MOABB: Trustworthy algorithm benchmarking for BCIs

TL;DR

This paper introduces MOABB, an open-source software framework that standardizes benchmarking of BCI algorithms across multiple datasets to improve reproducibility and reliability in BCI research.

Contribution

The paper presents MOABB, a unified, open-source benchmarking system that addresses reproducibility issues and enables consistent evaluation of BCI algorithms across diverse datasets.

Findings

Different datasets yield varying results for the same algorithms.

Many validated methods do not generalize well across datasets.

Benchmarking reveals the need for trustworthy evaluation in BCI research.

Abstract

BCI algorithm development has long been hampered by two major issues: small sample sets and a lack of reproducibility. We offer a solution to both of these problems via a software suite that streamlines both the issues of finding and preprocessing data in a reliable manner, as well as that of using a consistent interface for machine learning methods. By building on recent advances in software for signal analysis implemented in the MNE toolkit, and the unified framework for machine learning offered by the scikit-learn project, we offer a system that can improve BCI algorithm development. This system is fully open-source under the BSD licence and available at https://github.com/NeuroTechX/moabb. To validate our efforts, we analyze a set of state-of-the-art decoding algorithms across 12 open access datasets, with over 250 subjects. Our analysis confirms that different datasets can result in very different results for identical processing pipelines, highlighting the need for trustworthy algorithm benchmarking in the field of BCIs, and further that many previously validated methods do not hold up when applied across different datasets, which has wide-reaching implications for practical BCIs.