{
  "abstract": "Magnetoencephalography (MEG) is an imaging technique that offers a non-invasive, millisecond-resolution view of human brain activity. The increasing availability of MEG data presents an opportunity to take advantage of a recent advance in artificial intelligence, namely self-supervised foundation models. Existing foundation models for MEG (and electroencephalography) have been built on a transformer architecture. Here, we introduce MEG-Mamba: a generative foundation model for neural activity (source reconstructed, parcellated MEG) built on a Mamba architecture. MEG-Mamba is trained to predict the next token of a discretised MEG signal, conditioned on a brain region and recording session. MEG-Mamba surpasses the generative fidelity of a transformer-based alternative (MEG-GPT) while pre-training in less time (22 vs 400 GPU-hours) and modelling a longer context (4 vs 0.32 s). We evaluate MEG-Mamba by examining the spatio-spectral characteristics of the data it generates and by interpreting its learned embeddings. Furthermore, we demonstrate that lightweight stimulus conditioning (LoRA) can be used to generate realistic task-evoked responses that are not included in pre-training. Our results suggest that Mamba is a promising backbone for scaling neural foundation models.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00746",
  "author_total": 4,
  "authors": [
    {
      "name": "Chetan Gohil"
    },
    {
      "name": "SungJun Cho"
    },
    {
      "name": "Oiwi Parker Jones"
    },
    {
      "name": "Mark Woolrich"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9bzmwwx4hgvc5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00746",
  "fulltext_url": null,
  "id": "9bzmwwx4hgvc5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00746",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7216269758",
  "pdf_url": "https://arxiv.org/pdf/2610.00746",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9bzmwwx4hgvc5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:08Z",
      "native_id": "2610.00746",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00746"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "MEG-Mamba: A Scalable State-Space Foundation Model for Magnetoencephalography",
  "title_text": "MEG-Mamba: A Scalable State-Space Foundation Model for Magnetoencephalography",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:08Z",
  "url": "https://research.draconitic.com/paper/9bzmwwx4hgvc5",
  "venue": "arXiv",
  "versions": []
}