MNE: Software for Acquiring, Processing,and Visualizing MEG/EEG Data

  • Lorenz Esch
  • Christoph Dinh
  • Eric Larson
  • Denis Engemann
  • Mainak Jas
  • Sheraz Khan
  • Alexandre GramfortEmail author
  • Matti S. Hämäläinen
Living reference work entry


The methods for acquiring, processing, and visualizing magnetoencephalography (MEG) and electroencephalography (EEG) data are rapidly evolving. Advancements in hardware and software development offer new opportunities for cognitive and clinical neuroscientists but at the same time introduce new challenges as well. In recent years the MEG/EEG community has developed a variety of software tools to overcome these challenges and cater to individual research needs. As part of this endeavor, the MNE software project, which includes MNE-C, MNE-Python, MNE-CPP, and MNE-MATLAB as its subprojects, offers an efficient set of tools addressing certain common needs. Even more importantly, the MNE software family covers diverse use case scenarios. Here, we present the landscape of the MNE project and discuss how it will evolve to address the current and emerging needs of the MEG/EEG community.


Magnetoencephalography (MEG) Electroencephalography (EEG) Software Analysis tools Open-source Real-time analysis Signal processing Machine learning 


  1. Ahn S, Cho H, Kwon M, Kim K, Kwon H, Kim BS, Chang WS, Chang JW, Jun SC (2018) Interbrain phase synchronization during turn-taking verbal interaction a hyperscanning study using simultaneous EEG/MEG. Hum Brain Mapp 39(1):171–188CrossRefGoogle Scholar
  2. Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, Gallacher J, Green J, Matthews P, Pell J et al (2012) UK biobank: current status and what it means for epidemiology. Health Policy Technol 1(3):123–126CrossRefGoogle Scholar
  3. Baillet S (2017) Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci 20:327–339. CrossRefGoogle Scholar
  4. Bilek E, Stößel G, Schäfer A, Clement L, Ruf M, Robnik L, Neukel C, Tost H, Kirsch P, Meyer-Lindenberg A (2017) State-dependent cross-brain information flow in borderline personality disorder. JAMA Psychiat 74(9):949–957CrossRefGoogle Scholar
  5. Dalal SS, Zumer JM, Guggisberg AG, Trumpis M, Wong DDE, Sekihara K, Nagarajan SS (2011) MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. Comput Intell Neurosci 2011:1–17.,
  6. De Tiège X, Carrette E, Legros B, Vonck K, Bourguignon M, Massager N, David P, Van Roost D, Meurs A, Lapere S et al (2012) Clinical added value of magnetic source imaging in the presurgical evaluation of refractory focal epilepsy. J Neurol Neurosurg Psychiatry 83(4): 417–423CrossRefGoogle Scholar
  7. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1): 9–21.,, arXiv:1011.1669v3
  8. Delorme A, Mullen T, Kothe C, Akalin Acar Z, Bigdely-Shamlo N, Vankov A, Makeig S (2011) EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Comput Intell Neurosci 2011:1–12.,, 130714
  9. Dinh C, Strohmeier D, Luessi M, Güllmar D, Baumgarten D, Haueisen J, Hämäläinen MS (2015) Real-time MEG source localization using regional clustering. Brain Topogr 28(6):771–784. CrossRefGoogle Scholar
  10. Dinh C, Esch L, Rühle J, Bollmann S, Güllmar D, Baumgarten D, Hämäläinen MS, Haueisen J (2018) Real-time clustered multiple signal classification (RTC-MUSIC). Brain Topogr 31(1):125–128. CrossRefGoogle Scholar
  11. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A et al (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23(1):28CrossRefGoogle Scholar
  12. Engemann DA, Gramfort A (2015) Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. NeuroImage 108:328–342CrossRefGoogle Scholar
