Metadata-Version: 2.4
Name: eegdash
Version: 0.7.2
Summary: EEG-DaSh: an open data, tool, and compute resource — a Python library and catalog for 700+ BIDS-first EEG, MEG, fNIRS, EMG, and iEEG datasets, ML-ready via PyTorch
Author: Kuntal Kokate, Amitrava Majumdar, Seyed Yahya Shirazi
Author-email: Bruno Aristimunha <b.aristimunha@gmail.com>, Aviv Dotan <avivdot@bgu.post.ac.il>, Pierre Guetschel <pierre.guetschel@donders.ru.nl>, Dung Truong <dt.young112@gmail.com>, Aman Jaiswal <aman.jaiswal.1503@gmail.com>, Oren Shriki <shrikio@bgu.ac.il>, Arnaud Delorme <adelorme@ucsd.edu>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://eegdash.org
Project-URL: Documentation, https://eegdash.org
Project-URL: Repository, https://github.com/eegdash/EEGDash
Project-URL: Source, https://github.com/eegdash/EEGDash
Project-URL: Issues, https://github.com/eegdash/EEGDash/issues
Project-URL: Bug Tracker, https://github.com/eegdash/EEGDash/issues
Project-URL: Changelog, https://github.com/eegdash/EEGDash/releases
Project-URL: Discussions, https://github.com/eegdash/EEGDash/discussions
Project-URL: Chat, https://discord.gg/8jd7nVKwsc
Keywords: eeg,electroencephalography,brain-computer-interface,bci,neuroscience,machine-learning,deep-learning,bids,mne,signal-processing,feature-extraction,dataset
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: braindecode[hub]>=1.4.0
Requires-Dist: eeglabio
Requires-Dist: mne>=1.11.0
Requires-Dist: mne_bids>=0.18.0
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Requires-Dist: pymatreader
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Requires-Dist: eegdash[features]
Provides-Extra: features
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Provides-Extra: tests
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Dynamic: license-file

# EEG-Dash

[![PyPI version](https://img.shields.io/pypi/v/eegdash)](https://pypi.org/project/eegdash/)
[![Docs](https://img.shields.io/badge/docs-stable-brightgreen.svg)](https://sccn.github.io/eegdash)

[![License: BSD-3-Clause](https://img.shields.io/badge/License-BSD--3--Clause-blue.svg)](LICENSE)
[![Python versions](https://img.shields.io/pypi/pyversions/eegdash.svg)](https://pypi.org/project/eegdash/)
[![Downloads](https://pepy.tech/badge/eegdash)](https://pepy.tech/project/eegdash)
[![Coverage](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fraw.githubusercontent.com%2Feegdash%2FEEGDash%2Fmain%2Fcoverage.json&query=%24.totals.percent_covered_display&suffix=%25&label=coverage)](https://github.com/eegdash/EEGDash/blob/main/coverage.json)

EEG-DaSh is a data-sharing archive for MEEG (EEG, MEG) recordings contributed by collaborating labs. It preserves publicly funded research data and exposes it in a form that machine learning and deep learning workflows can use directly.

## Data source

The archive draws on 25 labs and 27,053 participants, with recordings covering both EEG and MEG. Subjects include healthy controls and clinical groups: ADHD, depression, schizophrenia, dementia, autism, and psychosis. Tasks range across sleep, meditation, and cognitive paradigms. EEG-DaSh also pulls in 330 BIDS-formatted MEEG datasets converted from NEMAR.

## Data format

EEGDash queries return a **PyTorch Dataset**. The format plugs directly into PyTorch's `DataLoader` for batching, shuffling, and parallel loading, which matters when training models on large EEG corpora.

## Data preprocessing

EEGDash datasets are [braindecode](https://braindecode.org/stable/index.html) datasets, which are themselves PyTorch datasets. Any preprocessing that works on a braindecode dataset works on an EEGDash dataset. See the braindecode tutorials for the available options.

## EEG-Dash usage

### Install
Requires Python 3.10 or higher. Use whichever environment manager you prefer.

```bash
pip install eegdash
```

Verify the install in a Python session:

```python
from eegdash import EEGDash
```

See the tutorials at [eegdash.org](https://eegdash.org/) for end-to-end examples.

## Education (coming soon)

We run workshops and student training events with US and Israeli partners, online and in person. 2025 dates will go out on the EEGLABNEWS mailing list. [Subscribe here](https://sccn.ucsd.edu/mailman/listinfo/eeglabnews).

## About EEG-DaSh

EEG-DaSh is a collaborative initiative between the United States and Israel, supported by the National Science Foundation (NSF). The partnership brings together experts from the Swartz Center for Computational Neuroscience (SCCN) at the University of California San Diego (UCSD) and Ben-Gurion University (BGU) in Israel. 

![Screenshot 2024-10-03 at 09 14 06](https://github.com/user-attachments/assets/327639d3-c3b4-46b1-9335-37803209b0d3)



