General bibliography

The references below are arranged alphabetically by first author.

  1. Pierre Ablin, Jean-Francois Cardoso, and Alexandre Gramfort. Faster Independent Component Analysis by preconditioning with hessian approximations. IEEE Transactions on Signal Processing, 66(15):4040–4049, 2018. doi:10.1109/TSP.2018.2844203.

  2. David J. Acunzo, Graham MacKenzie, and Mark C.W. van Rossum. Systematic biases in early ERP and ERF components as a result of high-pass filtering. Journal of Neuroscience Methods, 209(1):212–218, 2012. doi:10.1016/j.jneumeth.2012.06.011.

  3. Fiorenzo Artoni, Arnaud Delorme, and Scott Makeig. Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition. NeuroImage, 175:176–187, 2018. doi:10.1016/j.neuroimage.2018.03.016.

  4. Brian B. Avants, Charles L. Epstein, Murray C. Grossman, and James C. Gee. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26–41, 2008. doi:10.1016/j.media.2007.06.004.

  5. Sylvain Baillet, John C. Mosher, and Richard M. Leahy. Electromagnetic brain mapping. IEEE Signal Processing Magazine, 18(6):14–30, 2001. doi:10.1109/79.962275.

  6. Alexandre Barachant, Stephane Bonnet, Marco Congedo, and Christian Jutten. Common spatial pattern revisited by Riemannian geometry. In 2010 IEEE International Workshop on Multimedia Signal Processing, 472–476. 2010. doi:10.1109/MMSP.2010.5662067.

  7. David Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge, 2012. ISBN 978-0-521-51814-7. URL: http://www.cs.ucl.ac.uk/staff/d.barber/brml/.

  8. Yousra Bekhti, Daniel Strohmeiery, Mainak Jas, Roland Badeau, and Alexandre Gramfort. M/EEG source localization with multi-scale time-frequency dictionaries. In Proceedings of PRNI-2016, 1–4. Trento, 2016. IEEE. doi:10.1109/PRNI.2016.7552337.

  9. Anthony J. Bell and Terrence J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6):1129–1159, 1995. doi:10.1162/neco.1995.7.6.1129.

  10. Anna Rita Bentivoglio, Susan B. Bressman, Emanuele Cassetta, Donatella Carretta, Pietro Tonali, and Alberto Albanese. Analysis of blink rate patterns in normal subjects. Movement Disorders, 12(6):1028–1034, 1997. doi:10.1002/mds.870120629.

  11. Patrick Berg and Michael Scherg. A fast method for forward computation of multiple-shell spherical head models. Electroencephalography and Clinical Neurophysiology, 90(1):58–64, 1994. doi:10.1016/0013-4694(94)90113-9.

  12. Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, and Klaus-Robert Müller. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine, 25(1):41–56, 2008. doi:10.1109/MSP.2008.4408441.

  13. Fred L. Bookstein. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567–585, 1989. doi:10.1109/34.24792.

  14. Matthew J. Brookes, Jiri Vrba, Stephen E. Robinson, Claire M. Stevenson, Andrew M. Peters, Gareth R. Barnes, Arjan Hillebrand, and Peter G. Morris. Optimising experimental design for MEG beamformer imaging. NeuroImage, 39(4):1788–1802, 2008. doi:10.1016/j.neuroimage.2007.09.050.

  15. Filipa Campos Viola, Jeremy Thorne, Barrie Edmonds, Till Schneider, Tom Eichele, and Stefan Debener. Semi-automatic identification of independent components representing EEG artifact. Clinical Neurophysiology, 120(5):868–877, 2009. doi:10.1016/j.clinph.2009.01.015.

  16. Stanislas Chambon, Mathieu N. Galtier, Pierrick J. Arnal, Gilles Wainrib, and Alexandre Gramfort. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4):758–769, 2018. doi:10.1109/TNSRE.2018.2813138.

  17. Yilun Chen, Ami Wiesel, Yonina C. Eldar, and Alfred O. Hero. Shrinkage algorithms for MMSE covariance estimation. IEEE Transactions on Signal Processing, 58(10):5016–5029, 2010. doi:10.1109/TSP.2010.2053029.

  18. Radoslaw Martin Cichy, Dimitrios Pantazis, and Aude Oliva. Resolving human object recognition in space and time. Nature Neuroscience, 17(3):455–462, 2014. doi:10.1038/nn.3635.

  19. David Cohen and Hidehiro Hosaka. Part II magnetic field produced by a current dipole. Journal of Electrocardiology, 9(4):409–417, 1976. doi:10.1016/S0022-0736(76)80041-6.

  20. Mike X. Cohen. Analyzing Neural Time Series Data: Theory and Practice. MIT Press, 2014.

  21. Ronald E. Crochiere and Lawrence R. Rabiner. Multirate Digital Signal Processing. Pearson, Englewood Cliffs, NJ, 1 edition edition, December 1983. ISBN 978-0-13-605162-6.

  22. Michael J. Crosse, Giovanni M. Di Liberto, Adam Bednar, and Edmund C. Lalor. The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli. Frontiers in Human Neuroscience, 2016. doi:10.3389/fnhum.2016.00604.