  13. Engemann DA, Raimondo F, King JR, Rohaut B, Louppe G, Faugeras F, Annen J, Cassol H, Gosseries O, Fernandez-Slezak D, Laureys S, Naccache L, Dehaene S, Sitt JD (2018) Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain 141(11):3179–3192. CrossRefGoogle Scholar
  14. Esch L, Sun L, Klüber V, Lew S, Baumgarten D, Grant PE, Okada Y, Haueisen J, Hämäläinen MS, Dinh C (2018) MNE scan: software for real-time processing of electrophysiological data. J Neurosci Methods 303:55–67.,
  15. Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ (2017a) Mriqc: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS one 12(9):e0184661CrossRefGoogle Scholar
  16. Esteban O, Blair RW, Nielson D, Varada J, Marrett S, Thomas A, Poldrack R, Gorgolewski KJ (2017b) MRIQC Web-API: crowdsourcing image quality metrics and expert quality ratings of structural and functional MRI. bioRxiv, p 216671Google Scholar
  17. Esteban O, Markiewicz C, Blair RW, Moodie C, Isik AI, Aliaga AE, Kent J, Goncalves M, DuPre E, Snyder M et al (2018) Fmriprep: a robust preprocessing pipeline for functional MRI. bioRxiv, p 306951Google Scholar
  18. Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis: inflation, flattening, and a surface-based coordinate system. NeuroImage 9:195–207CrossRefGoogle Scholar
  19. Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F et al (2018) Boutiques: a flexible framework to integrate command-line applications in computing platforms. GigaScience 7(5):giy016Google Scholar
  20. Goldstein P, Weissman-Fogel I, Dumas G, Shamay-Tsoory SG (2018) Brain-to-brain coupling during handholding is associated with pain reduction. Proc Nat Acad Sci 115:201703643CrossRefGoogle Scholar
  21. Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO et al (2016) The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3:160044CrossRefGoogle Scholar
  22. Gorgolewski K, Esteban O, Schaefer G, Wandell BA, Poldrack RA (2017a) OpenNeuro a free online platform for sharing and analysis of neuroimaging data. Organization for Human Brain Mapping, Vancouver, p 1677Google Scholar
  23. Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, Churchill NW, Cohen AL, Craddock RC, Devenyi GA et al (2017b) BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol 13(3):e1005209CrossRefGoogle Scholar
  24. Graichen U, Eichardt R, Fiedler P, Strohmeier D, Zanow F, Haueisen J (2015) SPHARA – a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG. PLoS ONE 10:1–22. CrossRefGoogle Scholar
  25. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L et al (2013a) MEG and EEG data analysis with MNE-Python. Front Neurosci 7:267CrossRefGoogle Scholar
  26. Gramfort A, Strohmeier D, Haueisen J, Hämäläinen MS, Kowalski M (2013b) Time-frequency mixed-norm estimates: Sparse M/EEG 70:410–422. Google Scholar
  27. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Parkkonen L, Hämäläinen MS (2014) MNE software for processing MEG and EEG data. NeuroImage 86:446–460.,,, NIHMS150003
  28. Gross J, Kujala J, Hämäläinen MS, Timmermann L, Schnitzler A, Salmelin R (2001) Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc Nat Acad Sci 98(2):694–699CrossRefGoogle Scholar
  29. Guennebaud G, Benoît J, Others (2018) Eigen v3.
  30. Höhne J, Holz E, Staiger-Sälzer P, Müller KR, Kübler A, Tangermann M (2014) Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution. PLoS ONE 9(8):1–11. CrossRefGoogle Scholar
  31. Jas M, Engemann DA, Bekhti Y, Raimondo F, Gramfort A (2017) Autoreject: automated artifact rejection for MEG and EEG data. NeuroImage 159:417–429CrossRefGoogle Scholar
