This release is a pre-release and may not be stable for production use.
EEG-Dash
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.
2 maintainers
Release files for eegdash 0.7.2.dev183310367
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| eegdash-0.7.2.dev183310367.tar.gz | 285.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| eegdash-0.7.2.dev183310367-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 530.8 kB
Release files / eegdash-0.7.2.dev183310367.tar.gz
| Download URL | eegdash-0.7.2.dev183310367.tar.gz |
|---|---|
| Size | 285.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
85892fe69d556306467baf7ba314ae4cecce13a5432a191b8a70a1c2b6d0b1ba
|
|
BLAKE2b-256 checksum How to use checksums |
74721b2e1a626ef12878709af70874b15ffc2448f49ef6fed3daacfa3ffb622d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Release files / eegdash-0.7.2.dev183310367-py3-none-any.whl
| Download URL | eegdash-0.7.2.dev183310367-py3-none-any.whl |
|---|---|
| Size | 244.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c8a84e895ce39f96c045224d115492e141885745618a82189a125938aed6ae21
|
|
BLAKE2b-256 checksum How to use checksums |
842a7eeda34c8182264eadc2e7d5d798d2927ae5c0e88bef752ecc0b6577fb25
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
