Skip to main content
Pre-release

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

Join the chat at https://gitter.im/braindecodechat/community https://github.com/braindecode/braindecode/workflows/docs/badge.svg Doc build on CircleCI Code Coverage

Braindecode

Braindecode is an open-source Python toolbox for decoding raw electrophysiological brain data with deep learning models. It includes dataset fetchers, data preprocessing and visualization tools, as well as implementations of several deep learning architectures and data augmentations for analysis of EEG, ECoG and MEG.

For neuroscientists who want to work with deep learning and deep learning researchers who want to work with neurophysiological data.

Installation Braindecode

  1. Install pytorch from http://pytorch.org/ (you don’t need to install torchvision).

  2. If you want to download EEG datasets from MOABB, install it:

pip install moabb
  1. Install latest release of braindecode via pip:

pip install braindecode

If you want to install the latest development version of braindecode, please refer to contributing page

Documentation

Documentation is online under https://braindecode.org, both in the stable and dev versions.

Contributing to Braindecode

Guidelines for contributing to the library can be found on the braindecode github:

https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md

Braindecode chat

https://gitter.im/braindecodechat/community

Citing

If you use this code in a scientific publication, please cite us as:

@article {HBM:HBM23730,
author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,
  Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and
  Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
journal = {Human Brain Mapping},
issn = {1097-0193},
url = {http://dx.doi.org/10.1002/hbm.23730},
doi = {10.1002/hbm.23730},
month = {aug},
year = {2017},
keywords = {electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,
  brain–computer interface, model interpretability, brain mapping},
}

as well as the MNE-Python software that is used by braindecode:

@article{10.3389/fnins.2013.00267,
author={Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti},
title={{MEG and EEG data analysis with MNE-Python}},
journal={Frontiers in Neuroscience},
volume={7},
pages={267},
year={2013},
url={https://www.frontiersin.org/article/10.3389/fnins.2013.00267},
doi={10.3389/fnins.2013.00267},
issn={1662-453X},
}

Licensing

This project is primarily licensed under the BSD-3-Clause License.

Additional Components

Some components within this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Please refer to the LICENSE and NOTICE files for more detailed information.

Metadata

Release files for braindecode 1.3.0.dev181594385

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

Source distribution (sdist)

Source distribution for braindecode 1.3.0.dev181594385
File Size Uploaded Provenance Actions
braindecode-1.3.0.dev181594385.tar.gz 328.1 kB Without provenance Details for braindecode-1.3.0.dev181594385.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for braindecode 1.3.0.dev181594385
File Interpreter ABI Platform Actions
braindecode-1.3.0.dev181594385-py3-none-any.whl Python 3 none any Details for braindecode-1.3.0.dev181594385-py3-none-any.whl

Total release size: 680.3 kB

Release files / braindecode-1.3.0.dev181594385.tar.gz

Download URL braindecode-1.3.0.dev181594385.tar.gz
Size 328.1 kB
Tags Source
SHA-256 checksum
How to use checksums
bda2fa94a34e34e7ff983ad9734c80c3320de587e6822858ab38715492791d5f
BLAKE2b-256 checksum
How to use checksums
39758bc5f293b3fec62cde2444f7e79f835fed71021c3833d7a0a94dfbf36441
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.7

Release files / braindecode-1.3.0.dev181594385-py3-none-any.whl

Download URL braindecode-1.3.0.dev181594385-py3-none-any.whl
Size 352.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
055e6baf2998688e8e5658c872bf8a58775106e6550134482f8299788e927e63
BLAKE2b-256 checksum
How to use checksums
6dcd60338c6cc750ad7084a296cb40fb8c6801a40a82310cec861efbba2eda81
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.7

Security for braindecode 1.3.0.dev181594385

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release
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