Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research


The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this ‘living’ set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.

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Fig. 1: Overview of the total number of MEEG publications with emerging research fields.
Fig. 2: Standard MEEG preprocessing steps.
Fig. 3: Illustration of source modelling approaches.


  1. 1.

    Barba, L.A. Terminologies for reproducible research. Preprint at arXiv (2018).

  2. 2.

    Nichols, T.E. et al. Best Practices in data analysis and sharing in neuroimaging using MRI. Preprint at bioRxiv (2016).

  3. 3.

    Pernet, C.R. et al. Best practices in data analysis and sharing in neuroimaging using MEEG. Preprint at OSF (2018).

  4. 4.

    Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Niso, G. et al. MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Sci. Data 5, 180110 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Pernet, C. R. et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci. Data 6, 103 (2019).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Holdgraf, C. et al. iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Sci. Data 6, 102 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Donchin, M. et al. Publication criteria for studies of evoked potentials (EP) in man: Methodology and publication criteria. in Progress in Clinical Neurophysiology: Attention, Voluntary Contraction and Event-Related Cerebral Potentials. (ed. Desmedt, J. E.) vol. 1 1–11 (Karger, 1977).

  9. 9.

    Pivik, R. T. et al. Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology 30, 547–558 (1993).

    CAS  PubMed  Google Scholar 

  10. 10.

    Picton, T. W. et al. Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology 37, 127–152 (2000).

    CAS  PubMed  Google Scholar 

  11. 11.

    Duncan, C. C. et al. Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin. Neurophysiol. 120, 1883–1908 (2009).

    PubMed  Google Scholar 

  12. 12.

    Gross, J. et al. Good practice for conducting and reporting MEG research. Neuroimage 65, 349–363 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Keil, A. et al. Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiology 51, 1–21 (2014).

    PubMed  Google Scholar 

  14. 14.

    Kane, N. et al. A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clin. Neurophysiol. Pract. 2, 170–185 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hari, R. et al. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clin. Neurophysiol. 129, 1720–1747 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Hari, R. & Puce, A. MEG-EEG Primer. (Oxford Univ. Press, 2017).

  17. 17.

    Jobert, M. et al. Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG). Neuropsychobiology 66, 201–220 (2012).

    PubMed  Google Scholar 

  18. 18.

    Berger, H. Über das Elektroenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten 87, 527–570 (1929).

    Google Scholar 

  19. 19.

    Walter, W. G. The location of cerebral tumors by electroencephalography. Lancet 228, 305–308 (1936).

    Google Scholar 

  20. 20.

    Jasper, H. & Andrews, H. Electro-encephalography: III. Normal differentiation of occipital and precentral regions in man. Arch. Neurol. Psychiatry 39, 96–115 (1938).

    Google Scholar 

  21. 21.

    Krishnan, V., Chang, B.S. & Schomer, D.L. Normal EEG in wakefulness and sleep: adults and elderly. in Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (eds. Schomer, D.L. & Lopes da Silva, F.H.) 202–228 (Oxford Univ. Press, 2017).

  22. 22.

    Katznelson, R.D. EEG recording, electrode placement, and aspects of generator localization. in Electric Fields of the Brain. The Neurophysics of EEG (ed. Nunez, P.) 176–213 (Oxford Univ. Press, 1981).

  23. 23.

    Boudewyn, M. A., Luck, S. J., Farrens, J. L. & Kappenman, E. S. How many trials does it take to get a significant ERP effect? It depends. Psychophysiology 55, e13049 (2018).

    PubMed  Google Scholar 

  24. 24.

    Chaumon, M., Puce, A. & George, N. Statistical power: implications for planning MEG studies. Preprint at bioRxiv (2020).

  25. 25.

    Albers, C. & Lakens, D. When power analyses based on pilot data are biased: inaccurate effect size estimators and follow-up bias. J. Exp. Soc. Psychol. 74, 187–195 (2018).

    Google Scholar 

  26. 26.

