Skip to main content
Pre-release

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

DOI Docs Build Status Test Build Status Code Coverage PyPI Version Python versions Downloads

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

Citing

If you use Braindecode in scientific work, please cite the software using the global Zenodo DOI shown in the badge below:

DOI

You can use the following BibTeX entry:

@software{braindecode,
  author = {Aristimunha, Bruno and
            Guetschel, Pierre and
            Wimpff, Martin and
            Gemein, Lukas and
            Rommel, Cedric and
            Banville, Hubert and
            Sliwowski, Maciej and
            Wilson, Daniel and
            Brandt, Simon and
            Gnassounou, Théo and
            Paillard, Joseph and
            {Junqueira Lopes}, Bruna and
            Sedlar, Sara and
            Moreau, Thomas and
            Chevallier, Sylvain and
            Gramfort, Alexandre and
            Schirrmeister, Robin Tibor},
  title = {Braindecode: toolbox for decoding raw electrophysiological brain data
           with deep learning models},
  url = {https://github.com/braindecode/braindecode},
  doi = {10.5281/zenodo.17699192},
  publisher = {Zenodo},
  license = {BSD-3-Clause},
}

Additionally, we highly encourage you to cite the article that originally introduced the Braindecode library and has served as a foundational reference for many works on deep learning with EEG recordings. Please use the following reference:

@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 other licenses, including Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), MIT and Apache-2.0.

Please refer to the LICENSE and NOTICE files for the per-file list and more detailed information.

Metadata

Release files for braindecode 1.8.1.dev183897738

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 1.8.1.dev183897738
File Size Uploaded
braindecode-1.8.1.dev183897738.tar.gz 693.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for braindecode 1.8.1.dev183897738
File Interpreter ABI Platform
braindecode-1.8.1.dev183897738-py3-none-any.whl Python 3 none any Details

Total release size: 1.4 MB

Release files / braindecode-1.8.1.dev183897738.tar.gz

Download URL braindecode-1.8.1.dev183897738.tar.gz
Size 693.1 kB
Tags Source
SHA-256 checksum
How to use checksums
80dd21a59f79675aa94a260c7bebcf7cb2166ca888e1563cbcd21c3470b80c2b
BLAKE2b-256 checksum
How to use checksums
28258f0af8537e6b965bcf62e3563733068d1e094bbdd04dce1dc13542c993b3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / braindecode-1.8.1.dev183897738-py3-none-any.whl

Download URL braindecode-1.8.1.dev183897738-py3-none-any.whl
Size 710.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b922098063a7310dcbdcd22e5933a3bc638dff8f8b339c9368aecbd716f6fc6c
BLAKE2b-256 checksum
How to use checksums
12335d1231471276b3edffc7023cc5b4692f62cbd50a6db7f66f8f9819fbf17a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release history Release notifications | RSS feed

1.8.1

2 release files

This release

1.8.0

2 release files

1.7.0

2 release files

1.6.1

2 release files

1.5.2

2 release files

1.5.1

2 release files

1.5.0

2 release files

1.4.0

2 release files

1.3.2

2 release files

1.3.1

2 release files

1.3.0

2 release files

1.2.0

2 release files

1.1.0

2 release files

1.0.0

2 release files

0.8.1

2 release files

0.7

2 release files

0.6

2 release files

0.5.1

2 release files

0.5

1 release file

0.4.85

1 release file

0.4.84

1 release file

0.4.83

1 release file

0.4.82

1 release file

0.4.81

1 release file

0.4.8

1 release file

0.4.7

1 release file

0.4.6

1 release file

0.4.5

1 release file

0.4.4

1 release file

0.4.3

1 release file

0.4.1

1 release file

0.4.0

1 release file

0.3.2

1 release file

0.3.1

1 release file

0.3.0

1 release file

0.2.1

1 release file

0.2.0

1 release file

0.1.9

1 release file

0.1.8

1 release file

0.1.7

1 release file

0.1.6.4

1 release file

0.1.6.3

1 release file

0.1.6.2

1 release file

0.1.6.1

1 release file

0.1.6

1 release file

0.1.5

1 release file

0.1.4

1 release file

0.1.3

1 release file

0.1.2

1 release file

0.1.1

1 release file

0.1.0

1 release file

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