K. J. Gorgolewski and R. Poldrack, A practical guide for improving transparency and reproducibility in neuroimaging research, pp.2016-039354

D. Van-essen, K. Ugurbil, E. Auerbach, D. Barch, T. Behrens et al., The Human Connectome Project: A data acquisition perspective, NeuroImage, vol.62, issue.4, pp.2222-2231, 2012.
DOI : 10.1016/j.neuroimage.2012.02.018

L. Schoffelen, F. Marzetti, F. D. De-pasquale, and . Pompeo, Adding dynamics to the Human Connectome Project with MEG, NeuroImage, vol.80, pp.190-201, 2013.

A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, and P. Ivanov, PhysioBank, PhysioToolkit, and PhysioNet : Components of a New Research Resource for Complex Physiologic Signals, Circulation, vol.101, issue.23, pp.215-220, 2000.
DOI : 10.1161/01.CIR.101.23.e215

G. Niso, C. Rogers, J. T. Moreau, L. Chen, C. Madjar et al., OMEGA: The Open MEG Archive, OMEGA: the open MEG archive, pp.1182-1187, 2016.
DOI : 10.1016/j.neuroimage.2015.04.028

J. R. Taylor, N. Williams, R. Cusack, T. Auer, M. A. Shafto et al., 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, vol.144, 2015.
DOI : 10.1016/j.neuroimage.2015.09.018

K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das et al., The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments, p.34561, 2016.
DOI : 10.1101/034561

N. Bigdely-shamlo, K. Kreutz-delgado, K. Robbins, M. Miyakoshi, M. Westerfield et al., Hierarchical Event Descriptor (HED) tags for analysis of event-related EEG studies, 2013 IEEE Global Conference on Signal and Information Processing, pp.1-4
DOI : 10.1109/GlobalSIP.2013.6736796

D. A. Engemann and A. Gramfort, Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, NeuroImage, vol.108, pp.328-342, 2015.
DOI : 10.1016/j.neuroimage.2014.12.040

D. Cruse, S. Chennu, C. Chatelle, T. A. Bekinschtein, D. Fernández-espejo et al., Bedside detection of awareness in the vegetative state: a cohort study, The Lancet, vol.378, issue.9809, pp.378-2088, 2012.
DOI : 10.1016/S0140-6736(11)61224-5

A. M. Goldfine, J. C. Bardin, Q. Noirhomme, J. J. Fins, N. D. Schiff et al., Reanalysis of ???Bedside detection of awareness in the vegetative state: a cohort study???, The Lancet, vol.381, issue.9863, 2013.
DOI : 10.1016/S0140-6736(13)60125-7

A. Basirat, S. Dehaene, and G. , A hierarchy of cortical responses to sequence violations in three-month-old infants, Cognition, vol.132, issue.2, pp.137-150, 2014.
DOI : 10.1016/j.cognition.2014.03.013

S. Taulu, M. Kajola, and J. Simola, Suppression of Interference and Artifacts by the Signal Space Separation Method, Brain Topography, vol.16, issue.4, pp.269-275, 2004.
DOI : 10.1023/B:BRAT.0000032864.93890.f9

A. Widmann, E. Schröger, and B. Maess, Digital filter design for electrophysiological data ??? a practical approach, Journal of Neuroscience Methods, vol.250, pp.34-46, 2015.
DOI : 10.1016/j.jneumeth.2014.08.002

F. Tadel, S. Baillet, J. C. Mosher, D. Pantazis, and R. M. Leahy, Brainstorm: A User-Friendly Application for MEG/EEG Analysis, Computational Intelligence and Neuroscience, vol.57, issue.1, p.2011, 2011.
DOI : 10.1002/hbm.20781
URL : http://doi.org/10.1155/2011/879716

A. Delorme and S. Makeig, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, Journal of Neuroscience Methods, vol.134, issue.1, pp.9-21, 2004.
DOI : 10.1016/j.jneumeth.2003.10.009

R. Oostenveld, P. Fries, E. Maris, and J. Schoffelen, FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, Computational Intelligence and Neuroscience, vol.36, issue.3, p.2011, 2011.
DOI : 10.1016/j.neuroimage.2009.02.041

