A. Bashashati, M. Fatourechi, R. K. Ward, and G. E. Birch, A survey of signal processing algorithms in brain???computer interfaces based on electrical brain signals, Journal of Neural Engineering, vol.4, issue.2, pp.32-57, 2007.
DOI : 10.1088/1741-2560/4/2/R03

R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, 1973.

M. Girolami and S. Rogers, Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors, Neural Computation, vol.6, issue.8, pp.1790-1817, 2006.
DOI : 10.1109/34.735807

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.7451

M. Kuss and C. E. Rasmussen, Assessing approximate inference for binary gaussian process classification, Journal of Machine Learning Research, vol.6, pp.1679-1704, 2005.

R. Leeb, R. Scherer, F. Lee, H. Bischof, and G. Pfurtscheller, Navigation in Virtual Environments through Motor Imagery, 9th Computer Vision Winter Workshop, pp.99-108, 2004.

F. Lotte, The use of Fuzzy Inference Systems for classification in EEG-based Brain- Computer Interfaces, Proceedings of the third international Brain-Computer Interface workshop and training course, pp.12-13, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00134951

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, A review of classification algorithms for EEG-based brain???computer interfaces, Journal of Neural Engineering, vol.4, issue.2, pp.1-13, 2007.
DOI : 10.1088/1741-2560/4/2/R01

URL : https://hal.archives-ouvertes.fr/inria-00134950

F. Lotte, A. Lécuyer, F. Lamarche, and B. Arnaldi, Studying the use of fuzzy inference systems for motor imagery classification, IEEE Transactions on Neural System and Rehabilitation Engineering, vol.15, issue.2, pp.322-324, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00134958

D. J. Mackay, Information Theory, Inference, and Learning Algorithms, 2003.

T. Minka, A family of algorithm for approximate Bayessian inference, 2001.

K. R. Müller, M. Krauledat, G. Dornhege, G. Curio, and B. Blankertz, Machine learning techniques for brain-computer interfaces, Biomedical Engineering, vol.49, issue.1, pp.11-22, 2004.

R. Neal, Regression and classification using gaussian process priors, Bayessian Statistics 6, pp.475-501, 1998.

G. Pfurtscheller and C. Neuper, Motor Imagery and Direct Brain-Computer Communication . proceedings of the IEEE, pp.1123-1134, 2001.
DOI : 10.1109/5.939829

J. C. Platt, Probabilities for Support Vector Machines Advances in Large Margin Classifiers, pp.61-74, 1999.

M. E. Tipping, Sparse Bayesian learning and the relevance vector machine, Journal of Machine Learning Research, vol.1, pp.211-244, 2001.

G. Townsend, B. Graimann, and G. Pfurtscheller, Continuous EEG Classification During Motor Imagery???Simulation of an Asynchronous BCI, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.12, issue.2, pp.258-265, 2004.
DOI : 10.1109/TNSRE.2004.827220

C. Vidaurre, A. Schlogl, R. Cabeza, and G. Pfurtscheller, A fully on-line adaptive Brain Computer Interface, Biomed. Tech. Band, Special issue, vol.49, pp.760-761, 2004.

C. K. Williams and D. Barber, Bayesian classification with Gaussian processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.12, pp.1342-1352, 1998.
DOI : 10.1109/34.735807

J. R. Wolpaw, N. Birbaumer, D. J. Mcfarland, G. Pfurtscheller, and T. M. Vaughan, Brain???computer interfaces for communication and control, Clinical Neurophysiology, vol.113, issue.6, pp.767-791, 2002.
DOI : 10.1016/S1388-2457(02)00057-3