Mother of all BCI Benchmarks (MOABB)

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

Docs • Install • Examples • Benchmark • Datasets

## Quickstart ```shell pip install moabb ``` ```python import moabb from moabb.datasets import BNCI2014_001 from moabb.evaluations import CrossSessionEvaluation from moabb.paradigms import LeftRightImagery from moabb.pipelines.features import LogVariance from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA from sklearn.pipeline import make_pipeline moabb.set_log_level("info") pipelines = {"LogVar+LDA": make_pipeline(LogVariance(), LDA())} dataset = BNCI2014_001() dataset.subject_list = dataset.subject_list[:2] paradigm = LeftRightImagery(fmin=8, fmax=35) evaluation = CrossSessionEvaluation(paradigm=paradigm, datasets=[dataset]) results = evaluation.process(pipelines) print(results.head()) ``` For full installation options and troubleshooting, see the [documentation](https://moabb.neurotechx.com/docs/install/install.html). ## Disclaimer **This is an open science project that may evolve depending on the need of the community.** ## 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 - While many BCI datasets are made freely available, researchers do not publish code, and reproducing results required to benchmark new algorithms turns out to be trickier than it should be.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 Mother of all BCI Benchmarks was founded by [Alexander Barachant](http://alexandre.barachant.org/) and [Vinay Jayaram][link_vinay]. It is currently maintained by: * [Sylvain Chevallier](https://sylvchev.github.io/) * [Bruno Aristimunha](https://bruaristimunha.github.io/) * [Pierre Guetschel](https://github.com/PierreGtch) * [Grégoire Cattan](https://github.com/gcattan) * [Anton Andreev](https://github.com/toncho11) ## Contributors The MOABB is a community project, and we are always thankful to all the contributors!

## Acknowledgements MOABB has benefited from the support of the following organizations:

### 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. ## Cite MOABB If you use MOABB in your experiments, please cite MOABB and the related publications: 📚 [Full citation guide](https://moabb.neurotechx.com/docs/cite.html) ### Software Citation #### APA Format ```text 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., Andreev, A., Thielen, J., Hajhassani, D., Begany, K., Moreau, T., Roy, Y., Jayaram, V., Barachant, A., & Chevallier, S. (2026). Mother of all BCI Benchmarks (MOABB) (Version 1.5.0). Zenodo. https://doi.org/10.5281/zenodo.10034223 ``` #### BibTeX Format ```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 Andreev, Anton and Thielen, Jordy and Hajhassani, Davoud and Begany, Katelyn and Moreau, Thomas and Roy, Yannick and Jayaram, Vinay and Barachant, Alexandre and Chevallier, Sylvain}, title = {Mother of all BCI Benchmarks}, year = 2026, publisher = {Zenodo}, version = {1.5.0}, url = {https://github.com/NeuroTechX/moabb}, doi = {10.5281/zenodo.10034223}, } ``` ### Scientific Publications If you want to cite the scientific contributions of MOABB, please use the following papers: #### MOABB Benchmark 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 #### Original MOABB Paper > Vinay Jayaram and Alexandre Barachant > > **["MOABB: trustworthy algorithm benchmarking for BCIs"](https://doi.org/10.1088/1741-2552/aadea0)** > > Journal of Neural Engineering 15.6 (2018): 066011 > > [DOI: 10.1088/1741-2552/aadea0](https://doi.org/10.1088/1741-2552/aadea0) --- 📣 **If you publish a paper using MOABB, please [open an issue](https://github.com/NeuroTechX/moabb/issues) to let us know!** 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. [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://moabb.neurotechx.com/ [link_arxiv]: https://arxiv.org/abs/1805.06427 [link_jne]: https://doi.org/10.1088/1741-2552/aadea0