---
title: README
emoji: 📚
colorFrom: blue
colorTo: indigo
sdk: static
app_file: index.html
pinned: true
license: bsd-3-clause
short_description: PyTorch catalog of EEG/MEG/EMG/iEEG/fNRIS datasets.
---

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  <text x="28" y="48" font-family="Inter, system-ui, sans-serif" font-size="28" font-weight="700" fill="#0F172A">EEGDash</text>
  <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>
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  <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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*The open catalog of EEG / MEG datasets — indexed, described, and loadable with one line of Python.*

[![PyPI](https://img.shields.io/pypi/v/eegdash?style=flat-square&logo=pypi&logoColor=white&color=0072B2)](https://pypi.org/project/eegdash/)
[![Python](https://img.shields.io/pypi/pyversions/eegdash?style=flat-square&color=0072B2)](https://pypi.org/project/eegdash/)
[![License](https://img.shields.io/badge/license-BSD--3--Clause-009E73?style=flat-square)](https://github.com/eegdash/EEGDash/blob/main/LICENSE)
[![Downloads](https://static.pepy.tech/badge/eegdash)](https://pepy.tech/project/eegdash)
[![GitHub](https://img.shields.io/github/stars/eegdash/EEGDash?style=flat-square&logo=github&color=E69F00)](https://github.com/eegdash/EEGDash)

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

**[🗺️ Browse the interactive catalog](https://huggingface.co/spaces/EEGDash/catalog)** · [📚 Docs](https://eegdash.org) · [💻 GitHub](https://github.com/eegdash/EEGDash) · [📦 PyPI](https://pypi.org/project/eegdash/)

## Catalog shape

<svg viewBox="0 0 900 60" xmlns="http://www.w3.org/2000/svg" width="900" height="60" role="img" aria-label="Datasets by experimental paradigm">
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  <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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    <text x="0"   y="56">■ Visual 300</text>
    <text x="110" y="56" fill="#334155">■ 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>
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**In numbers:** the archive indexes **736** EEG / MEG datasets totalling **40,361** subjects, **222,750** recordings, and **85,298 hours** of signal. **600+** are already mirrored on 🤗 and growing daily, sourced from **[OpenNeuro](https://openneuro.org)** (546) and **[NEMAR](https://nemar.org)** (190). By recording type: **571 EEG · 73 iEEG · 55 MEG · 22 fNIRS**, plus a handful of multimodal combos.

## Featured datasets

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

<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 & 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'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>

**[Browse all 600 mirrored datasets →](https://huggingface.co/EEGDash)**

## Get started in 30 seconds

```bash
pip install eegdash
```

```python
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)
```

## Contribute

Missing a dataset? Wrong metadata? The whole catalog regenerates from one CSV — fix once, propagate everywhere. **[Open an issue](https://github.com/eegdash/EEGDash/issues)** or see **[CONTRIBUTING.md](https://github.com/eegdash/EEGDash/blob/main/CONTRIBUTING.md)**.

## Cite

If you use EEGDash in your research, please cite the software entry below (and the companion paper once it's available):

```bibtex
@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}}
}
```

When you use a specific dataset, always follow its upstream citation policy — the link lives in every dataset's HF card under *How to cite*.

---

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