A. Gramfort, M. Luessi, E. Larson, D. Engemann, and D. Strohmeier, MEG and EEG data analysis with MNE-Python, Frontiers in Neuroscience, vol.7, 2013.
DOI : 10.3389/fnins.2013.00267
URL : http://doi.org/10.3389/fnins.2013.00267

V. Litvak, J. Mattout, S. Kiebel, C. Phillips, R. Henson et al., EEG and MEG Data Analysis in SPM8, Computational Intelligence and Neuroscience, vol.19, issue.2, p.2011, 2011.
DOI : 10.1073/pnas.98.2.694
URL : http://doi.org/10.1155/2011/852961

H. Nolan, R. Whelan, and R. Reilly, FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection, Journal of Neuroscience Methods, vol.192, issue.1, pp.152-162, 2010.
DOI : 10.1016/j.jneumeth.2010.07.015
URL : http://www.tara.tcd.ie/bitstream/2262/41103/1/Faster%20-%20Fully%20Automated%20Statistical%20Thresholding.pdf

N. Bigdely-shamlo, T. Mullen, C. Kothe, K. Su, and K. Robbins, The PREP pipeline: standardized preprocessing for large-scale EEG analysis, Frontiers in Neuroinformatics, vol.78, 2015.
DOI : 10.3389/fninf.2015.00016
URL : http://doi.org/10.3389/fninf.2015.00016

A. Barachant, A. Andreev, and M. Congedo, The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry, pp.19-20
URL : https://hal.archives-ouvertes.fr/hal-00781701

J. Diedrichsen and R. Shadmehr, Detecting and adjusting for artifacts in fMRI time series data, NeuroImage, vol.27, issue.3, pp.624-634, 2005.
DOI : 10.1016/j.neuroimage.2005.04.039
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479857

A. De-cheveigné and J. Simon, Sensor noise suppression, Journal of Neuroscience Methods, vol.168, issue.1, pp.195-202, 2008.
DOI : 10.1016/j.jneumeth.2007.09.012

J. Snoek, H. Larochelle, and R. P. Adams, Practical bayesian optimization of machine learning algorithms, Advances in Neural Information Processing Systems, pp.2951-2959

J. S. Bergstra, R. Bardenet, Y. Bengio, and B. , Algorithms for hyper-parameter optimization, Advances in Neural Information Processing Systems, pp.2546-2554
URL : https://hal.archives-ouvertes.fr/hal-00642998

D. Wakeman and R. Henson, A multi-subject, multi-modal human neuroimaging dataset, Scientific Data, vol.2, 2015.
DOI : 10.1088/0031-9155/48/22/002
URL : http://doi.org/10.1038/sdata.2015.1

G. Schalk, D. J. Mcfarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, BCI2000: A General-Purpose Brain-Computer Interface (BCI) System, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.1034-1043, 2004.
DOI : 10.1109/TBME.2004.827072
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.7600

M. Jas, D. Engemann, F. Raimondo, Y. Bekhti, and A. Gramfort, Automated rejection and repair of bad trials in, 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI)
URL : https://hal.archives-ouvertes.fr/hal-01313458

D. Leung, Cross-validation in nonparametric regression with outliers, The Annals of Statistics, vol.33, issue.5, pp.2291-2310, 2005.
DOI : 10.1214/009053605000000499
URL : http://arxiv.org/abs/math/0602292

J. De-brabanter, K. Pelckmans, J. Suykens, J. Vandewalle, B. De et al., Robust cross-validation score functions with application to weighted least squares support vector machine function estimation, 2003.

A. Gramfort, M. Luessi, E. Larson, D. Engemann, and D. Strohmeier, MNE software for processing MEG and EEG data, NeuroImage, vol.86, pp.446-460, 2014.
DOI : 10.1016/j.neuroimage.2013.10.027
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930851

F. Perrin, J. Pernier, O. Bertrand, and J. Echallier, Spherical splines for scalp potential and current density mapping, Electroencephalography and Clinical Neurophysiology, vol.72, issue.2, pp.184-187, 1989.
DOI : 10.1016/0013-4694(89)90180-6

