MEG and EEG data analysis with MNE-Python

• Main Authors: Alexandre eGramfort, Martin eLuessi, Eric eLarson, Denis A Engemann, Daniel eStrohmeier, Christian eBrodbeck, Roman eGoj, Mainak eJas, Teon eBrooks, Lauri eParkkonen, Matti eHämäläinen
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-12-01
• Series: Frontiers in Neuroscience
• Subjects: Neuroimaging; Software; Electroencephalography (EEG); Magnetoencephalography (MEG); python; open-source
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00267/full

Magnetoencephalography and electroencephalography (M/EEG) measure the weak<br/>electromagnetic signals generated by neuronal activity in the brain. Using these<br/>signals to characterize and locate neural activation in the brain is a<br/>challenge that requires expertise in physics, signal<br/>processing, statistics, and numerical methods. As part of the MNE software<br/>suite, MNE-Python is an open-source<br/>software package that addresses this challenge by providing<br/>state-of-the-art algorithms implemented in Python that cover multiple methods of data <br/>preprocessing, source localization, statistical analysis, and estimation of<br/>functional connectivity between distributed brain regions.<br/>All algorithms and utility functions are implemented in a consistent manner <br/>with well-documented interfaces, enabling users to create M/EEG data analysis<br/>pipelines by writing Python scripts.<br/>Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific<br/>comptutation (Numpy, Scipy) and visualization (matplotlib and Mayavi), as well<br/>as the greater neuroimaging ecosystem in Python <br/>via the Nibabel package. The code is provided under the new BSD license<br/>allowing code reuse, even in commercial products. Although MNE-Python has only<br/>been under heavy development for a couple of years, it has rapidly evolved with<br/>expanded analysis capabilities and pedagogical tutorials because multiple <br/>labs have collaborated during code development to help share best practices.<br/>MNE-Python also gives easy access to preprocessed datasets,<br/>helping users to get started quickly and facilitating reproducibility of<br/>methods by other researchers. Full documentation, including dozens of<br/>examples, is available at http://martinos.org/mne.