K. Friston, L. Harrison, J. Daunizeau, S. Kiebel, C. Phillips et al., 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

D. Wipf and S. Nagarajan, A unified Bayesian framework for MEG/EEG source imaging, NeuroImage, vol.44, issue.3, pp.947-966, 2009.
DOI : 10.1016/j.neuroimage.2008.02.059

W. Ou, M. Hämaläinen, and P. Golland, 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
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730457

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, pp.1937-1961, 2012.
DOI : 10.1088/0031-9155/57/7/1937
URL : https://hal.archives-ouvertes.fr/hal-00690774

A. Gramfort, D. Strohmeier, J. Haueisen, M. S. Hämäläinen, and M. Kowalski, 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

E. J. Candès, M. B. Wakin, and S. P. Boyd, Enhancing sparsity by reweighted l1 minimization, J. Fourier Anal. Appl, vol.14, pp.5-6, 2008.

I. Daubechies, R. Devore, M. Fornasier, and C. S. Güntürk, 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
URL : http://arxiv.org/abs/0807.0575

R. Chartrand and W. Yin, Iteratively reweighted algorithms for compressive sensing, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3869-3872, 2008.
DOI : 10.1109/ICASSP.2008.4518498
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.8360

I. F. Gorodnitsky and B. D. Rao, Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm, IEEE Transactions on Signal Processing, vol.45, issue.3, pp.600-616, 1997.
DOI : 10.1109/78.558475

A. Rakotomamonjy, Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms, Signal Processing, vol.91, issue.7, pp.1505-1526, 2011.
DOI : 10.1016/j.sigpro.2011.01.012
URL : https://hal.archives-ouvertes.fr/hal-00328185

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

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

P. Tseng, Approximation accuracy, gradient methods, and error bound for structured convex optimization, Mathematical Programming, vol.68, issue.12, pp.263-295, 2010.
DOI : 10.1007/s10107-010-0394-2

R. Jenatton, J. Mairal, G. Obozinski, and F. Bach, Proximal methods for hierarchical sparse coding, J. Mach. Learn. Res, vol.12, pp.2297-2334, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00516723

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010.
DOI : 10.18637/jss.v033.i01

M. Vincent and N. R. Hansen, Sparse group lasso and high dimensional multinomial classification, Computational Statistics & Data Analysis, vol.71, pp.771-786, 2014.
DOI : 10.1016/j.csda.2013.06.004

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

M. Weisend, F. Hanlon, R. Montaño, S. Ahlfors, A. Leuthold et al., Paving the way for cross-site pooling of magnetoencephalography (MEG) data, International Congress Series, vol.1300, pp.615-618, 2007.
DOI : 10.1016/j.ics.2006.12.095