{
  "abstract": "Public recordings of electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG (iEEG), functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) are easy to find and hard to train on. Turning a deposit into model input still means writing download, parsing, repair and windowing code for each dataset, and a dataset that meets a metadata standard can still fail to load. EEGDash is an open-source registry over public Brain Imaging Data Structure (BIDS) datasets from the OpenNeuro and NEMAR archives and from local deposits. Each dataset becomes a Python class; format defects are repaired lazily; signal handling, windowing and format checking are delegated to MNE-Python, Braindecode and the official BIDS validator. Semantic search, a web browser, publication-derived dataset tags and a feature-extraction module share the registry. EEGDash indexes the metadata of 892 public BIDS datasets (43,911 participants and 126,672 hours across the five modalities), loads any of them into a PyTorch dataset without custom data-loading code, and shows on 503 OpenNeuro EEG datasets that passing the BIDS validator does not predict whether a dataset loads (Spearman $ρ= -0.05$), so an archive built for machine learning has to track both.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.16041",
  "author_total": 10,
  "authors": [
    {
      "name": "Bruno Aristimunha"
    },
    {
      "name": "Aviv Dotan"
    },
    {
      "name": "Pierre Guetschel"
    },
    {
      "name": "Aman Jaiswal"
    },
    {
      "name": "Gal Ashkenazi"
    },
    {
      "name": "Dung Truong"
    },
    {
      "name": "Kuntal Kokate"
    },
    {
      "name": "Amit Majumdar"
    },
    {
      "name": "Oren Shriki"
    },
    {
      "name": "Arnaud Delorme"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dbew1ejdjp7dw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.16041",
  "fulltext_url": null,
  "id": "dbew1ejdjp7dw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.16041",
  "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": "W7164887302",
  "pdf_url": "https://arxiv.org/pdf/2606.16041",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dbew1ejdjp7dw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:21Z",
      "native_id": "2606.16041",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.16041"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "EEGDash: An open-source platform for machine learning on public neurophysiological data",
  "title_text": "EEGDash: An open-source platform for machine learning on public neurophysiological data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:21Z",
  "url": "https://research.draconitic.com/paper/dbew1ejdjp7dw",
  "venue": "arXiv",
  "versions": []
}