mlsanityData to 14 Oct 2026

MOABB: Trustworthy algorithm benchmarking for BCIs

arXiv 1805.06427 ↗First posted 16 May 2018cs.HCcited by 82

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Abstract (arXiv)

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.

Key facts. Cited by 82 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2018.

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Citing papers per year: 2021: 3; 2022: 10; 2023: 16; 2024: 15; 2025: 10; 2026: 28.

82 papers.

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  1. 2022Decoding speech perception from non-invasive brain recordingscited by 130
  2. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 80
  3. 2023Brain decoding: toward real-time reconstruction of visual perceptioncited by 66
  4. 2023DreamDiffusion: Generating High-Quality Images from Brain EEG Signalscited by 56
  5. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  6. 2022Data augmentation for learning predictive models on EEG: a systematic comparisoncited by 33
  7. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 30
  8. 2023BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstructioncited by 30
  9. 2024The largest EEG-based BCI reproducibility study for open science: the MOABB benchmarkcited by 29
  10. 2026NeuralBench: A Unifying Framework to Benchmark NeuroAI Modelscited by 16
  11. 2023Brain-Conditional Multimodal Synthesis: A Survey and Taxonomycited by 15
  12. 2024T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIscited by 15
38 more
  1. 2024SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEGcited by 12
  2. 2022Deep Riemannian Networks for End-to-End EEG Decodingcited by 11
  3. 2023Riemannian Multinomial Logistics Regression for SPD Neural Networkscited by 11
  4. 2023EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanismscited by 11
  5. 2023Interpretable and Robust AI in EEG Systems: A Surveycited by 10
  6. 2023Evaluating the structure of cognitive tasks with transfer learningcited by 8
  7. 2024A Lie Group Approach to Riemannian Batch Normalizationcited by 8
  8. 2024RMLR: Extending Multinomial Logistic Regression into General Geometriescited by 8
  9. 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
  10. 2025Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classificationcited by 7
  11. 2026NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfacescited by 7
  12. 2021Functional connectivity ensemble method to enhance BCI performance (FUCONE)cited by 6
  13. 2021Minimizing subject-dependent calibration for BCI with Riemannian transfer learningcited by 6
  14. 2024Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decodingcited by 6
  15. 2022Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Trainingcited by 5
  16. 2023Personalised Federated Learning On Heterogeneous Feature Spacescited by 5
  17. 2023Classification of BCI-EEG based on augmented covariance matrixcited by 5
  18. 2023Pseudo-online framework for BCI evaluation: A MOABB perspectivecited by 5
  19. 2023A Strong and Simple Deep Learning Baseline for BCI MI Decodingcited by 5
  20. 2023BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervisioncited by 5
  21. 2024Geometric Neural Network based on Phase Space for BCI-EEG decodingcited by 5
  22. 2024CSP-Net: Common Spatial Pattern Empowered Neural Networks for EEG-Based Motor Imagery Classificationcited by 5
  23. 2025MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classificationcited by 5
  24. 2022Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer Interfacescited by 4
  25. 2023Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasetscited by 4
  26. 2025SAMBA: Toward a Long-Context EEG Foundation Model via Spatial Embedding and Differential Mambacited by 4
  27. 2022Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalitiescited by 3
  28. 2024Uncertainty Quantification for cross-subject Motor Imagery classificationcited by 3
  29. 2024A Survey of Spatio-Temporal EEG data Analysis: from Models to Applicationscited by 3
  30. 2021Riemannian classification of EEG signals with missing valuescited by 2
  31. 2022End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilitiescited by 2
  32. 2024Alljoined1 -- A dataset for EEG-to-Image decodingcited by 2
  33. 2024Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfacescited by 2
  34. 2026Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliabilitycited by 2
  35. 2023An embedding for EEG signals learned using a triplet losscited by 1
  36. 2024Playing With Neuroscience: Past, Present and Future of Neuroimaging and Gamescited by 1
  37. 2024Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learningcited by 1
  38. 2025Exploring Deep Learning Models for EEG Neural Decodingcited by 1

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