  32. Jas M, Larson E, Engemann DA, Leppakangas J, Taulu S, Brooks T, Hämäläinen MS, Gramfort A (2018) A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments and good practices. Front Neurosci 12:530.,
  33. Jiang L, Stocco A, Losey DM, Abernethy JA, Prat CS, Rao RPN (2018) BrainNet: a multi-person brain-to-brain interface for direct collaboration between brains. ArXiv e-prints 1809.08632Google Scholar
  34. Khan S, Michmizos K, Tommerdahl M, Ganesan S, Kitzbichler MG, Zetino M, Garel KLA, Herbert MR, Hämäläinen MS, Kenet T (2015) Somatosensory cortex functional connectivity abnormalities in autism show opposite trends, depending on direction and spatial scale. Brain 138(5):1394–1409. CrossRefGoogle Scholar
  35. Khan S, Hashmi JA, Mamashli F, Michmizos K, Kitzbichler MG, Bharadwaj H, Bekhti Y, Ganesan S, Garel KLA, Whitfield-Gabrieli S, Gollub RL, Kong J, Vaina LM, Rana KD, Stufflebeam SM, Hämäläinen MS, Kenet T (2018) Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. NeuroImage 174:57–68.,
  36. King JR, Gwilliams L, Holdgraf C, Sassenhagen J, Barachant A, Engemann D, Larson E, Gramfort A (2018) Encoding and decoding neuronal dynamics: methodological framework to uncover the algorithms of cognition. In: The cognitive neurosciences VI. Google Scholar
  37. Kriegeskorte N, Mur M, Bandettini P (2008) Representational similarity analysis–connecting the branches of systems neuroscience. Front Syst Neurosci 2:4CrossRefGoogle Scholar
  38. Leonelli S (2016) Data-centric biology: a philosophical study. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  39. Liem F, Varoquaux G, Kynast J, Beyer F, Masouleh SK, Huntenburg JM, Lampe L, Rahim M, Abraham A, Craddock RC et al (2017) Predicting brain-age from multimodal imaging data captures cognitive impairment. NeuroImage 148:179–188CrossRefGoogle Scholar
  40. Litvak V, Mattout J, Kiebel S, Phillips C, Henson R, Kilner J, Barnes G, Oostenveld R, Daunizeau J, Flandin G, Penny W, Friston K (2011) EEG and MEG data analysis in SPM8. Comput Intell Neurosci 2011:1–32.,
  41. Maris E, Oostenveld R (2007) Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164(1):177–190CrossRefGoogle Scholar
  42. Mohanty R, Sinha AM, Remsik AB, Dodd KC, Young BM, Jacobson T, Mcmillan M, Thoma J, Advani H, Nair VA et al (2018) Machine learning classification to identify the stage of brain-computer interface therapy for stroke rehabilitation using functional connectivity. Front Neurosci 12:353CrossRefGoogle Scholar
  43. Mosher JC, Leahy RM (1999) Source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans Sig Process 47(2):332–340CrossRefGoogle Scholar
  44. Niso G, Rogers C, Moreau JT, Chen LY, Madjar C, Das S, Bock E, Tadel F, Evans AC, Jolicoeur P et al (2016) OMEGA: the open MEG archive. Neuroimage 124:1182–1187CrossRefGoogle Scholar
  45. Niso G, Gorgolewski KJ, Bock E, Brooks TL, Flandin G, Gramfort A, Henson RN, Jas M, Litvak V, Moreau JT et al (2018) MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Sci Data 5:180110CrossRefGoogle Scholar
  46. Okada Y, Hämäläinen MS, Pratt K, Mascarenas A, Miller P, Han M, Robles J, Cavallini A, Power B, Sieng K, Sun L, Lew S, Dosh C, Ahtam B, Dinh C, Esch L, Grant E, Nummenmaa A, Paulson D (2016) BabyMEG: a whole-head pediatric magnetoencephalography system for human brain development research. Rev Sci Instrum 87(9):1–13CrossRefGoogle Scholar
  47. Oostenveld R, Fries P, Maris E, Schoffelen JM (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011: 1–9., 156869
  48. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830MathSciNetzbMATHGoogle Scholar
  49. Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafò MR, Nichols TE, Poline JB, Vul E, Yarkoni T (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 18(2):115CrossRefGoogle Scholar
