Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

EEG-Dash

PyPI version Docs

License: BSD-3-Clause Python versions Downloads Coverage

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 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.

pip install eegdash

Verify the install in a Python session:

from eegdash import EEGDash

See the tutorials at 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.

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

Release files for eegdash 0.8.0.dev174801290

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for eegdash 0.8.0.dev174801290
File Size Uploaded
eegdash-0.8.0.dev174801290.tar.gz 360.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for eegdash 0.8.0.dev174801290
File Interpreter ABI Platform
eegdash-0.8.0.dev174801290-py3-none-any.whl Python 3 none any Details

Total release size: 619.6 kB

Release files / eegdash-0.8.0.dev174801290.tar.gz

Download URL eegdash-0.8.0.dev174801290.tar.gz
Size 360.6 kB
Tags Source
SHA-256 checksum
How to use checksums
efd59a21eac776969ab75049aa662bdeb3a98a6dac5dff4543715d0311788973
BLAKE2b-256 checksum
How to use checksums
428d1b497ddf07567744fb2f6d235775ce3b4b42b1c2cd09a21a7305764ad2d6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.12

Release files / eegdash-0.8.0.dev174801290-py3-none-any.whl

Download URL eegdash-0.8.0.dev174801290-py3-none-any.whl
Size 259.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
184e62b245d533cc48112e2cbc2e33189df8cbe92ecf4ad4a318380dc585c44c
BLAKE2b-256 checksum
How to use checksums
ba63de5df7328028d8b8e010d80124586a00c85a4d39e08200dbf1bdd79fc7df
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.12

Release history Release notifications | RSS feed

0.9.1

2 release files

0.9.0

2 release files

0.8.5

2 release files

0.8.4

2 release files

0.8.3

2 release files

0.8.2

2 release files

0.8.1

2 release files

0.8.0

2 release files

This release

0.7.2

2 release files

0.7.0

2 release files

0.6.0

2 release files

0.5.0

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.8

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page