# NeuroTechX/moabb

Mother of All BCI Benchmarks

Repository: https://github.com/NeuroTechX/moabb
Canonical: https://ross.abutalabs.com/products/moabb
Homepage: https://moabb.neurotechx.com/docs/index.html
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: brain-computer-interface, machine-learning, eeg, neuroscience, bci, bci-benchmarks
Last push: 2026-08-31T22:32:49+00:00

## Health v2 (maintenance only)
Score: 96/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 88, longevity 100
- inputs: {"age_days": 3397, "days_push": 2, "days_rel": 2, "gap_med": 66.0, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1048, forks 261 (observed 2026-09-01T02:13:55.874204+00:00)

## What it is
MOABB (Mother of All BCI Benchmarks) is a Python library for reproducible benchmarking of machine-learning algorithms on EEG-based brain-computer interface tasks. It provides unified access to 160 open EEG datasets (3500+ subjects), standardized paradigms (motor imagery, P300, SSVEP, c-VEP), and evaluation schemes built on MNE-Python and scikit-learn.

## Use cases
- benchmark my EEG decoding pipeline across multiple public datasets
- download and load BCI EEG datasets in a unified format
- run cross-session or cross-subject evaluation for motor imagery classification
- compare P300 or SSVEP classification algorithms fairly
- reproduce published BCI benchmark results
- add a custom EEG dataset in BIDS format for benchmarking
- visualize and statistically analyze BCI classification results

## When to choose
- you need reproducible, standardized comparisons of BCI/EEG decoding algorithms
- you want unified access to many open EEG datasets without writing per-dataset loaders
- you work with scikit-learn or MNE-Python pipelines and want fair evaluation schemes
- you are doing open-science BCI research and need citable benchmark results

## When to avoid
- you need real-time BCI signal acquisition or online closed-loop operation
- you work with non-EEG modalities like fMRI or MEG exclusively
- you want a GUI-based EEG analysis tool rather than a Python library
- you need clinical-grade or regulatory-compliant EEG processing

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, benchmarking, data-science, data-visualization
- domain: machine-learning, data-science, healthcare
- platform: python, cross-platform
- tags: eeg, brain-computer-interface, bci, neuroscience, benchmark, motor-imagery, p300, ssvep, mne-python, open-science, signal-processing, research

## Member repositories
- NeuroTechX/moabb (main) score 96

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:13:55.874204+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:02:17.092445+00:00, confidence not recorded.
  - readme: https://github.com/NeuroTechX/moabb (fetched 2026-09-01T02:13:55.874204+00:00, sha 514f06696ff4)
  - homepage: https://moabb.neurotechx.com/docs/index.html (fetched 2026-08-29T13:03:49.792139+00:00, sha 174d8a405355)
  - site_page: https://moabb.neurotechx.com/docs/whats_new.html (fetched 2026-08-29T13:03:49.815214+00:00, sha 72c750a22554)
  - site_page: https://moabb.neurotechx.com/docs/dataset_summary.html (fetched 2026-08-29T13:03:49.805939+00:00, sha 434c859ce372)
  - site_page: https://moabb.neurotechx.com/docs/auto_examples/advanced_examples/plot_pre_processing_steps.html (fetched 2026-08-29T13:03:49.819801+00:00, sha 54efa6f9481d)
  - site_page: https://moabb.neurotechx.com/docs/install/install.html (fetched 2026-08-29T13:03:49.809466+00:00, sha 3a3ccd7f5e9a)
  - site_page: https://moabb.neurotechx.com/docs/auto_examples/index.html (fetched 2026-08-29T13:03:49.811060+00:00, sha f2ef7b7a9333)
  - site_page: https://moabb.neurotechx.com/docs/api.html (fetched 2026-08-29T13:03:49.812846+00:00, sha 8b348bc696aa)
  - site_page: https://moabb.neurotechx.com/docs/cite.html (fetched 2026-08-29T13:03:49.801575+00:00, sha 922244d3a2d6)
  - site_page: https://moabb.neurotechx.com/docs/paper_results.html (fetched 2026-08-29T13:03:49.803785+00:00, sha 7feb768765be)
- Data as of 2026-08-30T08:39:29.467469+00:00.