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<section class="hero">
  <svg viewBox="0 0 900 150" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="EEGDash catalog banner: 736 datasets, 40k subjects, 85k hours">
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    <text x="28" y="74" font-family="Inter, system-ui, sans-serif" font-size="13" font-weight="400" fill="#64748B">open catalog of EEG / MEG datasets · load with one line of Python</text>
    <text x="28" y="118" font-family="Inter, system-ui, sans-serif" font-size="14" fill="#334155"><tspan font-weight="700" fill="#0F172A" font-size="22">736</tspan> datasets · <tspan font-weight="700" fill="#0F172A" font-size="22">40,361</tspan> subjects · <tspan font-weight="700" fill="#0F172A" font-size="22">85,298</tspan> hours</text>
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<p class="tagline"><em>The open catalog of EEG / MEG datasets — indexed, described, and loadable with one line of Python.</em></p>

<div class="badges">
  <a href="https://pypi.org/project/eegdash/"><img src="https://img.shields.io/pypi/v/eegdash?style=flat-square&logo=pypi&logoColor=white&color=0072B2" alt="PyPI" /></a>
  <a href="https://pypi.org/project/eegdash/"><img src="https://img.shields.io/pypi/pyversions/eegdash?style=flat-square&color=0072B2" alt="Python" /></a>
  <a href="https://github.com/eegdash/EEGDash/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-BSD--3--Clause-009E73?style=flat-square" alt="License" /></a>
  <a href="https://pepy.tech/project/eegdash"><img src="https://static.pepy.tech/badge/eegdash" alt="Downloads" /></a>
  <a href="https://github.com/eegdash/EEGDash"><img src="https://img.shields.io/github/stars/eegdash/EEGDash?style=flat-square&logo=github&color=E69F00" alt="GitHub stars" /></a>
</div>

<p class="intro">Welcome to the official Hugging Face org for <strong><a href="https://eegdash.org">EEGDash</a></strong>. Raw EEG/MEG recordings are never rehosted here — each dataset on this page is a <strong>pointer</strong> to its canonical source (OpenNeuro, NEMAR, or the lab that collected it), and <code>EEGDashDataset</code> handles download, caching, and conversion to a PyTorch-ready <a href="https://braindecode.org">braindecode</a> object. One CSV drives the whole catalog; every card you see here regenerates from it automatically.</p>

<div class="ctas">
  <a href="https://huggingface.co/spaces/EEGDash/catalog">🗺️ Browse the interactive catalog</a>
  <a href="https://eegdash.org">📚 Docs</a>
  <a href="https://github.com/eegdash/EEGDash">💻 GitHub</a>
  <a href="https://pypi.org/project/eegdash/">📦 PyPI</a>
</div>

<h2>Catalog shape</h2>

<div class="shape">
<svg viewBox="0 0 900 60" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Datasets by experimental paradigm">
  <text x="0" y="12" font-family="Inter, system-ui, sans-serif" font-size="11" font-weight="600" fill="#64748B" letter-spacing="1.2">EXPERIMENTAL PARADIGM</text>
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    <rect x="708" y="22" width="30"  height="18" fill="#CC79A7"/>
    <rect x="738" y="22" width="28"  height="18" fill="#56B4E9"/>
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    <text x="0"   y="56">■ Visual 300</text>
    <text x="110" y="56">■ Auditory 59</text>
    <text x="205" y="56">■ Multi. 35</text>
    <text x="280" y="56">■ Other 26</text>
    <text x="345" y="56">■ Rest. 22</text>
    <text x="408" y="56">■ Motor 17</text>
    <text x="468" y="56">■ Tactile 16</text>
    <text x="535" y="56">■ Sleep 13</text>
    <text x="595" y="56">■ Anesth. 4</text>
    <text x="670" y="56" fill="#94A3B8">+ 207 unclassified</text>
  </g>
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<p class="stats-line"><strong>In numbers:</strong> the archive indexes <strong>736</strong> EEG / MEG datasets totalling <strong>40,361</strong> subjects, <strong>222,750</strong> recordings, and <strong>85,298 hours</strong> of signal. <strong>600+</strong> are already mirrored on 🤗 and growing daily, sourced from <a href="https://openneuro.org"><strong>OpenNeuro</strong></a> (546) and <a href="https://nemar.org"><strong>NEMAR</strong></a> (190). By recording type: <strong>571 EEG · 73 iEEG · 55 MEG · 22 fNIRS</strong>, plus a handful of multimodal combos.</p>

<h2>Featured datasets</h2>

<p>A handful of representative entries, grouped by population. Every slug links to its HF card; every card links back to the canonical source.</p>

