paper.bib

@article{Gramfort2014,
abstract = {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. {\textcopyright} 2013 Elsevier Inc.},
author = {Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis A. and Strohmeier, Daniel and Brodbeck, Christian and Parkkonen, Lauri and H{\"{a}}m{\"{a}}l{\"{a}}inen, Matti S.},
doi = {10.1016/j.neuroimage.2013.10.027},
issn = {1095-9572},
journal = {NeuroImage},
month = {feb},
pages = {446--60},
pmid = {24161808},
publisher = {Academic Press},
title = {{MNE software for processing MEG and EEG data.}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811913010501?via{\%}3Dihub http://www.ncbi.nlm.nih.gov/pubmed/24161808{\%}0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3930851},
volume = {86},
year = {2014}
}

@article{Gramfort2013,
abstract = {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. {\textcopyright} 2013 Gramfort, Luessi, Larson, Engemann, Strohmeier, Brodbeck, Goj, Jas, Brooks, Parkkonen and H{\"{a}}m{\"{a}}l{\"{a}}inen.},
author = {Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis A and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and H{\"{a}}m{\"{a}}l{\"{a}}inen, Matti},
doi = {10.3389/fnins.2013.00267},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Electroencephalography (EEG),Magnetoencephalography (MEG),Neuroimaging,Open-source,Python,Software},
month = {dec},
number = {7 DEC},
pages = {267},
publisher = {Frontiers},
title = {{MEG and EEG data analysis with MNE-Python}},
url = {http://journal.frontiersin.org/article/10.3389/fnins.2013.00267/abstract},
volume = {7},
year = {2013}
}