# braindecode/braindecode

Deep learning software to decode EEG, ECG or MEG signals

Repository: https://github.com/braindecode/braindecode
Canonical: https://ross.abutalabs.com/products/braindecode
Homepage: https://braindecode.org/
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: deep-learning, eeg, electroencephalogram, electroencephalography, neuroimaging, neuroscience, python, pytorch, meg, magnetoencephalography, electrocorticography, ecog
Last push: 2026-08-26T13:24:43+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 95, longevity 100
- inputs: {"age_days": 2430, "days_push": 7, "days_rel": 32, "gap_med": 30, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1293, forks 273 (observed 2026-08-28T04:04:16.252704+00:00)

## What it is
Braindecode is an open-source Python toolbox built on PyTorch for decoding raw electrophysiological brain signals such as EEG, ECoG, and MEG with deep learning models. It bundles dataset fetchers, preprocessing and visualization utilities, reference neural network architectures, and data augmentation techniques for neurophysiological analysis.

## Use cases
- decode EEG signals with deep learning
- classify motor imagery EEG recordings
- build a brain-computer interface decoder in Python
- apply deep learning to MEG or ECoG data
- preprocess and augment electrophysiology datasets for neural networks
- train PyTorch models on raw brain signal time series
- benchmark EEG decoding models on MOABB datasets

## When to choose
- You need ready-made, vetted deep learning architectures (e.g., convolutional models for EEG) instead of implementing them from scratch
- You are a neuroscientist who wants to apply PyTorch models to EEG/MEG/ECoG data with integrated preprocessing and visualization
- You need reproducible pipelines with dataset fetchers (e.g., MOABB integration) for BCI and neurophysiology research

## When to avoid
- You only need classical EEG analysis (filtering, epoching, ERP statistics) without deep learning - MNE-Python alone may suffice
- You need real-time closed-loop or online BCI systems rather than offline model training and analysis
- You work in a non-PyTorch ecosystem such as TensorFlow or JAX, since braindecode is PyTorch-only

## Facets
- artifact type: library
- maturity: active
- function: deep-learning, machine-learning, data-visualization
- domain: deep-learning, machine-learning, healthcare, data-science
- platform: python, cross-platform, windows
- tags: eeg, meg, ecg, ecog, electrophysiology, neuroscience, neuroimaging, pytorch, brain-computer-interface, signal-processing, mne, moabb, dataset-fetchers, data-augmentation, gpu, linux, macos

## Member repositories
- braindecode/braindecode (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:16.252704+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 2026-08-30T04:54:41.452185+00:00, confidence not recorded.
  - readme: https://github.com/braindecode/braindecode (fetched 2026-08-28T04:04:16.252704+00:00, sha e0055f77b449)
  - homepage: https://braindecode.org/ (fetched 2026-08-29T12:11:03.007816+00:00, sha 60c3ee0c41bf)
  - registry_pypi: https://pypi.org/pypi/braindecode/json (fetched 2026-08-29T12:11:03.017458+00:00, sha 7ce6bd34b041)
- Data as of 2026-08-30T08:39:29.467469+00:00.