<table>
<thead>
<tr>
<th>🟢 Healthy / neurotypical</th>
<th>🟠 Clinical populations</th>
<th>🟡 Developmental (HBN)</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>ds002718</strong> · Visual, 18 subj<br>Face processing (Wakeman &amp; Henson)<br><a href="https://huggingface.co/datasets/EEGDash/ds002718">HF</a> · <code>Wakeman2015</code></td>
<td><strong>ds003800</strong> · Resting, PD<br>EEG in Parkinson&rsquo;s disease<br><a href="https://huggingface.co/datasets/EEGDash/ds003800">HF</a></td>
<td><strong>EEG2025r1</strong> · 10 paradigms, 136 subj<br>Healthy Brain Network release 1<br><a href="https://huggingface.co/datasets/EEGDash/eeg2025r1">HF</a> · <code>HBN_r1_bdf</code></td>
</tr>
<tr>
<td><strong>ds000117</strong> · Visual, MEG + EEG<br>Multimodal face processing<br><a href="https://huggingface.co/datasets/EEGDash/ds000117">HF</a> · <code>WakemanHenson_MEEG</code></td>
<td><strong>ds002799</strong> · Clinical monitoring<br>Patient-day recording, dementia<br><a href="https://huggingface.co/datasets/EEGDash/ds002799">HF</a></td>
<td><strong>EEG2025r10</strong> · 8 paradigms, 533 subj<br>HBN release 10 — 32 GB<br><a href="https://huggingface.co/datasets/EEGDash/eeg2025r10">HF</a></td>
</tr>
<tr>
<td><strong>ds000246</strong> · Auditory, MEG<br>CTF 275-channel MEG<br><a href="https://huggingface.co/datasets/EEGDash/ds000246">HF</a></td>
<td><strong>ds004551</strong> · iEEG<br>Intracranial recordings, surgical<br><a href="https://huggingface.co/datasets/EEGDash/ds004551">HF</a></td>
<td><strong>EEG2025r10mini</strong> · 20 subj<br>HBN mini release for tutorials<br><a href="https://huggingface.co/datasets/EEGDash/eeg2025r10mini">HF</a></td>
</tr>
<tr>
<td><strong>ds003061</strong> · Auditory<br>Speech / naturalistic listening<br><a href="https://huggingface.co/datasets/EEGDash/ds003061">HF</a></td>
<td><strong>ds004598</strong> · Motor<br>Motor paradigm study<br><a href="https://huggingface.co/datasets/EEGDash/ds004598">HF</a></td>
<td>… 22 HBN releases total<br><a href="https://huggingface.co/EEGDash?search_datasets=eeg2025">browse all HBN</a></td>
</tr>
</tbody>
</table>

<a class="browse-cta" href="https://huggingface.co/EEGDash">Browse all 736 datasets →</a>

<h2>Get started in 30 seconds</h2>

<pre><code>pip install eegdash</code></pre>

<pre><code>from eegdash import EEGDashDataset

# Load any dataset in the catalog by its ID…
ds = EEGDashDataset(dataset="ds002718", cache_dir="./cache")

# …or by canonical alias — every known name is a registered class:
from eegdash.dataset import Wakeman2015
ds = Wakeman2015(cache_dir="./cache")

# …or pull a Hub-mirrored, pre-windowed Zarr copy:
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds002718")

# EEGDash datasets ARE braindecode datasets — plug into PyTorch unchanged.
from torch.utils.data import DataLoader
loader = DataLoader(ds, batch_size=32, shuffle=True)</code></pre>

<h2>Contribute</h2>

<p>Missing a dataset? Wrong metadata? The whole catalog regenerates from one CSV — fix once, propagate everywhere. <strong><a href="https://github.com/eegdash/EEGDash/issues">Open an issue</a></strong> or see <a href="https://github.com/eegdash/EEGDash/blob/main/CONTRIBUTING.md">CONTRIBUTING.md</a>.</p>

<h2>Cite</h2>

<p>If you use EEGDash in your research, please cite the software entry below (and the companion paper once it&rsquo;s available):</p>

<pre><code>@software{eegdash,
  title     = {EEG-DaSh: an open data, tool, and compute resource for machine learning on neuroelectromagnetic data},
  author    = {Aristimunha, Bruno and Dotan, Aviv and Guetschel, Pierre and Truong, Dung
               and Kokate, Kuntal and Jaiswal, Aman and Majumdar, Amitrava
               and Shirazi, Seyed Yahya and Shriki, Oren and Delorme, Arnaud},
  year      = {2026},
  version   = {0.6.0},
  license   = {BSD-3-Clause},
  url       = {https://eegdash.org},
  howpublished = {\url{https://github.com/eegdash/EEGDash}}
}</code></pre>

<p>When you use a specific dataset, always follow its upstream citation policy — the link lives in every dataset&rsquo;s HF card under <em>How to cite</em>.</p>

<footer>
EEGDash code is BSD-3-Clause. Each dataset retains its upstream license — always check the card before redistribution. <em>Open, indexed, loadable.</em>
</footer>

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