MEG-Mamba: A Scalable State-Space Foundation Model for Magnetoencephalography

arXiv:2610.00746 · q-bio.NC · Submitted 2026-09-30 · Read on arXiv

q-bio.NC

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/OHBA-analysis/MEG-Mamba

Terminology

• Foundation Model — 54× in this paper · explained in 3 episodes
• Embeddings — 27× in this paper · explained in 2 episodes
• Foundation Models — 27× in this paper · explained in 7 episodes
• Transformer — 13× in this paper · explained in 2 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 6× in this paper
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• In International Conference on Machine Learning — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Generative Model — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• On the Opportunities and Risks of Foundation Models
• A Roadmap for MEG Foundation Models
• BrainWave: A Brain Signal Foundation Model for Clinical Applications
• BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training
• DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations
• BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression
• B[FM]$^2$: Brain Foundation Model via Flow Matching with SplitUNet
• Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction
• Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
• Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal
• CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models
• SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting
• A Systematic Evaluation of Sample-Level Tokenization Strategies for MEG Foundation Models

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