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https://github.com/neurotechx/moabb

Mother of All BCI Benchmarks
https://github.com/neurotechx/moabb

bci bci-benchmarks brain-computer-interface eeg machine-learning neuroscience

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Mother of All BCI Benchmarks

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README 

        


  Mother of all BCI Benchmarks   

  Build a comprehensive benchmark of popular Brain-Computer Interface (BCI) algorithms applied on an extensive list of freely available EEG datasets.




## Disclaimer



**This is an open science project that may evolve depending on the need of the

community.**



[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10034224.svg)](https://doi.org/10.5281/zenodo.10034224)

[![Build Status](https://github.com/NeuroTechX/moabb/workflows/Test/badge.svg)](https://github.com/NeuroTechX/moabb/actions?query=branch%3Amaster)

[![PyPI](https://img.shields.io/pypi/v/moabb?color=blue&style=plastic)](https://img.shields.io/pypi/v/moabb)

[![Downloads](https://pepy.tech/badge/moabb)](https://pepy.tech/project/moabb)



### The problem



[Brain-Computer Interfaces](https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface)

allow to interact with a computer using brain signals. In this project, we focus mostly on

electroencephalographic signals

([EEG](https://en.wikipedia.org/wiki/Electroencephalography)), that is a very active

research domain, with worldwide scientific contributions. Still:



- Reproducible Research in BCI has a long way to go.

- While many BCI datasets are made freely available, researchers do not publish code, and

  reproducing results required to benchmark new algorithms turns out to be trickier than

  it should be.

- Performances can be significantly impacted by parameters of the preprocessing steps,

  toolboxes used and implementation “tricks” that are almost never reported in the

  literature.



As a result, there is no comprehensive benchmark of BCI algorithms, and newcomers are

spending a tremendous amount of time browsing literature to find out what algorithm works

best and on which dataset.



### The solution



The Mother of all BCI Benchmarks allows to:



- Build a comprehensive benchmark of popular BCI algorithms applied on an extensive list

  of freely available EEG datasets.

- The code is available on GitHub, serving as a reference point for the future algorithmic

  developments.

- Algorithms can be ranked and promoted on a website, providing a clear picture of the

  different solutions available in the field.



This project will be successful when we read in an abstract “ … the proposed method

obtained a score of 89% on the MOABB (Mother of All BCI Benchmarks), outperforming the

state of the art by 5% ...”.



## Core Team



This project is under the umbrella of [NeuroTechX][link_neurotechx], the international

community for NeuroTech enthusiasts.



The project is currently maintained by:



  

    

      Sylvain Chevallier

      Bruno Aristimunha

      Igor Carrara

      Pierre Guetschel

    

  

  

    

      Sylvain Chevallier

      Bruno Aristimunha

      Igor Carrara

      Pierre Guetschel



  



The Mother of all BCI Benchmarks was founded by Alexander Barachant and Vinay Jayaram, who

are experts in the field of Brain-Computer Interfaces (BCI). At the moment, both work as

Research Scientists at Meta.



  

    

      Alexander Barachant

      Vinay Jayaram

    

  

  

    

Alexander Barachant

Vinay Jayaram

  



## Contributors



The MOABB is a community project, and we are always thankful to all the contributors!






const endpoint = 'https://api.github.com/repos/NeuroTechX/moabb/contributors';

const container = document.getElementById('contributors-container');

const filterList = ["bruAristimunha", "sylvchev", "carraraig", "pierreGtch", "sara04", "pre-commit-ci[bot]", "dependabot[bot]", "alexandrebarachant", "vinay-jayaram"];

fetch(endpoint)

  .then(response => response.json())

  .then(contributors => {

    const filteredContributors = contributors.filter(contributor => !filterList.includes(contributor.login));    filteredContributors.forEach(contributor => {

      const link = document.createElement('a');

      link.href = contributor.html_url;

      link.target = '_blank';

      const img = document.createElement('img');

      img.src = contributor.avatar_url;

      img.alt = contributor.login;

      img.style.width = '100px';

      img.style.height = '100px';

      img.style.objectFit = 'cover';

      img.style.borderRadius = '50%';

      link.appendChild(img);

      container.appendChild(link);

    });

  });






Special acknowledge for the extra MOABB contributors:



  

    

      Pedro Rodrigues

    

  

  

    

 Pedro L. C. Rodrigues

  



### What do we need?



**You**! In whatever way you can help.



We need expertise in programming, user experience, software sustainability, documentation

and technical writing and project management.