    Brysbaert, M. & Stevens, M. Power analysis and effect size in mixed effects models: a tutorial. J. Cogn. 1, 9 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Robbins, K. A., Touryan, J., Mullen, T., Kothe, C. & Bigdely-Shamlo, N. How sensitive are EEG results to preprocessing methods: a benchmarking study. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1081–1090 (2020).

    PubMed  Google Scholar 

  28. 28.

    Baillet, S., Mosher, J. C. & Leahy, R. M. Electromagnetic brain mapping. IEEE Signal Process. Mag. 18, 14–30 (2001).

    Google Scholar 

  29. 29.

    Michel, C. & He, B. EEG Mapping and Source Imaging. in Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (eds. Schomer, D. L. & da Silva, F. H. L.) chap 45 (Oxford University Press, 2018).

  30. 30.

    Michel, C. M. et al. EEG source imaging. Clin. Neurophysiol. 115, 2195–2222 (2004).

    PubMed  Google Scholar 

  31. 31.

    Michel, C. M. & Brunet, D. EEG source imaging: a practical review of the analysis steps. Front. Neurol. 10, 325 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Brodbeck, V. et al. Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients. Brain 134, 2887–2897 (2011).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Hassan, M., Dufor, O., Merlet, I., Berrou, C. & Wendling, F. EEG source connectivity analysis: from dense array recordings to brain networks. PLoS ONE 9, e105041 (2014).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Kass, R. E. et al. Ten simple rules for effective statistical practice. PLoS Comput. Biol. 12, e1004961 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kriegeskorte, N., Lindquist, M. A., Nichols, T. E., Poldrack, R. A. & Vul, E. Everything you never wanted to know about circular analysis, but were afraid to ask. J. Cereb. Blood Flow Metab. 30, 1551–1557 (2010).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Kilner, J. M., Kiebel, S. J. & Friston, K. J. Applications of random field theory to electrophysiology. Neurosci. Lett. 374, 174–178 (2005).

    CAS  PubMed  Google Scholar 

  38. 38.

    Pernet, C. R., Chauveau, N., Gaspar, C. & Rousselet, G. A. LIMO EEG: a toolbox for hierarchical linear modeling of electroencephalographic data. Comput. Intell. Neurosci. 2011, 831409 (2011).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Guthrie, D. & Buchwald, J. S. Significance testing of difference potentials. Psychophysiology 28, 240–244 (1991).

    CAS  PubMed  Google Scholar 

  40. 40.

    Piai, V., Dahlslätt, K. & Maris, E. Statistically comparing EEG/MEG waveforms through successive significant univariate tests: how bad can it be? Psychophysiology 52, 440–443 (2015).

    PubMed  Google Scholar 

  41. 41.

    Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl. Acad. Sci. USA 113, 7900–7905 (2016).

    CAS  PubMed  Google Scholar 

  42. 42.

    Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    PubMed  Google Scholar 

  43. 43.

    Pernet, C. R., Latinus, M., Nichols, T. E. & Rousselet, G. A. Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: a simulation study. J. Neurosci. Methods 250, 85–93 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Varoquaux, G. et al. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. Neuroimage 145, 166–179 (2017). Pt B.

    PubMed  Google Scholar 

  45. 45.

    O’Neill, G. C. et al. Dynamics of large-scale electrophysiological networks: a technical review. Neuroimage 180, 559–576 (2018). Pt B.

    PubMed  Google Scholar 

  46. 46.

    He, B. et al. Electrophysiological brain connectivity: theory and implementation. IEEE Trans. Biomed. Eng. (2019).

  47. 47.

    Friston, K. J. Functional and effective connectivity: a review. Brain Connect. 1, 13–36 (2011).

    PubMed  Google Scholar 

  48. 48.

    Haufe, S., Nikulin, V. V., Müller, K.-R. & Nolte, G. A critical assessment of connectivity measures for EEG data: a simulation study. Neuroimage 64, 120–133 (2013).

    PubMed  Google Scholar 

  49. 49.

