MNE software for processing MEG and EEG data
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Lauri Parkkonen,Matti Hämäläinen +7 more
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TLDR
Detailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis.About:
This article is published in NeuroImage.The article was published on 2014-02-01 and is currently open access. It has received 1447 citations till now.read more
Citations
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Journal ArticleDOI
MEG and EEG data analysis with MNE-Python
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Roman Goj,Mainak Jas,Teon L Brooks,Lauri Parkkonen,Matti Hämäläinen +10 more
TL;DR: MNE-Python as discussed by the authors is an open-source software package that provides 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.
Journal ArticleDOI
Parameterizing neural power spectra into periodic and aperiodic components.
Thomas Donoghue,Matar Haller,Erik J. Peterson,Paroma Varma,Priyadarshini Sebastian,Richard Gao,Torben Noto,Antonio H. Lara,Joni D. Wallis,Joni D. Wallis,Robert T. Knight,Robert T. Knight,Avgusta Y. Shestyuk,Bradley Voytek +13 more
TL;DR: An algorithm to parameterize electrophysiological neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks is introduced, addressing limitations of common approaches.
Journal ArticleDOI
Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas
Georgios Michalareas,Julien Vezoli,Stan van Pelt,Stan van Pelt,Jan-Mathijs Schoffelen,Jan-Mathijs Schoffelen,Henry Kennedy,Henry Kennedy,Pascal Fries,Pascal Fries +9 more
TL;DR: Rhythmic inter-areal influences constrain a functional hierarchy of the seven homologous human visual areas that is in close agreement with the respective macaque anatomical hierarchy and allow an extension of the hierarchy to 26human visual areas including uniquely human brain areas.
Journal ArticleDOI
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Gaël Varoquaux,Pradeep Reddy Raamana,Denis A. Engemann,Andrés Hoyos-Idrobo,Yannick Schwartz,Bertrand Thirion +5 more
TL;DR: T theory and experiments outline that the popular “leave‐one‐out” strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred, and it can be favorable to use sane defaults, in particular for non‐sparse decoders.
MEG and EEG data analysis with MNE-Python
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Roman Goj,Mainak Jas,Teon L Brooks,Lauri Parkkonen,Matti Hämäläinen +10 more
TL;DR: 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.
References
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Journal ArticleDOI
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
Journal ArticleDOI
Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
TL;DR: In this article, the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation, and measures of causal lag and causal strength can then be constructed.