MEG and EEG data analysis with MNE-Python

TLDR

Magnetoencephalography and electroencephalography measure weak electromagnetic signals generated by neuronal activity, and using these signals to characterize and locate neural activation is challenging, requiring expertise in physics, signal processing, statistics, and numerical methods, with full documentation available at http://martinos.org/mne. MNE‑Python aims to address the challenge of analyzing M/EEG data by providing state‑of‑the‑art algorithms for preprocessing, source localization, statistical analysis, and functional connectivity estimation. It is implemented in Python with a consistent interface, integrates with core scientific libraries (NumPy, SciPy, matplotlib, Mayavi, Nibabel), and is released under a permissive BSD license. Since its recent development, MNE‑Python has rapidly expanded its capabilities and tutorials through collaborative code development, and provides easy access to preprocessed datasets, enabling quick starts and reproducible research.

Abstract

Magnetoencephalography and electroencephalography (M/EEG) measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation offunctional connectivity between distributed brain regions.All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysispipelines by writing Python scripts.Moreover, MNE-Python is tightly integrated with the core Python libraries for scientificcomptuation (Numpy, Scipy) and visualization (matplotlib and Mayavi), as wellas the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD licenseallowing code reuse, even in commercial products. Although MNE-Python has onlybeen under heavy development for a couple of years, it has rapidly evolved withexpanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices.MNE-Python also gives easy access to preprocessed datasets,helping users to get started quickly and facilitating reproducibility ofmethods by other researchers. Full documentation, including dozens ofexamples, is available at http://martinos.org/mne.