    Jensen, O. & Colgin, L. L. Cross-frequency coupling between neuronal oscillations. Trends Cogn. Sci. 11, 267–269 (2007).

    PubMed  Google Scholar 

  50. 50.

    Tort, A. B. L., Komorowski, R., Eichenbaum, H. & Kopell, N. Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. J. Neurophysiol. 104, 1195–1210 (2010).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    van Wijk, B. C. M., Jha, A., Penny, W. & Litvak, V. Parametric estimation of cross-frequency coupling. J. Neurosci. Methods 243, 94–102 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Dupré la Tour, T. et al. Non-linear auto-regressive models for cross-frequency coupling in neural time series. PLOS Comput. Biol. 13, e1005893 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Lai, M., Demuru, M., Hillebrand, A. & Fraschini, M. A comparison between scalp- and source-reconstructed EEG networks. Sci. Rep. 8, 12269 (2018).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Valdes-Sosa, P. A., Roebroeck, A., Daunizeau, J. & Friston, K. Effective connectivity: influence, causality and biophysical modeling. Neuroimage 58, 339–361 (2011).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Reid, A. T. et al. Advancing functional connectivity research from association to causation. Nat. Neurosci. 22, 1751–1760 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Mahjoory, K. et al. Consistency of EEG source localization and connectivity estimates. Neuroimage 152, 590–601 (2017).

    PubMed  Google Scholar 

  57. 57.

    Pearl, P.L. et al. Normal EEG in wakefulness and sleep: preterm; term; infant; adolescent. in Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (eds. Schomer, D.L. & Lopes da Silva, F.H.) 167–201 (Oxford Univ. Press, 2018).

  58. 58.

    Jas, M. et al. A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good practices. Front. Neurosci. 12, 530 (2018).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Rousselet, G. A. & Pernet, C. R. Quantifying the time course of visual object processing using ERPs: it’s time to up the game. Front. Psychol. 2, 107 (2011).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Eglen, S. J. et al. Toward standard practices for sharing computer code and programs in neuroscience. Nat. Neurosci. 20, 770–773 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Leppäaho, E. et al. Discovering heritable modes of MEG spectral power. Hum. Brain Mapp. 40, 1391–1402 (2019).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Pernet, D. C., Heunis, S., Herholz, P. & Halchenko, Y. O. The Open Brain Consent: informing research participants and obtaining consent to share brain imaging data. Preprint at PsyArXiv (2020).

  63. 63.

    Tuckute, G., Hansen, S. T., Pedersen, N., Steenstrup, D. & Hansen, L. K. Single-trial decoding of scalp EEG under natural conditions. Comput. Intell. Neurosci. 2019, 9210785 (2019).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Pion-Tonachini, L., Kreutz-Delgado, K. & Makeig, S. The ICLabel dataset of electroencephalographic (EEG) independent component (IC) features. Data Brief 25, 104101 (2019).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Boto, E. et al. A new generation of magnetoencephalography: room temperature measurements using optically-pumped magnetometers. Neuroimage 149, 404–414 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Boto, E. et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555, 657–661 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Brown, G. D., Yamada, S. & Sejnowski, T. J. Independent component analysis at the neural cocktail party. Trends Neurosci. 24, 54–63 (2001).

    CAS  PubMed  Google Scholar 

  68. 68.

    Jung, T. P. et al. Imaging brain dynamics using independent component analysis. Proc. IEEE Inst. Electr. Electron. Eng. 89, 1107–1122 (2001).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Onton, J., Westerfield, M., Townsend, J. & Makeig, S. Imaging human EEG dynamics using independent component analysis. Neurosci. Biobehav. Rev. 30, 808–822 (2006).

    PubMed  Google Scholar 

  70. 70.

    Uusitalo, M. A. & Ilmoniemi, R. J. Signal-space projection method for separating MEG or EEG into components. Med. Biol. Eng. Comput. 35, 135–140 (1997).

    CAS  PubMed  Google Scholar 

  71. 71.

