M. Clerc, E. Daucé, J. Mattout, M. Clerc, L. Bougrain et al., Adaptive Methods in Machine Learning, Brain-Computer Interfaces, vol.8, issue.3-4, pp.209-232, 2016.
DOI : 10.1007/BF00992696
URL : https://hal.archives-ouvertes.fr/hal-01409016

M. Krauledat, M. Tangermann, B. Blankertz, and K. Müller, Towards Zero Training for Brain-Computer Interfacing, PLoS ONE, vol.12, issue.6, p.2967, 2008.
DOI : 10.1371/journal.pone.0002967.t002
URL : http://doi.org/10.1371/journal.pone.0002967

B. Reuderink, J. Farquhar, M. Poel, and A. Nijholt, A subject-independent brain-computer interface based on smoothed, second-order baselining, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.4600-4604, 2011.
DOI : 10.1109/IEMBS.2011.6091139

B. Blankertz, G. Dornhege, M. Krauledat, K. Müller, and G. Curio, The non-invasive Berlin Brain?Computer Interface: Fast acquisition of effective performance in untrained subjects, NeuroImage, vol.37, issue.2, pp.539-550, 2007.
DOI : 10.1016/j.neuroimage.2007.01.051

S. Fazli, M. Popescu, M. Danóczy, B. Blankertz, K. Müller et al., Subject-independent mental state classification in single trials, Neural Networks, vol.22, issue.9, pp.1305-1312, 2009.
DOI : 10.1016/j.neunet.2009.06.003

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

S. Lu, C. Guan, and H. Zhang, Unsupervised brain computer interface based on intersubject information and online adaptation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.17, issue.2, pp.135-145, 2009.

A. Barachant and M. Congedo, A plug&play p300 bci using information geometry. arXiv preprint, 2004.

N. Courty, R. Flamary, D. Tuia, and A. Rakotomamonjy, Optimal Transport for Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
DOI : 10.1109/TPAMI.2016.2615921
URL : https://hal.archives-ouvertes.fr/hal-01103073

R. Ma and T. Coleman, Generalizing the Posterior Matching Scheme to higher dimensions via optimal transportation, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp.96-102, 2011.
DOI : 10.1109/Allerton.2011.6120155

C. Villani, Optimal transport: old and new, 2008.
DOI : 10.1007/978-3-540-71050-9

L. Kantorovitch, On the Translocation of Masses, Management Science, vol.5, issue.1, pp.1-4, 1958.
DOI : 10.1287/mnsc.5.1.1

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, Proc. Advances in Neural Information Processing Systems, pp.2292-2300, 2013.

P. Knight, The Sinkhorn?Knopp Algorithm: Convergence and Applications, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.1, pp.261-275, 2008.
DOI : 10.1137/060659624
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.9066

B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface, IEEE Transactions on Biomedical Engineering, vol.56, issue.8, pp.2035-2043, 2009.
DOI : 10.1109/TBME.2009.2012869

M. Clerc, J. Mattout, E. Maby, D. Devlaminck, T. Papadopoulo et al., Verbal communication through brain computer interfaces, Proc. Interspeech-14th Annual Conference of the International Speech Communication Association, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00842851

L. Breiman, Bagging predictors. Machine learning, pp.123-140, 1996.
DOI : 10.1007/bf00058655

S. Sun, C. Zhang, and D. Zhang, An experimental evaluation of ensemble methods for EEG signal classification, Pattern Recognition Letters, vol.28, issue.15, pp.2157-2163, 2007.
DOI : 10.1016/j.patrec.2007.06.018

L. Van-der-maaten and G. Hinton, Visualizing data using t-SNE, Journal of Machine Learning Research, vol.9, pp.2579-2605, 2008.