2608.29755 · cs.LG · Submitted 2026-08-30 · Read on arXiv

cs.LG

Submitted: 2026-08-30

Updated: 2026-08-30

Journal ref: MICCAI 2026 EMERGE

Code: https://github.com/mtanveer1/GraM-Diff

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 11× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Mode Collapse — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
• EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
• LEAD: An EEG Foundation Model for Alzheimer's Disease Detection
• ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
• Diffusion-TS: Interpretable Diffusion for General Time Series Generation

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