mne-python and braindecode
Braindecode builds deep learning models on top of MNE's EEG preprocessing and signal processing capabilities, making them complements rather than competitors—you typically use MNE to clean and prepare raw signals, then feed them into Braindecode for neural network-based decoding tasks.
About mne-python
mne-tools/mne-python
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Provides comprehensive analysis pipelines covering preprocessing, source estimation via inverse problems, time-frequency decomposition, and connectivity metrics across diverse neurophysiological formats (sEEG, ECoG). Built on NumPy/SciPy with lazy-loading for memory efficiency, integrating seamlessly with the scientific Python stack for machine learning and statistical workflows.
About braindecode
braindecode/braindecode
Deep learning software to decode EEG, ECG or MEG signals
Built on PyTorch, it provides pre-implemented convolutional neural network architectures specifically designed for brain signal analysis, along with dataset fetchers (MOABB integration), preprocessing pipelines, and data augmentation techniques. The toolbox bridges neuroscience and deep learning by handling raw signal decoding end-to-end, leveraging MNE-Python for signal processing and supporting ECoG in addition to EEG and MEG modalities.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work