  23. Sarang S. Dalal, Adrian G. Guggisberg, Erik Edwards, Kensuke Sekihara, Anne M. Findlay, Ryan T. Canolty, Mitchel S. Berger, Robert T. Knight, Nicholas M. Barbaro, Heidi E. Kirsch, and Srikantan S. Nagarajan. Five-dimensional neuroimaging: localization of the time–frequency dynamics of cortical activity. NeuroImage, 40(4):1686–1700, 2008. doi:10.1016/j.neuroimage.2008.01.023.

  24. Anders M. Dale, Bruce Fischl, and Martin I. Sereno. Cortical surface-based analysis: I. segmentation and surface reconstruction. NeuroImage, 9(2):179–194, 1999. doi:10.1006/nimg.1998.0395.

  25. Anders M. Dale, Arthur K. Liu, Bruce R. Fischl, Randy L. Buckner, John W. Belliveau, Jeffrey D. Lewine, and Eric Halgren. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26(1):55–67, 2000. doi:10.1016/S0896-6273(00)81138-1.

  26. Anders M. Dale and Martin I. Sereno. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. Journal of Cognitive Neuroscience, 5(2):162–176, 1993. doi:10.1162/jocn.1993.5.2.162.

  27. Jürgen Dammers, Michael Schiek, Frank Boers, Carmen Silex, Mikhail Zvyagintsev, Uwe Pietrzyk, and Klaus Mathiak. Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings. IEEE Transactions on Biomedical Engineering, 55(10):2353–2362, 2008. doi:10.1109/TBME.2008.926677.

  28. Felix Darvas, John J. Ermer, John C. Mosher, and Richard M. Leahy. Generic head models for atlas-based EEG source analysis. Human Brain Mapping, 27(2):129–143, 2006. doi:10.1002/hbm.20171.

  29. Christophe Destrieux, Bruce Fischl, Anders Dale, and Eric Halgren. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 53(1):1–15, 2010. doi:10.1016/j.neuroimage.2010.06.010.

  30. Stéphane Dufau, Jonathan Grainger, Katherine J. Midgley, and Phillip J. Holcomb. A thousand words are worth a picture: snapshots of printed-word processing in an event-related potential megastudy. Psychological Science, 26(12):1887–1897, 2015. doi:10.1177/0956797615603934.

  31. Sven Dähne, Frank C. Meinecke, Stefan Haufe, Johannes Höhne, Michael Tangermann, Klaus-Robert Müller, and Vadim V. Nikulin. SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters. NeuroImage, 86:111–122, 2014. doi:10.1016/j.neuroimage.2013.07.079.

  32. Bradley Efron and Trevor Hastie. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Number 5 in Institute of Mathematical Statistics Monographs. Cambridge University Press, New York, 2016. ISBN 978-1-107-14989-2. URL: https://web.stanford.edu/~hastie/CASI/.

  33. Denis A. Engemann and Alexandre Gramfort. Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. NeuroImage, 108:328–342, 2015. doi:10.1016/j.neuroimage.2014.12.040.

  34. Bruce Fischl, David H. Salat, André J.W. van der Kouwe, Nikos Makris, Florent Ségonne, Brian T. Quinn, and Anders M. Dale. Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23:S69–S84, 2004. doi:10.1016/j.neuroimage.2004.07.016.

  35. Bruce Fischl, Martin I. Sereno, and Anders M. Dale. Cortical surface-based analysis: II. inflation, flattening, and a surface-based coordinate system. NeuroImage, 9(2):195–207, 1999. doi:10.1006/nimg.1998.0396.

  36. Bruce Fischl, Martin I. Sereno, Roger B.H. Tootell, and Anders M. Dale. High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8(4):272–284, 1999. doi:10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4.

  37. Frank A Fishburn, Ruth S Ludlum, Chandan J Vaidya, and Andrei V Medvedev. Temporal derivative distribution repair (tddr): a motion correction method for fnirs. NeuroImage, 184:171–179, 2019. doi:10.1016/j.neuroimage.2018.09.025.

  38. Matthew F. Glasser, Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, Jesper Andersson, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith, and David C. Van Essen. A multi-modal parcellation of human cerebral cortex. Nature, 536(7615):171–178, 2016. doi:10.1038/nature18933.

  39. Matthew F. Glasser, Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, Jesper Andersson, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith, and David C. Van Essen. Supplementary neuroanatomical results for “A multi-modal parcellation of human cerebral cortex”. Nature, 2016. URL: https://static-content.springer.com/esm/art%3A10.1038%2Fnature18933/MediaObjects/41586_2016_BFnature18933_MOESM330_ESM.pdf#page=2.

  40. Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 2000. doi:10.1161/01.CIR.101.23.e215.

  41. Daniel M. Goldenholz, Seppo P. Ahlfors, Matti S. Hämäläinen, Dahlia Sharon, Mamiko Ishitobi, Lucia M. Vaina, and Steven M. Stufflebeam. Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography. Human Brain Mapping, 30(4):1077–1086, 2009. doi:10.1002/hbm.20571.

