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

Y. Wang, G. Shangkai, and G. Xiaorong, Common Spatial Pattern Method for Channel Selection in Motor Imagery Based Brain-Computer Interface, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005.

E. Oja and Z. Yuan, The FastICA Algorithm Revisited: Convergence Analysis, IEEE Transactions on Neural Networks, vol.17, issue.6, pp.1370-1381, 2006.
DOI : 10.1109/TNN.2006.880980

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.622.1413

P. Augustyniak, Adaptive Wavelet Discrimination of Muscular Noise in the ECG, Proc. Computers in Cardiology, vol.33, pp.481-484, 2006.

R. Yang, A. Song, and B. Xu, FEATURE EXTRACTION OF MOTOR IMAGERY EEG BASED ON WAVELET TRANSFORM AND HIGHER-ORDER STATISTICS, International Journal of Wavelets, Multiresolution and Information Processing, vol.10, issue.03, pp.373-384, 2010.
DOI : 10.4236/jbise.2008.11010

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

I. Koprinska, Feature Selection for Brain-Computer Interfaces, LNAI, vol.5669, pp.100-111, 2009.
DOI : 10.1007/978-3-642-14640-4_8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.169.3755

I. Rejer, Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand, Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, Advances in Intelligent Systems and Computing, 2013.
DOI : 10.1007/978-3-319-00969-8_57

R. Burduk, Recognition Task with Feature Selection and Weighted Majority Voting Based on Interval-Valued Fuzzy Sets in Computational Collective Intelligence. Technologies and Izabela Rejer and Pawe? Górski Applications, 2012.

G. Pfurtscheller, . Neuper-ch, A. Schlögl, and K. Lugger, Separability of EEG Singals Recorded During Right and Left Motor Imagery Using Adaptive Autoregressive Parameters, IEEE Trans. on Rehabilitation Engineering, vol.6, issue.3, 1998.

L. Bobrowski and M. Topczewska, Separable Linearization of Learning Sets by Ranked Layer of Radial Binary Classifiers, Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, Advances in Intelligent Systems and Computing, 2013.
DOI : 10.1007/978-3-319-00969-8_13

M. Wozniak and B. Krawczyk, Combined classifier based on feature space partitioning, International Journal of Applied Mathematics and Computer Science, vol.22, issue.4, pp.855-866, 2012.
DOI : 10.2478/v10006-012-0063-0

X. Chen, L. Wang, and Y. Xu, A symmetric orthogonal FastICA algorithm and applications in EEG. ICNC'09, Fifth International Conference on Natural Computation, 2009.
DOI : 10.1109/icnc.2009.482

D. A. Peterson, J. N. Knight, M. J. Kirby, A. W. Ch, and M. H. Thaut, Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface, EURASIP Journal on Advances in Signal Processing, vol.2005, issue.19, pp.3128-3140, 2005.
DOI : 10.1155/ASP.2005.3128

URL : http://doi.org/10.1155/asp.2005.3128

P. Boord, A. Craig, Y. Tran, and H. Nguyen, Discrimination of left and right leg motor imagery for brain???computer interfaces, Medical & Biological Engineering & Computing, vol.90, issue.4, pp.343-350, 2010.
DOI : 10.1007/s11517-010-0579-0

H. M. Park, S. H. Oh, and S. Y. Lee, A modified infomax algorithm for blind signal separation, Neurocomputing, vol.70, issue.1-3, pp.229-240, 2006.
DOI : 10.1016/j.neucom.2006.03.009

J. V. Stone, Independent component analysis: an introduction, Trends in Cognitive Sciences, vol.6, issue.2, pp.59-64, 2002.
DOI : 10.1016/S1364-6613(00)01813-1

D. Langlois, S. Chartier, and D. Gosselin, An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms, Tutorials in Quantitative Methods for Psychology, vol.6, issue.1, pp.31-38, 2010.
DOI : 10.20982/tqmp.06.1.p031

URL : http://doi.org/10.20982/tqmp.06.1.p031

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

P. Tichavský, Z. Koldovský, and E. Oja, Performance analysis of the FastICA algorithm and Crame/spl acute/r-rao bounds for linear independent component analysis, IEEE Transactions on Signal Processing, vol.54, issue.4, pp.1189-1203, 2006.
DOI : 10.1109/TSP.2006.870561

G. Naik and D. Kumar, An Overview of Independent Component Analysis and Its Applications, Informatykica, pp.63-81, 2011.

C. Fan, B. Wang, and H. Ju, A New FastICA Algorithm with Symmetric Orthogonalization, 2006 International Conference on Communications, Circuits and Systems, pp.2058-2061, 2006.
DOI : 10.1109/ICCCAS.2006.285083

R. Linsker, Self-organization in a perceptual network, Computer, vol.21, issue.3, pp.105-117, 1988.
DOI : 10.1109/2.36

A. J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.696

F. Raimondo, J. E. Kamienkowski, M. Sigman, and D. F. Slezak, CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis, Computational Intelligence and Neuroscience, vol.268, issue.5215, 2012.
DOI : 10.1109/TBME.2004.826699

URL : http://doi.org/10.1155/2012/206972

J. Karhunen, Neural approaches to independent component analysis and source separation, Proc 4th European Symposium on Artificial Neural Networks (ESANN '96), 1996.

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