We'd love your feedback along the way.



Our primary goal is to build a comprehensive benchmark of popular BCI algorithms applied

on an extensive list of freely available EEG datasets, and we're excited to support the

professional development of any and all of our contributors. If you're looking to learn to

code, try out working collaboratively, or translate your skills to the digital domain,

we're here to help.



## Citing MOABB and related publications



If you use MOABB in your experiments, please cite this library when

publishing a paper to increase the visibility of open science initiatives:



* Here is the APA version:

```

Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Barthelemy, Q., Schirrmeister, R. T., Kobler, R., Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., Roy, Y., Jayaram, V., Barachant, A., & Chevallier, S. (2025).

Mother of all BCI Benchmarks (MOABB), 2025. DOI: 10.5281/zenodo.10034223.

```



and the Bibtex version:



```bibtex



    @software{Aristimunha_Mother_of_all,

      author       = {Aristimunha, Bruno and

                      Carrara, Igor and

                      Guetschel, Pierre and

                      Sedlar, Sara and

                      Rodrigues, Pedro and

                      Sosulski, Jan and

                      Narayanan, Divyesh and

                      Bjareholt, Erik and

                      Barthelemy, Quentin and

                      Schirrmeister, Robin Tibor and

                      Kobler, Reinmar and

                      Kalunga, Emmanuel and

                      Darmet, Ludovic and

                      Gregoire, Cattan and

                      Abdul Hussain, Ali and

                      Gatti, Ramiro and

                      Goncharenko, Vladislav and

                      Thielen, Jordy and

                      Moreau, Thomas and

                      Roy, Yannick and

                      Jayaram, Vinay and

                      Barachant, Alexandre and

                      Chevallier, Sylvain},

            title        = {Mother of all BCI Benchmarks},

            year         = 2025,

            publisher    = {Zenodo},

            version      = {v1.2.0},

            url = {https://github.com/NeuroTechX/moabb},

            doi = {10.5281/zenodo.10034223},

    }

```



If you want to cite the scientific contributions of MOABB, you could use the following paper:



> Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Junqueira Lopes, Sébastien Velut, Salim Khazem, Thomas Moreau

> ["The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark"](https://cnrs.hal.science/hal-04537061/)

> HAL: hal-04537061.



> Vinay Jayaram and Alexandre Barachant.

> ["MOABB: trustworthy algorithm benchmarking for BCIs."](http://iopscience.iop.org/article/10.1088/1741-2552/aadea0/meta)

> Journal of neural engineering 15.6 (2018): 066011.

> [DOI](https://doi.org/10.1088/1741-2552/aadea0)



If you publish a paper using MOABB, please contact us on [gitter][link_gitter] or open an

issue! We would love to hear about your work and help you promote it.



## Contact us



If you want to report a problem or suggest an enhancement, we'd love for you to

[open an issue](https://github.com/NeuroTechX/moabb/issues) at this GitHub repository

because then we can get right on it.



For a less formal discussion or exchanging ideas, you can also reach us on the Github or join our weekly office hours! This an open video meeting

happening on a [regular basis](https://github.com/NeuroTechX/moabb/issues/191), please ask

the link on the gitter channel. We are also on NeuroTechX Slack channel

[#moabb][link_neurotechx_signup].



[link_alex_b]: http://alexandre.barachant.org/

[link_vinay]: https://www.linkedin.com/in/vinay-jayaram-8635aa25

[link_neurotechx]: http://neurotechx.com/

[link_sylvain]: https://sylvchev.github.io/

[link_bruno]: https://www.linkedin.com/in/bruaristimunha/

[link_igor]: https://www.linkedin.com/in/carraraig/

[link_pierre]: https://www.linkedin.com/in/pierreguetschel/

[link_neurotechx_signup]: https://neurotechx.com/

[link_gitter]: https://app.gitter.im/#/room/#moabb_dev_community:gitter.im

[link_moabb_docs]: https://neurotechx.github.io/moabb/

[link_arxiv]: https://arxiv.org/abs/1805.06427

[link_jne]: http://iopscience.iop.org/article/10.1088/1741-2552/aadea0/meta