.. currentmodule:: moabb

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.

Thank you for visiting the Mother of all BCI Benchmark documentation and associated GitHub repository

This document is a hub to give you some information about the project. Jump straight to one of the sections below, or just scroll down to find out more.

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:

• 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• While many BCI datasets are made freely available, researchers do not publish code, and reproducing results required to benchmark new algorithms turns out to be trickier than it should be.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 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% ...”.

First, you could take a look at our tutorials that cover the most important concepts and use cases. Also, we have a gallery of examples available.

Currently pip install moabb fails when pip version < 21, e.g. with 20.0.2 due to an idna package conflict. Newer pip versions resolve this conflict automatically. To fix this you can upgrade your pip version using: pip install -U pip before installing moabb.

This project is under the umbrella of NeuroTechX, the international community for NeuroTech enthusiasts.

The project is currently maintained by:

Sylvain Chevallier	Bruno Aristimunha	Igor Carrara

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 moment, both works as Research Scientist

Alexander Barachant	Vinay Jayaram

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

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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.

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 Gitter channel 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.

Thank you so much (Danke schön! Merci beaucoup!) for visiting the project and we do hope that you'll join us on this amazing journey to build a comprehensive benchmark of popular BCI algorithms applied on an extensive list of freely available EEG datasets.