  50. QtProject (2018) Qt.
  51. Rao RPN, Stocco A, Bryan M, Sarma D, Youngquist TM, Wu J, Prat CS (2014) A direct brain-to-brain interface in humans. PLoS ONE 9(11):1–12. CrossRefGoogle Scholar
  52. Rivet B, Souloumiac A, Attina V, Gibert G (2009) xDAWN algorithm to enhance evoked potentials: application to brain–computer interface. IEEE Trans Biomed Eng 56(8):2035–2043CrossRefGoogle Scholar
  53. Sharon D, Hämäläinen MS, Tootell RBH, Halgren E, Belliveau JW (2007) The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. Neuroimage 36:1225–1235. CrossRefGoogle Scholar
  54. Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011:1–13.,, 879716
  55. Taulu S, Kajola M (2005) Presentation of electromagnetic multichannel data: the signal space separation method. J Appl Phys 97(12):124905CrossRefGoogle Scholar
  56. Taylor JR, Williams N, Cusack R, Auer T, Shafto MA, Dixon M, Tyler LK, Henson RN et al (2017) The Cambridge centre for ageing and neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144:262–269CrossRefGoogle Scholar
  57. Uusitalo MA, Ilmoniemi RJ (1997) Signal-space projection method for separating MEG or EEG into components. Med Biol Eng Comput 35(2):135–140CrossRefGoogle Scholar
  58. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K, Consortium WMH et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79CrossRefGoogle Scholar
  59. Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 44(9):867–880CrossRefGoogle Scholar
  60. Westner BU, Dalal SS, Hanslmayr S, Staudigl T (2018) Across-subjects classification of stimulus modality from human MEG high frequency activity. PLoS Comput Biol 14(3):e1005938CrossRefGoogle Scholar
  61. Wipf D, Nagarajan S (2009) A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 44(3):947–966.,
  62. Wolpaw JR, McFarland DJ, Neat GW, Forneris CA (1991) An eeg-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78(3):252–259CrossRefGoogle Scholar
  63. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD (2011) Large-scale automated synthesis of human functional neuroimaging data. Nat Methods 8(8):665CrossRefGoogle Scholar
  64. Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, Mariani S, Mobley D, Redline S (2018) The national sleep research resource: towards a sleep data commons. J Am Med Inform Assoc 25(10):1351–1358CrossRefGoogle Scholar
  65. Zhdanov A, Nurminen J, Baess P, Hirvenkari L, Jousmäki V, Mäkelä JP, Mandel A, Meronen L, Hari R, Parkkonen L (2015) An internet-based real-time audiovisual link for dual MEG recordings. PLoS One 10(6):e0128485CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lorenz Esch
    • 1
    • 2
    • 3
  • Christoph Dinh
    • 3
    • 4
  • Eric Larson
    • 5
  • Denis Engemann
    • 6
  • Mainak Jas
    • 1
  • Sheraz Khan
    • 1
    • 7
    • 8
  • Alexandre Gramfort
    • 6
    Email author
  • Matti S. Hämäläinen
    • 1
    • 7
    • 8
  1. 1.Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUSA
  2. 2.Institute of Biomedical Engineering and InformaticsTechnische Universität IlmenauIlmenauGermany
  3. 3.Boston Children’s HospitalBostonUSA
  4. 4.Institute for Medical Engineering, Research Campus STIMULATEOtto-von-Guericke UniversityMagdeburgGermany
  5. 5.Institute for Learning and Brain SciencesUniversity of WashingtonSeattleUSA
  6. 6.INRIA, CEA, Université Paris-SaclayPalaiseauFrance
  7. 7.Massachusetts Institute of TechnologyCambridgeUSA
  8. 8.Harvard Medical SchoolBostonUSA

Section editors and affiliations

  • Alexandre Gramfort
    • 1
  1. 1.Inria Saclay Île-de-FrancePalaiseauFrance

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