  42. Sónia I. Gonçalves, Jan Casper de Munck, Jeroen P. A. Verbunt, Fetsje Bijma, Rob M. Heethaar, and Fernando Lopes da Silva. In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head. IEEE Transactions on Biomedical Engineering, 50(6):754–767, 2003. doi:10.1109/TBME.2003.812164.

  43. Bernhard Graimann, Jane E. Huggins, Simon P. Levine, and Gert Pfurtscheller. Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clinical Neurophysiology, 113(1):43–47, 2002. doi:10.1016/S1388-2457(01)00697-6.

  44. Alexandre Gramfort, Matthieu Kowalski, and Matti S. Hämäläinen. Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods. Physics in Medicine and Biology, 57(7):1937–1961, 2012. doi:10.1088/0031-9155/57/7/1937.

  45. Alexandre Gramfort, Daniel Strohmeier, Jens Haueisen, Matti S. Hämäläinen, and Matthieu Kowalski. Functional brain imaging with M/EEG using structured sparsity in time-frequency dictionaries. In Gábor Székely and Horst K. Hahn, editors, Information Processing in Medical Imaging, volume 6801, pages 600–611. Springer, Berlin; Heidelberg, 2011. doi:10.1007/978-3-642-22092-0_49.

  46. Alexandre Gramfort, Daniel T. Strohmeier, Jens Haueisen, Matti S. Hämäläinen, and Matthieu Kowalski. Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations. NeuroImage, 70:410–422, 2013. doi:10.1016/j.neuroimage.2012.12.051.

  47. Gabriele Gratton, Michael G. H Coles, and Emanuel Donchin. A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4):468–484, April 1983. doi:10.1016/0013-4694(83)90135-9.

  48. Douglas N. Greve, Lise Van der Haegen, Qing Cai, Steven Stufflebeam, Mert R. Sabuncu, Bruce Fischl, and Marc Brysbaert. A surface-based analysis of language lateralization and cortical asymmetry. Journal of Cognitive Neuroscience, 25(9):1477–1492, 2013. doi:10.1162/jocn_a_00405.

  49. Joachim Groß, Jan Kujala, Matti S. Hämäläinen, Lars Timmermann, Alfons Schnitzler, and Riitta Salmelin. Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proceedings of the National Academy of Sciences, 98(2):694–699, 2001. doi:10.1073/pnas.98.2.694.

  50. Jeff Hanna, Cora Kim, and Nadia Müller-Voggel. External noise removed from magnetoencephalographic signal using independent component analysis of reference channels. Journal of Neuroscience Methods, 2020. doi:10.1016/j.jneumeth.2020.108592.

  51. Riitta Hari and Riitta Salmelin. Human cortical oscillations: a neuromagnetic view through the skull. Trends in Neurosciences, 20(1):44–49, 1997. doi:10.1016/S0166-2236(96)10065-5.

  52. Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, John-Dylan Haynes, Benjamin Blankertz, and Felix Bießmann. On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87:96–110, 2014. doi:10.1016/j.neuroimage.2013.10.067.

  53. Olaf Hauk, Matt H. Davis, Michael A. Ford, Friedmann Pulvermüller, and William D. Marslen-Wilson. The time course of visual word recognition as revealed by linear regression analysis of ERP data. NeuroImage, 30(4):1383–1400, 2006. doi:10.1016/j.neuroimage.2005.11.048.

  54. Gary W. Heiman. Research Methods in Psychology. Houghton Mifflin Company, Boston, 3 edition, 2002. ISBN 978-0-618-17028-9.

  55. Joerg F Hipp, David J Hawellek, Maurizio Corbetta, Markus Siegel, and Andreas K Engel. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nature Neuroscience, 15(6):884–890, 2012. doi:10.1038/nn.3101.

  56. Joerg F. Hipp, Andreas K. Engel, and Markus Siegel. Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron, 69(2):387–396, 2011. doi:10.1016/j.neuron.2010.12.027.

  57. Christopher R. Holdgraf, Wendy de Heer, Brian Pasley, Jochem Rieger, Nathan Crone, Jack J. Lin, Robert T. Knight, and Frédéric E. Theunissen. Rapid tuning shifts in human auditory cortex enhance speech intelligibility. Nature Communications, 2016. doi:10.1038/ncomms13654.

  58. Aapo Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626–634, 1999. doi:10.1109/72.761722.

  59. Matti S. Hämäläinen and Riitta Hari. Magnetoencephalographic characterization of dynamic brain activation: basic principles and methods of data collection and source analysis. In Arthur W. Toga and John C. Mazziotta, editors, Brain Mapping: The Methods, pages 227 – 253. Academic Press, San Diego, 2 edition, 2002. doi:10.1016/B978-012693019-1/50012-5.

  60. Matti S. Hämäläinen, Riitta Hari, Risto J. Ilmoniemi, Jukka Knuutila, and Olli V. Lounasmaa. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65(2):413–497, 1993. doi:10.1103/RevModPhys.65.413.

