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.dev173935361

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.dev173935361
File Size Uploaded
braindecode-1.8.1.dev173935361.tar.gz 900.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for braindecode 1.8.1.dev173935361
File Interpreter ABI Platform
braindecode-1.8.1.dev173935361-py3-none-any.whl Python 3 none any Details

Total release size: 1.8 MB

Release files / braindecode-1.8.1.dev173935361.tar.gz

Download URL braindecode-1.8.1.dev173935361.tar.gz
Size 900.8 kB
Tags Source
SHA-256 checksum
How to use checksums
965e16985c76d627e1f898cd2eb9f884245b0cf85af85e36e7aa2c354e1a0a65
BLAKE2b-256 checksum
How to use checksums
afd1e5b87edfbd51450f73a26b065101bcc03fdd2edb651c6d4bd7882a6a52e5
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.dev173935361-py3-none-any.whl

Download URL braindecode-1.8.1.dev173935361-py3-none-any.whl
Size 903.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
07ea028bbdb9731abcb0e81e3e5253ce4090cc7981bdc23a27a798d552cc7674
BLAKE2b-256 checksum
How to use checksums
ed8f5cd7d9b7d04d41841c7edaf453640730b5f78231e642aa65edcb1000bd0a
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