L. Breiman, Arcing classifier. The annals of statistics, pp.801-849, 1998.

R. Burduk, The AdaBoost Algorithm with the Imprecision Determine the Weights of the Observations, Lecture Notes in Computer Science, vol.8398, pp.110-116, 2014.
DOI : 10.1007/978-3-319-05458-2_12

M. Donnerer and A. Steed, Using a P300 Brain???Computer Interface in an Immersive Virtual Environment, Presence: Teleoperators and Virtual Environments, vol.6, issue.1, pp.12-24, 2010.
DOI : 10.1016/S1388-2457(02)00057-3

M. Fatourechi, A. Bashashati, R. K. Ward, and G. E. Birch, EMG and EOG artifacts in brain computer interface systems: A survey, Clinical Neurophysiology, vol.118, issue.3, pp.480-494, 2007.
DOI : 10.1016/j.clinph.2006.10.019

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting. The annals of statistics, pp.337-407, 2000.

C. Guger, G. Edlinger, W. Harkam, I. Niedermayer, and G. Pfurtscheller, How many people are able to operate an eeg-based brain-computer interface (bci)? IEEE transactions on neural systems and rehabilitation engineering, pp.145-147, 2003.

C. Guger, A. Schlogl, C. Neuper, D. Walterspacher, T. Strein et al., Rapid prototyping of an EEG-based brain-computer interface (BCI), IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.9, issue.1, pp.49-58, 2001.
DOI : 10.1109/7333.918276

C. Guger, A. Schlogl, D. Walterspacher, and G. Pfurtscheller, Design of an EEG-based Brain-Computer Interface (BCI) from Standard Components running in Real-time under Windows - Entwurf eines EEG-basierten Brain-Computer Interfaces (BCI) mit Standardkomponenten, das unter Windows in Echtzeit arbeitet, Biomedizinische Technik/Biomedical Engineering, vol.44, issue.1-2, pp.12-16, 1999.
DOI : 10.1007/BF02520010

I. Hayashi, S. Tsuruse, J. Suzuki, and R. T. Kozma, A proposal for applying pdi-Boosting to brain-computer interfaces, 2012 IEEE International Conference on Fuzzy Systems, pp.1-6, 2012.
DOI : 10.1109/FUZZ-IEEE.2012.6251152

H. H. Jasper, The ten twenty electrode system of the international federation, Electroencephalography and clinical neurophysiology, vol.10, pp.371-375, 1958.

S. Z. Li and Z. Zhang, FloatBoost learning and statistical face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1112-1123, 2004.
DOI : 10.1109/TPAMI.2004.68

Y. Liu, H. Zhang, Q. Zhao, and L. Zhang, Common spatial-spectral boosting pattern for brain-computer interface, Proceedings of the Twenty-first European Conference on Artificial Intelligence, pp.537-542, 2014.

G. R. Müller-putz, V. Kaiser, T. Solis-escalante, and G. Pfurtscheller, Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG, Medical & Biological Engineering & Computing, vol.111, issue.4, pp.229-233, 2010.
DOI : 10.1007/s11517-009-0572-7

G. Pfurtscheller, C. Brunner, A. Schlögl, F. L. Da, and . Silva, Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks, NeuroImage, vol.31, issue.1, pp.31153-159, 2006.
DOI : 10.1016/j.neuroimage.2005.12.003

G. Pfurtscheller, C. Neuper, A. Schlogl, and K. Lugger, Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters, IEEE Transactions on Rehabilitation Engineering, vol.6, issue.3, pp.316-325, 1998.
DOI : 10.1109/86.712230

P. Porwik, R. Doroz, and T. Orczyk, The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition, Pattern Analysis and Applications, vol.70, issue.1, pp.983-1001, 2015.
DOI : 10.1086/340392

P. Porwik, T. Orczyk, M. Lewandowski, and M. Cholewa, Feature projection k-NN classifier model for imbalanced and incomplete medical data, Biocybernetics and Biomedical Engineering, vol.36, issue.4, pp.644-656, 2016.
DOI : 10.1016/j.bbe.2016.08.002

S. J. Raudys and A. K. Jain, Small sample size effects in statistical pattern recognition: recommendations for practitioners, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.3, pp.252-264, 1991.
DOI : 10.1109/34.75512

Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby et al., OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain???Computer Interfaces in Real and Virtual Environments, Presence: Teleoperators and Virtual Environments, vol.2008, issue.3, pp.35-53, 2010.
DOI : 10.1016/j.patrec.2007.10.009

URL : https://hal.archives-ouvertes.fr/hal-00477153

R. E. Schapire, Using output codes to boost multiclass learning problems, ICML, pp.313-321, 1997.