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.
This is an open science project that may evolve depending on the need of the community.
Brain-Computer Interfaces allow to interact with a computer using brain signals. In this project, we focus mostly on electroencephalographic signals (EEG), that is a very active research domain, with worldwide scientific contributions. Still:
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 Mother of all BCI Benchmarks allows to:
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% ...”.
This project is under the umbrella of NeuroTechX, the international community for NeuroTech enthusiasts.
The project is currently maintained by:
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.
The MOABB is a community project, and we are always thankful to all the contributors!
<script> 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); }); }); </script>Special acknowledge for the extra MOABB contributors:
Pedro RodriguesYou! 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 publicationsIf you use MOABB in your experiments, please cite this library when publishing a paper to increase the visibility of open science initiatives:
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:
@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" HAL: hal-04537061.
Vinay Jayaram and Alexandre Barachant. "MOABB: trustworthy algorithm benchmarking for BCIs." Journal of neural engineering 15.6 (2018): 066011. DOI
If you publish a paper using MOABB, please contact us on gitter or open an issue! We would love to hear about your work and help you promote it.
If you want to report a problem or suggest an enhancement, we'd love for you to open an issue 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, please ask the link on the gitter channel. We are also on NeuroTechX Slack channel #moabb.
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