M. Hämäläinen and R. Ilmoniemi, Interpreting magnetic fields of the brain: minimum norm estimates, Medical & Biological Engineering & Computing, vol.22, issue.1, pp.35-42, 1994.
DOI : 10.1109/T-ED.1984.21877

C. Hsu, C. Chang, and C. Lin, A practical guide to support vector classification, 2003.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, pp.1097-1105
DOI : 10.1162/neco.2009.10-08-881

C. E. Rasmussen and C. K. Williams, Gaussian Processes in Machine Learning, pp.715-719, 2006.
DOI : 10.1162/089976602317250933
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.7289

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. A. Fischler and R. C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.
DOI : 10.1145/358669.358692

R. Vigário, J. Sarela, V. Jousmiki, M. Hamalainen, and E. Oja, Independent component approach to the analysis of EEG and MEG recordings, IEEE Transactions on Biomedical Engineering, vol.47, issue.5, pp.589-593, 2000.
DOI : 10.1109/10.841330

J. Dammers, M. Schiek, F. Boers, C. Silex, M. Zvyagintsev et al., Integration of Amplitude and Phase Statistics for Complete Artifact Removal in Independent Components of Neuromagnetic Recordings, IEEE Transactions on Biomedical Engineering, vol.55, issue.10, pp.2353-2362, 2008.
DOI : 10.1109/TBME.2008.926677

M. Junghöfer, T. Elbert, D. M. Tucker, and B. Rockstroh, Statistical control of artifacts in dense array EEG/MEG studies, Psychophysiology, vol.37, issue.4, pp.523-532, 2000.
DOI : 10.1111/1469-8986.3740523

D. Engemann, F. Raimondo, J. King, M. Jas, A. Gramfort et al., Automated measurement and prediction of consciousness in vegetative state and minimally conscious patients, Machine Learning and Neuroscience at the International Conference on Machine Learning (ICML)

R. Vigário, Extraction of ocular artefacts from EEG using independent component analysis, Electroencephalography and Clinical Neurophysiology, vol.103, issue.3, pp.395-404, 1997.
DOI : 10.1016/S0013-4694(97)00042-8

M. Uusitalo and R. Ilmoniemi, Signal-space projection method for separating MEG or EEG into components, Medical & Biological Engineering & Computing, vol.7, issue.Suppl. A, pp.135-140, 1997.
DOI : 10.1007/BF02534144

L. Parkkonen, Instrumentation and data preprocessing MEG: an introduction to methods, 2010.
DOI : 10.1093/acprof:oso/9780195307238.003.0002

S. S. Dalal, A. G. Guggisberg, E. Edwards, K. Sekihara, A. M. Findlay et al., Five-dimensional neuroimaging: Localization of the time???frequency dynamics of cortical activity, NeuroImage, vol.40, issue.4, pp.1686-1700, 2008.
DOI : 10.1016/j.neuroimage.2008.01.023

J. Groß, J. Kujala, M. Hämäläinen, L. Timmermann, A. Schnitzler et al., Dynamic imaging of coherent sources: Studying neural interactions in the human brain, Proceedings of the National Academy of Sciences, vol.1, issue.8, pp.694-699, 2001.
DOI : 10.1088/0031-9155/44/8/317

K. Uutela, M. Hämäläinen, and E. Somersalo, Visualization of Magnetoencephalographic Data Using Minimum Current Estimates, NeuroImage, vol.10, issue.2, pp.173-180, 1999.
DOI : 10.1006/nimg.1999.0454

A. M. Dale, A. K. Liu, B. R. Fischl, R. L. Buckner, J. W. Belliveau et al., Dynamic Statistical Parametric Mapping, Neuron, vol.26, issue.1, pp.55-67, 2000.
DOI : 10.1016/S0896-6273(00)81138-1
URL : http://doi.org/10.1016/s0896-6273(00)81138-1

R. D. Pascual-marqui, Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, Methods Find Exp Clin Pharmacol, vol.24, pp.5-12, 2002.
DOI : 10.1016/s0013-4694(97)88020-4

M. Woolrich, L. Hunt, A. Groves, and G. Barnes, MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization, NeuroImage, vol.57, issue.4, pp.1466-1479, 2011.
DOI : 10.1016/j.neuroimage.2011.04.041
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894461