braindecode/braindecode

Health v2 · maintenance only

Adoption not part of the score

1293 stars · 273 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

deep-learning machine-learning data-visualization deep-learning machine-learning healthcare data-science python cross-platform windows eeg meg ecg ecog electrophysiology neuroscience neuroimaging pytorch brain-computer-interface signal-processing mne moabb dataset-fetchers data-augmentation gpu linux macos

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