  61. Matti S. Hämäläinen and Ilmoniemi Ilmoniemi, Risto J. Interpreting magnetic fields of the brain: minimum norm estimates. Medical & Biological Engineering & Computing, 32(1):35–42, 1994. doi:10.1007/BF02512476.

  62. Matti S. Hämäläinen and Risto J. Ilmoniemi. Interpreting measured magnetic fields of the brain: estimates of current distributions. Technical Report TKK-F-A559, Helsinki University of Technology, Helsinki, 1984.

  63. Matti S. Hämäläinen and Jukka Sarvas. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Transactions on Biomedical Engineering, 36(2):165–171, 1989. doi:10.1109/10.16463.

  64. Emmanuel C. Ifeachor and Barrie W. Jervis. Digital Signal Processing: A Practical Approach. Pearson, 2 edition, 2002.

  65. Kevin A. Jones, Bernice Porjesz, David Chorlian, Madhavi Rangaswamy, Chella Kamarajan, Ajayan Padmanabhapillai, Arthur Stimus, and Henri Begleiter. S-transform time-frequency analysis of P300 reveals deficits in individuals diagnosed with alcoholism. Clinical Neurophysiology, 117(10):2128–2143, 2006. doi:10.1016/j.clinph.2006.02.028.

  66. Jorge Jovicich, Silvester Czanner, Douglas Greve, Elizabeth Haley, Andre van der Kouwe, Randy Gollub, David Kennedy, Franz Schmitt, Gregory Brown, James MacFall, Bruce Fischl, and Anders Dale. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. NeuroImage, 30(2):436–443, 2006. doi:10.1016/j.neuroimage.2005.09.046.

  67. Emily S. Kappenman and Steven J. Luck. The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology, 2010. doi:10.1111/j.1469-8986.2010.01009.x.

  68. Jürgen Kayser and Craig E. Tenke. On the benefits of using surface Laplacian (Current Source Density) methodology in electrophysiology. International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 97(3):171–173, September 2015. doi:10.1016/j.ijpsycho.2015.06.001.

  69. B. Kemp, A. H. Zwinderman, B. Tuk, H. A. C. Kamphuisen, and J. J. L. Oberyé. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering, 47(9):1185–1194, 2000. doi:10.1109/10.867928.

  70. Sheraz Khan and David Cohen. Note: magnetic noise from the inner wall of a magnetically shielded room. Review of Scientific Instruments, 84(5):056101, 2013. doi:10.1063/1.4802845.

  71. Sheraz Khan, Javeria A. Hashmi, Fahimeh Mamashli, Konstantinos Michmizos, Manfred G. Kitzbichler, Hari Bharadwaj, Yousra Bekhti, Santosh Ganesan, Keri-Lee A. Garel, Susan Whitfield-Gabrieli, Randy L. Gollub, Jian Kong, Lucia M. Vaina, Kunjan D. Rana, Steven M. Stufflebeam, Matti S. Hämäläinen, and Tal Kenet. Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. NeuroImage, 174:57–68, 2018. doi:10.1016/j.neuroimage.2018.02.018.

  72. Jean-Rémi King and Stanislas Dehaene. Characterizing the dynamics of mental representations: the temporal generalization method. Trends in Cognitive Sciences, 18(4):203–210, 2014. doi:10.1016/j.tics.2014.01.002.

  73. Jean-Rémi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache, and Stanislas Dehaene. Two distinct dynamic modes subtend the detection of unexpected sounds. PLoS ONE, 9(1):e85791, 2014. doi:10.1371/journal.pone.0085791.

  74. Jean-Rémi King, Laura Gwilliams, Chris Holdgraf, Jona Sassenhagen, Alexandre Barachant, Denis Engemann, Eric Larson, and Alexandre Gramfort. Encoding and decoding neuronal dynamics: methodological framework to uncover the algorithms of cognition. hal-01848442, 2018. URL: https://hal.archives-ouvertes.fr/hal-01848442.

  75. Jukka E. T. Knuutila, Antti I. Ahonen, Matti S. Hämäläinen, Matti J. Kajola, P. P. Laine, Olli V. Lounasmaa, Lauri T. Parkkonen, Juha T. A. Simola, and Claudia D. Tesche. A 122-channel whole-cortex SQUID system for measuring the brain’s magnetic fields. IEEE Transactions on Magnetics, 29(6):3315–3320, 1993. doi:10.1109/20.281163.

  76. Zoltan J. Koles. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalography and Clinical Neurophysiology, 79(6):440–447, 1991. doi:10.1016/0013-4694(91)90163-X.

  77. Zoltan J. Koles, Michael S. Lazar, and Steven Z. Zhou. Spatial patterns underlying population differences in the background EEG. Brain Topography, 2(4):275–284, 1990. doi:10.1007/BF01129656.

  78. Nikolaus Kriegeskorte, Marieke Mur, and Peter Bandettini. Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2:4, 2008. doi:10.3389/neuro.06.004.2008.

  79. Aarre Laakso and Garrison Cottrell. Content and cluster analysis: assessing representational similarity in neural systems. Philosophical Psychology, 13(1):47–76, 2000. doi:10.1080/09515080050002726.