    Taulu, S., Kajola, M. & Simola, J. Suppression of interference and artifacts by the signal space separation method. Brain Topogr. 16, 269–275 (2004).

    PubMed  Google Scholar 

  72. 72.

    Taulu, S. & Simola, J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol. 51, 1759–1768 (2006).

    CAS  PubMed  Google Scholar 

  73. 73.

    Rousselet, G. A. Does filtering preclude us from studying ERP time-courses? Front. Psychol. 3, 131 (2012).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Widmann, A., Schröger, E. & Maess, B. Digital filter design for electrophysiological data—a practical approach. J. Neurosci. Methods 250, 34–46 (2015).

    PubMed  Google Scholar 

  75. 75.

    Fraschini, M. et al. The effect of epoch length on estimated EEG functional connectivity and brain network organisation. J. Neural Eng. 13, 036015 (2016).

    PubMed  Google Scholar 

  76. 76.

    Grandchamp, R. & Delorme, A. Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials. Front. Psychol. 2, 236 (2011).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Alday, P. M. How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits. Psychophysiology 56, e13451 (2019).

    PubMed  Google Scholar 

  78. 78.

    Engemann, D. A. & Gramfort, A. Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. Neuroimage 108, 328–342 (2015).

    PubMed  Google Scholar 

  79. 79.

    Guggenmos, M., Sterzer, P. & Cichy, R. M. Multivariate pattern analysis for MEG: A comparison of dissimilarity measures. Neuroimage 173, 434–447 (2018).

    PubMed  Google Scholar 

  80. 80.

    Cohen, M. Analyzing Neural Time Series Data. Theory and Practice. (MIT Press, 2014).

  81. 81.

    Bloomfield, P. Fourier Analysis of Time Series: An Introduction. (Wiley, 2013).

  82. 82.

    Boashash, B. Time-frequency Signal Analysis and Processing: a Comprehensive Reference. (Elsevier, 2003).

  83. 83.

    Farahibozorg, S.-R., Henson, R. N. & Hauk, O. Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes. Neuroimage 169, 23–45 (2018).

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Sporns, O. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17, 652–660 (2014).

    CAS  PubMed  Google Scholar 

  85. 85.

    Tewarie, P. et al. Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity. Neuroimage 200, 38–50 (2019).

    PubMed  Google Scholar 

  86. 86.

    Litvak, V. et al. EEG and MEG data analysis in SPM8. Comput. Intell. Neurosci. 2011, 852961 (2011).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Amzica, F. & da Silva, F.H.L. Cellular substrates of brain rhythms. in Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (eds. Schomer, D.L. & Silva, F) ch. 2 (Oxford Univ. Press, 2018).

  88. 88.

    Baillet, S. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci. 20, 327–339 (2017).

    CAS  PubMed  Google Scholar 

  89. 89.

    Uhlhaas, P. J., Pipa, G., Neuenschwander, S., Wibral, M. & Singer, W. A new look at gamma? High- (>60 Hz) γ-band activity in cortical networks: function, mechanisms and impairment. Prog. Biophys. Mol. Biol. 105, 14–28 (2011).

    PubMed  Google Scholar 

  90. 90.

    Lopes da Silva, F. EEG and MEG: relevance to neuroscience. Neuron 80, 1112–1128 (2013).

    CAS  PubMed  Google Scholar 

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The Committee thanks the hundreds of OHBM members who provided feedback on the early version of the report and on the website. Thank you to T. Nichols for his insightful comments on an earlier draft of this Perspective.

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C.P. and A.P. chaired the committee, planned the overall structure of the COBIDAS document and this manuscript. Each author contributed to entire sections of the COBIDAS document used for this manuscript, and all authors contributed and reviewed this manuscript.

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Correspondence to Cyril Pernet or Aina Puce.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Michael Cohen, Joachim Gross, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Pernet, C., Garrido, M.I., Gramfort, A. et al. Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nat Neurosci (2020).

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