A. Argyriou, R. Hauser, C. A. Micchelli, and M. Pontil, A DC-programming algorithm for kernel selection, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.41-48, 2006.
DOI : 10.1145/1143844.1143850

A. Argyriou, T. Evgeniou, and M. Pontil, Convex multi-task feature learning, Machine Learning, pp.243-272, 2008.

F. Bach, Exploring large feature spaces with hierarchical multiple kernel learning, Advances in Neural Information Processing Systems 21, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00319660

F. R. Bach, G. R. Lanckriet, and M. I. Jordan, Multiple kernel learning, conic duality, and the SMO algorithm, Twenty-first international conference on Machine learning , ICML '04, pp.41-48, 2004.
DOI : 10.1145/1015330.1015424

Y. Bengio and Y. Grandvalet, No unbiased estimator of the variance of K-fold crossvalidation, Journal of Machine Learning Research (JMLR), vol.5, pp.1089-1105, 2004.

B. Blankertz, K. Müller, G. Curio, T. M. Vaughan, G. Schalk et al., The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.511044-1051, 2004.
DOI : 10.1109/TBME.2004.826692

O. Bousquet and A. Elisseeff, Stability and generalization, Journal of Machine Learning Research, vol.2, pp.499-526, 2002.

O. Bousquet and D. J. Herrmann, On the complexity of learning the kernel matrix, Advances in Neural Information Processing Systems 15, pp.399-406, 2003.

L. Breiman, Heuristics of instability and stabilization in model selection, The Annals of Statistics, vol.24, issue.6, pp.2350-2383, 1996.
DOI : 10.1214/aos/1032181158

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, Choosing multiple parameters for support vector machines, Machine Learning, pp.131-159, 2002.

N. Cristianini, C. Campbell, and J. Shawe-taylor, Dynamically adapting kernels in support vector machines, Advances in Neural Information Processing Systems 11, pp.204-210, 1999.

N. Cristianini, J. Shawe-taylor, A. Elisseeff, and K. Kandola, On Kernel Target Alignment, Advances in Neural Information Processing Systems 14, pp.367-373, 2002.
DOI : 10.1007/3-540-33486-6_8

A. Farwell and E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials, Electroencephalography and Clinical Neurophysiology, vol.70, issue.6, pp.510-523, 1998.
DOI : 10.1016/0013-4694(88)90149-6

G. Garipelli, R. Chavarriaga, J. Del, and R. Millán, Fast recognition of anticipation related potentials, IEEE Transactions on Biomedical Engineering

Y. Grandvalet and S. Canu, Adaptive scaling for feature selection in SVMs, Advances in Neural Information Processing Systems 15, pp.569-576, 2003.

Y. Grandvalet and S. Canu, Outcomes of the equivalence of adaptive ridge with least absolute shrinkage, Advances in Neural Information Processing Systems 11 (NIPS 1998), pp.445-451, 1999.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

M. Kowalski and B. Torrésani, Sparsity and persistence: mixed norms provide simple signals models with dependent coefficients. Signal, Image and Video Processing, pp.1863-1703, 2008.

G. R. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. I. Jordan, Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, vol.5, pp.27-72, 2004.

M. Nikolova, Local Strong Homogeneity of a Regularized Estimator, SIAM Journal on Applied Mathematics, vol.61, issue.2, pp.633-658, 2000.
DOI : 10.1137/S0036139997327794

C. S. Ong, A. J. Smola, and R. C. Williamson, Learning the kernel with hyperkernels, Journal of Machine Learning Research, vol.6, pp.1043-1071, 2005.

A. Rakotomamonjy and V. Guigue, BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller, IEEE Transactions on Biomedical Engineering, vol.55, issue.3, pp.1147-1154, 2008.
DOI : 10.1109/TBME.2008.915728

URL : https://hal.archives-ouvertes.fr/hal-00439462

B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001.

M. Schröder, T. N. Lal, T. Hinterberger, M. Bogdan, J. Hill et al., Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces, EURASIP Journal on Advances in Signal Processing, vol.2005, issue.19, pp.3103-3112, 2005.
DOI : 10.1155/ASP.2005.3103

S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, Large scale Multiple Kernel Learning, Journal of Machine Learning Research, vol.7, pp.1531-1565, 2006.

N. Srebro and S. Ben-david, Learning Bounds for Support Vector Machines with Learned Kernels, 19th Annual Conference on Learning Theory, pp.169-183, 2006.
DOI : 10.1007/11776420_15

M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy, Composite kernel learning, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1040-1047, 2008.
DOI : 10.1145/1390156.1390287

URL : https://hal.archives-ouvertes.fr/hal-00316016

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B, vol.58, issue.1, pp.267-288, 1996.

V. N. Vapnik, The nature of statistical learning theory, 1995.

W. G. Walter, R. Cooper, V. J. Aldridge, W. C. Mccallum, and A. L. Winter, Contingent Negative Variation : An Electric Sign of Sensori-Motor Association and Expectancy in the Human Brain, Nature, vol.15, issue.4943, pp.380-384, 1964.
DOI : 10.1038/203380a0

J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio et al., Feature selection for SVMs, Advances in Neural Information Processing Systems 13, pp.668-674, 2001.

Z. Xu, R. Jin, I. King, and M. Lyu, An extended level method for efficient multiple kernel learning, Advances in Neural Information Processing Systems 21, pp.1825-1832, 2009.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356

P. Zhao, G. Rocha, and B. Yu, The composite absolute penalties family for grouped and hierarchical variable selection, The Annals of Statistics, vol.37, issue.6A
DOI : 10.1214/07-AOS584