  80. Jean-Philippe Lachaux, Eugenio Rodriguez, Jacques Martinerie, and Francisco J. Varela. Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4):194–208, 1999. doi:10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C.

  81. Eric Larson and Adrian K.C. Lee. The cortical dynamics underlying effective switching of auditory spatial attention. NeuroImage, 64:365–370, 2013. doi:10.1016/j.neuroimage.2012.09.006.

  82. Eric Larson and Samu Taulu. The importance of properly compensating for head movements during MEG acquisition across different age groups. Brain Topography, 30(2):172–181, 2017. doi:10.1007/s10548-016-0523-1.

  83. Eric Larson and Samu Taulu. Reducing sensor noise in MEG and EEG recordings using oversampled temporal projection. IEEE Transactions on Biomedical Engineering, 65(5):1002–1013, 2018. doi:10.1109/TBME.2017.2734641.

  84. Olivier Ledoit and Michael Wolf. A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2):365–411, 2004. doi:10.1016/S0047-259X(03)00096-4.

  85. Te-Won Lee, Mark Girolami, and Terrence J. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 11(2):417–441, 1999. doi:10.1162/089976699300016719.

  86. Seok Lew, Carsten H. Wolters, Alfred Anwander, Scott Makeig, and Rob S. MacLeod. Improved EEG source analysis using low-resolution conductivity estimation in a four-compartment finite element head model. Human Brain Mapping, 30(9):2862–2878, 2009. doi:10.1002/hbm.20714.

  87. Fa-Hsuan Lin, John W. Belliveau, Anders M. Dale, and Matti S. Hämäläinen. Distributed current estimates using cortical orientation constraints. Human Brain Mapping, 27(1):1–13, 2006. doi:10.1002/hbm.20155.

  88. Fa-Hsuan Lin, Thomas Witzel, Matti S. Hämäläinen, Anders M. Dale, John W. Belliveau, and Steven M. Stufflebeam. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain. NeuroImage, 23(2):582–595, 2004. doi:10.1016/j.neuroimage.2004.04.027.

  89. Arthur K. Liu, John W. Belliveau, and Anders M. Dale. Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. Proceedings of the National Academy of Sciences, 95(15):8945–8950, 1998. doi:10.1073/pnas.95.15.8945.

  90. Arthur K. Liu, Anders M. Dale, and John W. Belliveau. Monte Carlo simulation studies of EEG and MEG localization accuracy. Human Brain Mapping, 16(1):47–62, 2002. doi:10.1002/hbm.10024.

  91. Richard Lowry. One-way analysis of variance for independent samples. 2014. URL: http://vassarstats.net/textbook/.

  92. Burkhard Maess, Erich Schröger, and Andreas Widmann. High-pass filters and baseline correction in M/EEG analysis-continued discussion. Journal of Neuroscience Methods, 266:171–172, 2016. doi:10.1016/j.jneumeth.2016.01.016.

  93. Burkhard Maess, Erich Schröger, and Andreas Widmann. High-pass filters and baseline correction in M/EEG analysis. Commentary on: “How inappropriate high-pass filters can produce artefacts and incorrect conclusions in ERP studies of language and cognition”. Journal of Neuroscience Methods, 266:164–165, 2016. doi:10.1016/j.jneumeth.2015.12.003.

  94. Scott Makeig. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography and Clinical Neurophysiology, 86(4):283–293, 1993. doi:10.1016/0013-4694(93)90110-H.

  95. Eric Maris and Robert Oostenveld. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1):177–190, 2007. doi:10.1016/j.jneumeth.2007.03.024.

  96. Kathryn Mills. HCP-MMP1.0 projected on fsaverage. 2016. doi:10.6084/m9.figshare.3498446.v2.

  97. John C. Mosher and Richard M. Leahy. Source localization using recursively applied and projected (RAP) MUSIC. IEEE Transactions on Signal Processing, 47(2):332–340, 1999. doi:10.1109/78.740118.

  98. John C. Mosher, Richard M. Leahy, and Paul S. Lewis. EEG and MEG: forward solutions for inverse methods. IEEE Transactions on Biomedical Engineering, 46(3):245–259, 1999. doi:10.1109/10.748978.

  99. Ali Moukadem, Zied Bouguila, Djaffar Ould Abdeslam, and Alain Dieterlen. Stockwell transform optimization applied on the detection of split in heart sounds. In Proceedings of EUSIPCO-2014, 2015–2019. Lisbon, 2014. IEEE. URL: https://ieeexplore.ieee.org/document/6952743.

  100. M. S. Mourtazaev, B. Kemp, A. H. Zwinderman, and H. A. C. Kamphuisen. Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep, 18(7):557–564, 1995. doi:10.1093/sleep/18.7.557.

  101. Suresh Muthukumaraswamy. High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Frontiers in Human Neuroscience, 7:138, 2013. doi:10.3389/fnhum.2013.00138.

  102. Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, and Joseph Salmon. GAP safe screening rules for sparse-group lasso. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, 388–396. Curran Associates, Inc., 2016. URL: http://papers.nips.cc/paper/6405-gap-safe-screening-rules-for-sparse-group-lasso.pdf.

