K. Friston, Multiple sparse priors for the M/EEG inverse problem, NeuroImage, vol.39, issue.3, pp.1104-1120, 2008.
DOI : 10.1016/j.neuroimage.2007.09.048

K. Sekihara and S. S. Nagarajan, A Unified Bayesian Framework for MEG/EEG Source Imaging, Electromagnetic Brain Imaging, pp.119-137, 2015.
DOI : 10.1007/978-3-319-14947-9_6

A. Bolstad, B. Van-veen, and R. Nowak, Space???time event sparse penalization for magneto-/electroencephalography, NeuroImage, vol.46, issue.4, pp.1066-1081, 2009.
DOI : 10.1016/j.neuroimage.2009.01.056

F. Lucka, S. Pursiainen, M. Burger, and C. H. Wolters, Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents, NeuroImage, vol.61, issue.4, pp.1364-1382, 2012.
DOI : 10.1016/j.neuroimage.2012.04.017

W. Ou, A distributed spatio-temporal EEG/MEG inverse solver, NeuroImage, vol.44, issue.3, pp.932-946, 2009.
DOI : 10.1016/j.neuroimage.2008.05.063

A. Gramfort, M. Kowalski, and M. Hämäläinen, Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Physics in Medicine and Biology, vol.57, issue.7, 1937.
DOI : 10.1088/0031-9155/57/7/1937

URL : https://hal.archives-ouvertes.fr/hal-00690774

S. Haufe, V. V. Nikulin, A. Ziehe, K. Müller, and G. Nolte, Combining sparsity and rotational invariance in EEG/MEG source reconstruction, NeuroImage, vol.42, issue.2, pp.726-738, 2008.
DOI : 10.1016/j.neuroimage.2008.04.246

A. Gramfort, Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations, NeuroImage, vol.70, pp.410-422, 2013.
DOI : 10.1016/j.neuroimage.2012.12.051

URL : https://hal.archives-ouvertes.fr/hal-00773276

S. Castaño-candamil, J. Höhne, J. Martínez-vargas, X. An, G. Castellanos-domínguez et al., Solving the EEG inverse problem based on space???time???frequency structured sparsity constraints, NeuroImage, vol.118, pp.598-612, 2015.
DOI : 10.1016/j.neuroimage.2015.05.052

D. Strohmeier, A. Gramfort, and J. Haueisen, MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain, 2015 International Workshop on Pattern Recognition in NeuroImaging, pp.21-24, 2015.
DOI : 10.1109/PRNI.2015.14

URL : https://hal.archives-ouvertes.fr/hal-01183552

E. J. Candes, M. B. Wakin, and S. P. Boyd, Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.5-6, 2008.
DOI : 10.1007/s00041-008-9045-x

I. Daubechies, Iteratively reweighted least squares minimization for sparse recovery, Communications on Pure and Applied Mathematics, vol.58, issue.1, pp.1-38, 2010.
DOI : 10.1002/cpa.20303

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, 2010.
DOI : 10.18637/jss.v033.i01

D. Engemann, D. Strohmeier, E. Larson, and A. Gramfort, Mind the Noise Covariance When Localizing Brain Sources with M/EEG, 2015 International Workshop on Pattern Recognition in NeuroImaging, pp.9-12, 2015.
DOI : 10.1109/PRNI.2015.25

URL : https://hal.archives-ouvertes.fr/hal-01183551

P. Tseng, Approximation accuracy, gradient methods, and error bound for structured convex optimization, Mathematical Programming, pp.263-295, 2010.
DOI : 10.1007/s10107-010-0394-2

J. Montoya-martínez, A. Artés-rodríguez, M. Pontil, and L. K. Hansen, A regularized matrix factorization approach to induce structured sparselow-rank solutions in the EEG inverse problem, EURASIP Journal on Advances in Signal Processing, vol.2014, issue.1, pp.1-13, 2014.

M. Weisend, Paving the way for cross-site pooling of magnetoencephalography (MEG) data, International Congress Series, pp.615-618, 2007.
DOI : 10.1016/j.ics.2006.12.095

D. Strohmeier, J. Haueisen, and A. Gramfort, Improved MEG/EEG source localization with reweighted mixed-norms, 2014 International Workshop on Pattern Recognition in Neuroimaging, pp.1-4, 2014.
DOI : 10.1109/PRNI.2014.6858545

URL : https://hal.archives-ouvertes.fr/hal-01044748

A. M. Dale, Dynamic Statistical Parametric Mapping, Neuron, vol.26, issue.1, pp.55-67, 2000.
DOI : 10.1016/S0896-6273(00)81138-1

A. Gramfort, MNE software for processing MEG and EEG data, NeuroImage, vol.86, pp.446-460, 2014.
DOI : 10.1016/j.neuroimage.2013.10.027