MEG and EEG data analysis with MNE-Python

Frontiers in Neuroscience

Lauri eParkkonen¹  Matti eHämäläinen¹  Mainak eJas¹  Martin eLuessi²  Denis A Engemann³  Daniel eStrohmeier⁵  Alexandre eGramfort⁷  Teon eBrooks⁸  Christian eBrodbeck⁸  Roman eGoj⁹  Eric eLarson^1,10 

[1] Aalto University School of Science;Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School;Brain Imaging Lab, Department of Psychiatry, University Hospital;Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI;Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology;Institute of Neuroscience and Medicine - Cogntive Neuroscience (INM-3), Forschungszentrum;NeuroSpin, CEA Saclay;New York University;Psychological Imaging Laboratory, Psychology, School of Natural Sciences, University of Stirling;University of Washington, Institute for Learning and Brain Sciences;

关键词: Neuroimaging;    Software;    Electroencephalography (EEG);    Magnetoencephalography (MEG);    python;    open-source;   

DOI  :  10.3389/fnins.2013.00267

来源: DOAJ

【 摘 要 】

Magnetoencephalography and electroencephalography (M/EEG) measure the weak
electromagnetic signals generated by neuronal activity in the brain. Using these
signals to characterize and locate neural activation in the brain is a
challenge that requires expertise in physics, signal
processing, statistics, and numerical methods. As part of the MNE software
suite, MNE-Python is an open-source
software package that addresses this challenge by providing
state-of-the-art algorithms implemented in Python that cover multiple methods of data
preprocessing, source localization, statistical analysis, and estimation of
functional 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 analysis
pipelines by writing Python scripts.
Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific
comptutation (Numpy, Scipy) and visualization (matplotlib and Mayavi), as well
as the greater neuroimaging ecosystem in Python
via the Nibabel package. The code is provided under the new BSD license
allowing code reuse, even in commercial products. Although MNE-Python has only
been under heavy development for a couple of years, it has rapidly evolved with
expanded 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 of
methods by other researchers. Full documentation, including dozens of
examples, is available at http://martinos.org/mne.

【 授权许可】

Unknown