  103. Thomas E. Nichols and Andrew P. Holmes. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15(1):1–25, 2002. doi:10.1002/hbm.1058.

  104. Guido Nolte, Ou Bai, Lewis Wheaton, Zoltan Mari, Sherry Vorbach, and Mark Hallett. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical Neurophysiology, 115(10):2292–2307, 2004. doi:10.1016/j.clinph.2004.04.029.

  105. Guido Nolte, Andreas Ziehe, Vadim V. Nikulin, Alois Schlögl, Nicole Krämer, Tom Brismar, and Klaus-Robert Müller. Robustly estimating the flow direction of information in complex physical systems. Physical Review Letters, 2008. doi:10.1103/PhysRevLett.100.234101.

  106. Jussi Nurminen, Hilla Paananen, and Jyrki Mäkelä. High frequency somatosensory MEG: evoked responses, freesurfer reconstruction. 2017. doi:10.5281/zenodo.889234.

  107. Thom F. Oostendorp, Jean Delbeke, and Dick F. Stegeman. The conductivity of the human skull: results of in vivo and in vitro measurements. IEEE Transactions on Biomedical Engineering, 47(11):1487–1492, 2000. doi:10.1109/TBME.2000.880100.

  108. Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Prentice Hall, Upper Saddle River, NJ, 2 edition edition, January 1999. ISBN 978-0-13-754920-7.

  109. Thomas W. Parks and C. Sidney S. Burrus. Digital Filter Design. Topics in Digital Signal Processing. Wiley, New York, 1987. ISBN 978-0-471-82896-9.

  110. Roberto D. Pascual-Marqui, Dietrich Lehmann, Martha Koukkou, Kieko Kochi, Peter Anderer, Bernd Saletu, Hideaki Tanaka, Koichi Hirata, E. Roy John, Leslie Prichep, Rolando Biscay-Lirio, and Toshihiko Kinoshita. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1952):3768–3784, October 2011. URL: https://royalsocietypublishing.org/doi/full/10.1098/rsta.2011.0081, doi:10.1098/rsta.2011.0081.

  111. Donald B. Percival and Andrew T. Walden. Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques. Cambridge University Press, Cambridge; New York, 1993. ISBN 978-0-521-35532-2. URL: https://www.cambridge.org/us/academic/subjects/physics/mathematical-methods/spectral-analysis-physical-applications.

  112. F. Perrin, O. Bertrand, and J. Pernier. Scalp Current Density Mapping: Value and Estimation from Potential Data. IEEE Transactions on Biomedical Engineering, BME-34(4):283–288, April 1987. doi:10.1109/TBME.1987.326089.

  113. François M. Perrin, Jacques Pernier, Olivier M. Bertrand, and Jean Franćois Echallier. Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2):184–187, 1989. doi:10.1016/0013-4694(89)90180-6.

  114. Gert Pfurtscheller and Fernando H. Lopes da Silva. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110(11):1842–1857, 1999. doi:10.1016/S1388-2457(99)00141-8.

  115. Dinh Tuan Pham. Joint approximate diagonalization of positive definite hermitian matrices. SIAM Journal on Matrix Analysis and Applications, 22(4):1136–1152, 2001. doi:10.1137/S089547980035689X.

  116. Luca Pollonini, Cristen Olds, Homer Abaya, Heather Bortfeld, Michael S Beauchamp, and John S Oghalai. Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy. Hearing research, 309:84–93, 2014.

  117. Gerard R. Ridgway, Vladimir Litvak, Guillaume Flandin, Karl J. Friston, and Will D. Penny. The problem of low variance voxels in statistical parametric mapping; a new hat avoids a ‘haircut’. NeuroImage, 59(3):2131–2141, 2012. doi:10.1016/j.neuroimage.2011.10.027.

  118. Bertrand Rivet, Hubert Cecotti, Antoine Souloumiac, Emmanuel Maby, and Jérémie Mattout. Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI. In Proceedings of EUSIPCO-2011, 1382–1386. Barcelona, 2011. IEEE. URL: https://ieeexplore.ieee.org/document/7073970.

  119. Bertrand Rivet, Antoine Souloumiac, Virginie Attina, and Guillaume Gibert. xDAWN algorithm to enhance evoked potentials: application to brain–computer interface. IEEE Transactions on Biomedical Engineering, 56(8):2035–2043, 2009. doi:10.1109/TBME.2009.2012869.

  120. Guillaume A. Rousselet. Does filtering preclude us from studying ERP time-courses? Frontiers in Psychology, 2012. doi:10.3389/fpsyg.2012.00131.

  121. Guillaume A. Rousselet. LIMO EEG dataset. 2016. doi:10.7488/ds/1556.

  122. Guillaume A. Rousselet, Carl M. Gaspar, Cyril R. Pernet, Jesse S. Husk, Patrick J. Bennett, and Allison B. Sekuler. Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task. Frontiers in Psychology, 1(19):1–14, 2010. doi:10.3389/fpsyg.2010.00019.

  123. Jukka Sarvas. Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine and Biology, 32(1):11–22, 1987. doi:10.1088/0031-9155/32/1/004.

