Feb 26, 2025 · Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more. Distributed, sparse, mixed-norm, beam­formers, dipole fitting, and more. Advanced decoding models including time general­iza­tion. Receptive field estima­tion with optional smooth­ness priors.

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MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

These tutorials provide narrative explanations, sample code, and expected output for the most common MNE-Python analysis tasks. The emphasis here is on thorough explanations that get you up to speed quickly, at the expense of covering only a limited number of topics.

Dec 18, 2024 · MNE-Python project for MEG and EEG data analysis. MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more.

MNE-CPP is a cross-platform, open-source framework which offers a variety of software tools to the neuroscientific research community. We provide applications for the acquisition and processing of MEG/EEG data, both in real-time and offline.

MNE-tools hosts a collection of software packages for analysis of (human) neuroimaging data, with emphasis on EEG, MEG, ECoG, iEEG, and fNIRS data. Limited support for MRI data is also provided, mostly for defining brain surfaces/volumes used to restrict inverse imaging of external (MEG) or scalp-based (EEG) data.