MNE software for processing MEG and EEG data

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.

[1]  A. Iacobucci Spectral Analysis for Economic Time Series , 2005 .

[2]  J. M. Moran,et al.  Local and long-range functional connectivity is reduced in concert in autism spectrum disorders , 2013, Proceedings of the National Academy of Sciences.

[3]  Olivier D. Faugeras,et al.  A common formalism for the Integral formulations of the forward EEG problem , 2005, IEEE Transactions on Medical Imaging.

[4]  O Bertrand,et al.  Combined EEG and MEG recordings of visual 40 Hz responses to illusory triangles in human , 1997, Neuroreport.

[5]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[6]  Harald Köstler,et al.  Numerical Mathematics of the Subtraction Method for the Modeling of a Current Dipole in EEG Source Reconstruction Using Finite Element Head Models , 2007, SIAM J. Sci. Comput..

[7]  Darren Price,et al.  Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.

[8]  David P. Wipf,et al.  Beamforming using the relevance vector machine , 2007, ICML '07.

[9]  A. Seth,et al.  Behaviour of Granger causality under filtering: Theoretical invariance and practical application , 2011, Journal of Neuroscience Methods.

[10]  Jens Haueisen,et al.  Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations , 2013, NeuroImage.

[11]  Anders M. Dale,et al.  Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.

[12]  O. Bertrand,et al.  Oscillatory gamma activity in humans and its role in object representation , 1999, Trends in Cognitive Sciences.

[13]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[14]  J. Tukey,et al.  An Algorithm for the Machine Calculation of , 2016 .

[15]  J.C. Mosher,et al.  Recursive MUSIC: A framework for EEG and MEG source localization , 1998, IEEE Transactions on Biomedical Engineering.

[16]  R. Hari On Brain's Magnetic Responses to Sensory Stimuli , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[17]  S. Taulu,et al.  Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements , 2006, Physics in medicine and biology.

[18]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[19]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[20]  Richard A. Becker,et al.  A Tour of Trellis Graphics , 1996 .

[21]  Seppo P. Ahlfors,et al.  Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates , 2006, NeuroImage.

[22]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[23]  J. Morlet,et al.  Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media , 1982 .

[24]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[25]  M. Fuchs,et al.  Linear and nonlinear current density reconstructions. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[26]  M. Hämäläinen,et al.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data , 1989, IEEE Transactions on Biomedical Engineering.

[27]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[28]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[29]  P. Fries Neuronal gamma-band synchronization as a fundamental process in cortical computation. , 2009, Annual review of neuroscience.

[30]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[31]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[32]  A K Liu,et al.  Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Karl J. Friston,et al.  The problem of low variance voxels in statistical parametric mapping; a new hat avoids a ‘haircut’ , 2012, NeuroImage.

[34]  J. Schoffelen,et al.  Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.

[35]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[36]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[37]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[38]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[39]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[40]  M. Scherg,et al.  Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. , 1985, Electroencephalography and clinical neurophysiology.

[41]  R. Ilmoniemi,et al.  Signal-space projection method for separating MEG or EEG into components , 1997, Medical and Biological Engineering and Computing.

[42]  Joachim Gross,et al.  Good practice for conducting and reporting MEG research , 2013, NeuroImage.

[43]  K. Matsuura,et al.  Selective minimum-norm solution of the biomagnetic inverse problem , 1995, IEEE Transactions on Biomedical Engineering.

[44]  A. Pérez-Villalba Rhythms of the Brain, G. Buzsáki. Oxford University Press, Madison Avenue, New York (2006), Price: GB £42.00, p. 448, ISBN: 0-19-530106-4 , 2008 .

[45]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[46]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[47]  Stefan Haufe,et al.  A critical assessment of connectivity measures for EEG data: A simulation study , 2013, NeuroImage.

[48]  Alex Martin,et al.  A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG , 2011, NeuroImage.

[49]  E. Somersalo,et al.  Visualization of Magnetoencephalographic Data Using Minimum Current Estimates , 1999, NeuroImage.

[50]  A. Scott,et al.  MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data , 2011, Neuroinformatics.

[51]  Joachim Gross,et al.  The effect of filtering on Granger causality based multivariate causality measures , 2010, NeuroImage.

[52]  Satrajit S. Ghosh,et al.  Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python , 2011, Front. Neuroinform..