  124. Abraham Savitzky and Marcel J. E. Golay. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8):1627–1639, 1964. doi:10.1021/ac60214a047.

  125. Gerwin Schalk, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R. Wolpaw. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6):1034–1043, 2004. doi:10.1109/TBME.2004.827072.

  126. Aaron Schurger, Sebastien Marti, and Stanislas Dehaene. Reducing multi-sensor data to a single time course that reveals experimental effects. BMC Neuroscience, 2013. doi:10.1186/1471-2202-14-122.

  127. Kensuke Sekihara and Srikantan S. Nagarajan. Adaptive Spatial Filters for Electromagnetic Brain Imaging. Series in Biomedical Engineering. Springer, Berlin; Heidelberg, 2008. ISBN 978-3-540-79369-4 978-3-540-79370-0. doi:10.1007/978-3-540-79370-0.

  128. Roger N. Shepard. Multidimensional scaling, tree-fitting, and clustering. Science, 210(4468):390–398, 1980. doi:10.1126/science.210.4468.390.

  129. David S. Slepian. Prolate spheroidal wave functions, fourier analysis, and uncertainty-V: the discrete case. Bell System Technical Journal, 57(5):1371–1430, 1978. doi:10.1002/j.1538-7305.1978.tb02104.x.

  130. Nathaniel J. Smith and Marta Kutas. Regression-based estimation of ERP waveforms: II. Nonlinear effects, overlap correction, and practical considerations: rERPS II. Psychophysiology, 52(2):169–181, 2015. doi:10.1111/psyp.12320.

  131. Stephen M. Smith and Thomas E. Nichols. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1):83–98, 2009. doi:10.1016/j.neuroimage.2008.03.061.

  132. Cornelis J. Stam, Guido Nolte, and Andreas Daffertshofer. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11):1178–1193, 2007. doi:10.1002/hbm.20346.

  133. R. G. Stockwell. Why use the S-transform? In Luigi Rodino, Bert-Wolfgang Schulze, and M. W. Wong, editors, Pseudo-Differential Operators: Partial Differential Equations and Time-Frequency Analysis, number 52 in Fields Institute Communications, pages 279–309. American Mathematical Society, Providence, RI, 2007. doi:10.1090/fic/052.

  134. Daniel Strohmeier, Yousra Bekhti, Jens Haueisen, and Alexandre Gramfort. The iterative reweighted mixed-norm estimate for spatio-temporal MEG/EEG source reconstruction. IEEE Transactions on Medical Imaging, 35(10):2218–2228, 2016. doi:10.1109/TMI.2016.2553445.

  135. Daniel Strohmeier, Jens Haueisen, and Alexandre Gramfort. Improved MEG/EEG source localization with reweighted mixed-norms. In Proceedings of PRNI-2014, 1–4. Tübingen, 2014. IEEE. doi:10.1109/PRNI.2014.6858545.

  136. Florent Ségonne, Anders M. Dale, Evelina Busa, Maureen Glessner, David Salat, Horst Karl Hahn, and Bruce R. Fischl. A hybrid approach to the skull stripping problem in MRI. NeuroImage, 22(3):1060–1075, 2004. doi:10.1016/j.neuroimage.2004.03.032.

  137. François Tadel, Sylvain Baillet, John C. Mosher, Dimitrios Pantazis, and Richard M. Leahy. Brainstorm: a user-friendly application for MEG/EEG analysis. Computational Intelligence and Neuroscience, 2011:1–13, 2011. doi:10.1155/2011/879716.

  138. Darren Tanner, James J.S. Norton, Kara Morgan-Short, and Steven J. Luck. On high-pass filter artifacts (they’re real) and baseline correction (it’s a good idea) in ERP/ERMF analysis. Journal of Neuroscience Methods, 266:166–170, 2016. doi:10.1016/j.jneumeth.2016.01.002.

  139. Darren Tanner, Kara Morgan-Short, and Steven J. Luck. How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition: high-pass filtering and artifactual ERP effects. Psychophysiology, 52(8):997–1009, 2015. doi:10.1111/psyp.12437.

  140. Samu Taulu and Matti Kajola. Presentation of electromagnetic multichannel data: the signal space separation method. Journal of Applied Physics, 97(12):124905, 2005. doi:10.1063/1.1935742.

  141. Samu Taulu and Juha Simola. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology, 51(7):1759–1768, 2006. doi:10.1088/0031-9155/51/7/008.

  142. Samu Taulu, Juha Simola, and Matti J. Kajola. Applications of the signal space separation method. IEEE Transactions on Signal Processing, 53(9):3359–3372, 2005. doi:10.1109/TSP.2005.853302.

  143. Claudia D. Tesche, Mikko A. Uusitalo, Risto J. Ilmoniemi, Minna Huotilainen, Matti J. Kajola, and Oili L. M. Salonen. Signal-space projections of MEG data characterize both distributed and well-localized neuronal sources. Electroencephalography and Clinical Neurophysiology, 95(3):189–200, 1995. doi:10.1016/0013-4694(95)00064-6.

