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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. Phillip M. Alday. How much baseline correction do we need in ERP research? extended GLM model can replace baseline correction while lifting its limits. Psychophysiology, 2019. doi:10.1111/psyp.13451.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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/.

  9. H. Becker, L. Albera, P. Comon, J. -C. Nunes, R. Gribonval, J. Fleureau, P. Guillotel, and I. Merlet. SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity. NeuroImage, 157:157–172, August 2017. doi:10.1016/j.neuroimage.2017.05.046.

  10. Yousra Bekhti, Daniel Strohmeier, 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.

  11. 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.

  12. 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.

  13. 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.

  14. Quentin Bertrand and Mathurin Massias. Anderson acceleration of coordinate descent. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, 1288–1296. PMLR, 13–15 Apr 2021. URL: http://proceedings.mlr.press/v130/bertrand21a.html.

  15. Nima Bigdely-Shamlo, Kenneth Kreutz-Delgado, Kay Robbins, Makoto Miyakoshi, Marissa Westerfield, Tarik Bel-Bahar, Christian Kothe, Jessica Hsi, and Scott Makeig. Hierarchical event descriptor (HED) tags for analysis of event-related EEG studies. In 2013 IEEE Global Conference on Signal and Information Processing, 1–4. IEEE, 2013. doi:10.1109/GlobalSIP.2013.6736796.

  16. 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.

  17. 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.

  18. 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.

  19. Oleg Burdakov and Boris Merkulov. On a new norm for data fitting and optimization problems. Technical Report LiTH-MAT-R-2001-29, Linköping University, Linköping, 2001.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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.

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

  26. BIDS contributors. Brain imaging data structure — specification. URL: https://bids-specification.readthedocs.io/en/stable/ (visited on 12-October-2020).

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

  28. R. J. Croft and R. J. Barry. Removal of ocular artifact from the EEG: a review. Clinical Neurophysiology, 30(1):5–19, 2000. doi:10.1016/S0987-7053(00)00055-1.

  29. 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.

  30. 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.

  31. 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.

  32. 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.

  33. 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.

  34. 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.

  35. 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.

  36. Charles-Alban Deledalle, Samuel Vaiter, Jalal Fadili, and Gabriel Peyré. Stein unbiased gradient estimator of the risk (sugar) for multiple parameter selection. SIAM Journal on Imaging Sciences, 7(4):2448–2487, 2014. doi:10.1137/140968045.

  37. Arnaud Delorme and Scott Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1):9–21, March 2004. doi:10.1016/j.jneumeth.2003.10.009.

  38. 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.

  39. Dhani Dharmaprani, Hoang K. Nguyen, Trent W. Lewis, Dylan DeLosAngeles, John O. Willoughby, and Kenneth J. Pope. A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 825–828. Orlando, FL, USA, 2016. IEEE. doi:10.1109/EMBC.2016.7590828.

  40. 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.

  41. 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.

  42. 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/.

  43. 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.

  44. 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.

  45. 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.

  46. 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.

  47. 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.

  48. Christopher R. Genovese, Nicole A. Lazar, and Thomas Nichols. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15(4):870–878, 2002. doi:https://doi.org/10.1006/nimg.2001.1037.

  49. 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.

  50. 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.

  51. 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.

  52. 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.

  53. 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.

  54. 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.

  55. Alexandre Gramfort, Renaud Keriven, and Maureen Clerc. Graph-based variability estimation in single-trial event-related neural responses. IEEE Transactions on Biomedical Engineering, 57(5):1051–1061, 2010. doi:10.1109/tbme.2009.2037139.

  56. 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.

  57. Alexandre Gramfort, Martin Luessi, Eric Larson, Denis A. Engemann, Daniel Strohmeier, Christian Brodbeck, Roman Goj, Mainak Jas, Teon Brooks, Lauri Parkkonen, and Matti S. Hämäläinen. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7(267):1–13, 2013. doi:10.3389/fnins.2013.00267.

  58. Alexandre Gramfort, Martin Luessi, Eric Larson, Denis A. Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, and Matti S. Hämäläinen. MNE software for processing MEG and EEG data. NeuroImage, 86:446–460, 2014. doi:10.1016/j.neuroimage.2013.10.027.

  59. 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.

  60. 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.

  61. 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, 1983. doi:10.1016/0013-4694(83)90135-9.

  62. Lawrence L. Greischar, Cory A. Burghy, Carien M. van Reekum, Daren C. Jackson, Diego A. Pizzagalli, Corrina Mueller, and Richard J. Davidson. Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording. Clinical Neurophysiology, 115(3):710–720, March 2004. doi:10.1016/j.clinph.2003.10.028.

  63. 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.

  64. 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.

  65. Liberty S. Hamilton, David L. Chang, Morgan B. Lee, and Edward F. Chang. Semi-automated anatomical labeling and inter-subject warping of high-density intracranial recording electrodes in electrocorticography. Frontiers in Neuroinformatics, October 2017. doi:10.3389/fninf.2017.00062.

  66. 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.

  67. 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.

  68. Stefan Haufe, Sven Dähne, and Vadim V Nikulin. Dimensionality reduction for the analysis of brain oscillations. NeuroImage, 101:583–597, 2014. doi:https://doi.org/10.1016/j.neuroimage.2014.06.073.

  69. 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.

  70. 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.

  71. Olaf Hauk, Matti Stenroos, and Matthias Treder. Towards an objective evaluation of EEG/MEG source estimation methods: the linear tool kit. bioRxiv, 2019. doi:10.1101/672956.

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  75. 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.

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  85. 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.

  86. Emily S. Kappenman, Jaclyn L. Farrens, Wendy Zhang, Andrew X. Stewart, and Steven J. Luck. ERP CORE: an open resource for human event-related potential research. NeuroImage, 225:117465, 2021. doi:10.1016/j.neuroimage.2020.117465.

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  89. 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.

  90. 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.

  91. 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.

  92. 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.

  93. 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.

  94. 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.

  95. 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.

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