MNE software for processing MEG and EEG data

• Published in: NeuroImage (Orlando, Fla.) Vol. 86; pp. 446 - 460
• Main Authors: Gramfort, Alexandre, Luessi, Martin, Larson, Eric, Engemann, Denis A., Strohmeier, Daniel, Brodbeck, Christian, Parkkonen, Lauri, Hämäläinen, Matti S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.02.2014; Elsevier; Elsevier Limited
• Subjects: Algorithms; Applications; Biological and medical sciences; Brain - physiology; Brain Mapping - methods; Computer Science; Computer Simulation; Connectivity; Data analysis; Data processing; Electroencephalography (EEG); Electroencephalography - methods; Evoked Potentials - physiology; Fundamental and applied biological sciences. Psychology; Humans; Image Interpretation, Computer-Assisted - methods; Inverse problem; Magnetoencephalography (MEG); Magnetoencephalography - methods; Medical Imaging; Methods; Models, Neurological; Non-parametric statistics; Other Statistics; Software; Software Design; Software packages; Software Validation; Statistics; Time–frequency analysis; Vertebrates: nervous system and sense organs; Magnetoencephalography (MEG); Electroencephalography (EEG); Connectivity; Software; Time–frequency analysis; Non-parametric statistics; Inverse problem; Magnetoencephalography; Electrophysiology; Time―frequency analysis; Electroencephalography
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.10.027

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. [Display omitted] •The MNE software provides a complete pipeline for MEG and EEG data analysis.•MNE covers preprocessing, forward modeling, inverse methods, and visualization.•MNE supports advanced analysis: time-frequency, statistics, and connectivity.•MNE-Python enables fast and memory-efficient processing of large data sets.•MNE-Python is an open-source software supporting a collaborative development effort.