  144. Frédéric E. Theunissen, Stephen V. David, Nandini C. Singh, Ann Hsu, William E. Vinje, and Jack L. Gallant. Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network: Computation in Neural Systems, 12(3):289–316, 2001. doi:10.1080/net.12.3.289.316.

  145. Michael E. Tipping and Christopher M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611–622, 1999. doi:10.1111/1467-9868.00196.

  146. Mikko A. Uusitalo and Risto J. Ilmoniemi. Signal-space projection method for separating MEG or EEG into components. Medical & Biological Engineering & Computing, 35(2):135–140, 1997. doi:10.1007/BF02534144.

  147. Barry D. Van Veen, Wim van Drongelen, Moshe Yuchtman, and Akifumi Suzuki. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on Biomedical Engineering, 44(9):867–880, 1997. doi:10.1109/10.623056.

  148. Rufin VanRullen. Four common conceptual fallacies in mapping the time course of recognition. Frontiers in Psychology, 2011. doi:10.3389/fpsyg.2011.00365.

  149. Martin Vinck, Robert Oostenveld, Marijn van Wingerden, Franscesco Battaglia, and Cyriel M.A. Pennartz. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage, 55(4):1548–1565, 2011. doi:10.1016/j.neuroimage.2011.01.055.

  150. Martin Vinck, Marijn van Wingerden, Thilo Womelsdorf, Pascal Fries, and Cyriel M.A. Pennartz. The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization. NeuroImage, 51(1):112–122, 2010. doi:10.1016/j.neuroimage.2010.01.073.

  151. Daniel T. Wehner, Matti S. Hämäläinen, Maria Mody, and Seppo P. Ahlfors. Head movements of children in MEG: quantification, effects on source estimation, and compensation. NeuroImage, 40(2):541–550, 2008. doi:10.1016/j.neuroimage.2007.12.026.

  152. Katherine L. Wheat, Piers L. Cornelissen, Stephen J. Frost, and Peter C. Hansen. During visual word recognition, phonology is accessed within 100 ms and may be mediated by a speech production code: evidence from magnetoencephalography. Journal of Neuroscience, 30(15):5229–5233, 2010. doi:10.1523/JNEUROSCI.4448-09.2010.

  153. Andreas Widmann and Erich Schröger. Filter effects and filter artifacts in the analysis of electrophysiological data. Frontiers in Psychology, 2012. doi:10.3389/fpsyg.2012.00233.

  154. Andreas Widmann, Erich Schröger, and Burkhard Maess. Digital filter design for electrophysiological data – a practical approach. Journal of Neuroscience Methods, 250:34–46, 2015. doi:10.1016/j.jneumeth.2014.08.002.

  155. Ben Willmore and Darragh Smyth. Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes. Network: Computation in Neural Systems, 14(3):553–577, 2003. doi:10.1088/0954-898X_14_3_309.

  156. Irene Winkler, Stefan Debener, Klaus-Robert Müller, and Michael Tangermann. On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. In Proceedings of EMBC-2015, 4101–4105. Milan, 2015. IEEE. doi:10.1109/EMBC.2015.7319296.

  157. David Wipf and Srikantan Nagarajan. A unified Bayesian framework for MEG/EEG source imaging. NeuroImage, 44(3):947–966, 2009. doi:10.1016/j.neuroimage.2008.02.059.

  158. David P. Wipf, Rey Ramírez, Jason Palmer, Scott Makeig, and Bhaskar D. Rao. Analysis of empirical bayesian methods for neuroelectromagnetic source localization. In Bernhard Schölkopf, John C. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, 1505–1512. MIT Press, 2007. URL: http://papers.nips.cc/paper/3089-analysis-of-empirical-bayesian-methods-for-neuroelectromagnetic-source-localization.pdf.

  159. D. Yao. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiological Measurement, 22(4):693–711, November 2001. doi:10.1088/0967-3334/22/4/305.

  160. Zhi Zhang. A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres. Physics in Medicine and Biology, 40(3):335–349, 1995. doi:10.1088/0031-9155/40/3/001.

  161. Moritz Grosse-Wentrup and Martin Buss. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Transactions on Biomedical Engineering, 55(8):1991–2000, 2008. doi:10.1109/TBME.2008.921154.

  162. Jair Montoya-Martínez, Jean-François Cardoso, and Alexandre Gramfort. Caveats with stochastic gradient and maximum likelihood based ICA for EEG. In Petr Tichavský, Massoud Babaie-Zadeh, Olivier J.J. Michel, and Nadège Thirion-Moreau, editors, Latent Variable Analysis and Signal Separation, number 10169 in Lecture Notes in Computer Science, pages 279–289. Springer International Publishing, Cham, 2017. doi:10.1007/978-3-319-53547-0_27.

  163. Roberto D. Pascual-Marqui. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and Findings in Experimental and Clinical Pharmacology, 24(D):5–12, 2002.

  164. Marijn van Vliet, Mia Liljeström, Susanna Aro, Riitta Salmelin, and Jan Kujala. Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python. bioRxiv, 2018. doi:10.1101/245530.