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
This package helps neuroscientists explore, visualize, and analyze brain activity data recorded from human participants. It takes raw neurophysiological data from techniques like MEG, EEG, sEEG, or ECoG as input. The output includes processed signals, statistical analyses, source estimations of brain activity, and interactive visualizations, allowing researchers to understand brain function. It's designed for researchers, academics, and clinicians working with human brain imaging.
About braindecode
braindecode/braindecode
Deep learning software to decode EEG, ECG or MEG signals
This tool helps neuroscientists and deep learning researchers analyze raw brain activity signals like EEG, ECoG, or MEG using deep learning models. It takes raw electrophysiological data as input and provides processed, visualized data and insights from various deep learning architectures. It's designed for those who want to apply advanced computational methods to understand brain function or build brain-computer interfaces.
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