[53]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[54]  A. Gramfort,et al.  Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods , 2012, Physics in medicine and biology.

[55]  Anders M. Dale,et al.  Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain , 2004, NeuroImage.

[56]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[57]  L. Kaufman,et al.  Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation , 1992, IEEE Transactions on Biomedical Engineering.

[58]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[59]  J. Sarvas,et al.  Bioelectromagnetic forward problem: isolated source approach revis(it)ed , 2012, Physics in medicine and biology.

[60]  Paul F. Dubois,et al.  Maintaining correctness in scientific programs , 2005, Comput. Sci. Eng..

[61]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[62]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[63]  Martin Vinck,et al.  The pairwise phase consistency: A bias-free measure of rhythmic neuronal synchronization , 2010, NeuroImage.

[64]  M. Corbetta,et al.  Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.

[65]  Riitta Hari,et al.  Comparison of Minimum Current Estimate and Dipole Modeling in the Analysis of Simulated Activity in the Human Visual Cortices , 2002, NeuroImage.

[66]  Eric Larson,et al.  The cortical dynamics underlying effective switching of auditory spatial attention , 2013, NeuroImage.

[67]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[68]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[69]  Richard M. Leahy,et al.  A comparison of random field theory and permutation methods for the statistical analysis of MEG data , 2005, NeuroImage.

[70]  A. Dale,et al.  Distributed current estimates using cortical orientation constraints , 2006, Human brain mapping.

[71]  R. Leahy,et al.  Equivalence of linear approaches in bioelectromagnetic inverse solutions , 2004, IEEE Workshop on Statistical Signal Processing, 2003.

[72]  Polina Golland,et al.  A Distributed Spatio-temporal EEG/MEG Inverse Solver , 2008, MICCAI.

[73]  Pekka Abrahamsson,et al.  Long-Term Effects of Test-Driven Development A Case Study , 2009, XP.

[74]  Andreas Prlic,et al.  Ten Simple Rules for the Open Development of Scientific Software , 2012, PLoS Comput. Biol..

[75]  Jens Timmer,et al.  Handbook of Time Series Analysis , 2006 .

[76]  Gareth R. Barnes,et al.  The use of anatomical constraints with MEG beamformers , 2003, NeuroImage.

[77]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[78]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[79]  Lauri Parkkonen,et al.  Dynamical MEG source modeling with multi‐target Bayesian filtering , 2009, Human brain mapping.

[80]  Kensuke Sekihara,et al.  MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG , 2011, Comput. Intell. Neurosci..

[81]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[82]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[83]  J Gross,et al.  REPRINTS , 1962, The Lancet.

[84]  R. Hari,et al.  Human cortical oscillations: a neuromagnetic view through the skull , 1997, Trends in Neurosciences.

[85]  Brian E. Granger,et al.  IPython: A System for Interactive Scientific Computing , 2007, Computing in Science & Engineering.

[86]  K. Uutela,et al.  Detecting and Correcting for Head Movements in Neuromagnetic Measurements , 2001, NeuroImage.

[87]  Karl J. Friston,et al.  EEG and MEG Data Analysis in SPM8 , 2011, Comput. Intell. Neurosci..

[88]  P. Welch The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[89]  David P. Wipf,et al.  A unified Bayesian framework for MEG/EEG source imaging , 2009, NeuroImage.

[90]  Nachiappan Nagappan,et al.  Evaluating the efficacy of test-driven development: industrial case studies , 2006, ISESE '06.

[91]  Se Robinson,et al.  Functional neuroimaging by Synthetic Aperture Magnetometry (SAM) , 1999 .

[92]  C. Chui,et al.  Article in Press Applied and Computational Harmonic Analysis a Randomized Algorithm for the Decomposition of Matrices , 2022 .

[93]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[94]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[95]  S. Taulu,et al.  Applications of the signal space separation method , 2005, IEEE Transactions on Signal Processing.

[96]  Ian M. Mitchell,et al.  Best Practices for Scientific Computing , 2012, PLoS biology.

[97]  Jack W. Tsao,et al.  Observed brain dynamics, P.P. Mitra, H. Bokil. Oxford University Press (2008), ISBN-13: 978-0-19-517808-1, 381 pages, $65.00 , 2009 .

[98]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[99]  Boby George,et al.  A structured experiment of test-driven development , 2004, Inf. Softw. Technol..

[100]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.