E. A. Curran and M. J. Stokes, Learning to control brain activity: A review of the production and control of EEG components for driving brain???computer interface (BCI) systems, Brain and Cognition, vol.51, issue.3, pp.51-326, 2003.
DOI : 10.1016/S0278-2626(03)00036-8

L. 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, 1988.
DOI : 10.1016/0013-4694(88)90149-6

J. Jin, B. Z. Allison, E. W. Sellers, C. Brunner, P. Horki et al., An adaptive P300-based control system, Journal of Neural Engineering, vol.8, issue.3, p.36006, 2011.
DOI : 10.1088/1741-2560/8/3/036006
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429775/pdf

J. J. Vidal, Toward Direct Brain-Computer Communication, Annual Review of Biophysics and Bioengineering, vol.2, issue.1, pp.157-180, 1973.
DOI : 10.1146/annurev.bb.02.060173.001105

X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. Jung et al., High-speed spelling with a noninvasive brain???computer interface, Proceedings of the National Academy of Sciences, vol.2014, issue.5, pp.6058-6067, 2015.
DOI : 10.1109/78.157221

Z. Qiu, B. Z. Allison, J. Jin, Y. Zhang, X. Wang et al., Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.25, issue.7, 2017.
DOI : 10.1109/TNSRE.2017.2655542
URL : http://ir.sia.cn//bitstream/173321/20871/1/Optimized%20Motor%20Imagery%20Paradigm%20Based%20on%20Imagining%20Chinese%20Characters%20Writing%20Movement.pdf

G. Pfurtscheller, B. Z. Allison, C. Brunner, G. Bauernfeind, T. Solis-escalante et al., The hybrid BCI, Frontiers in Neuroscience, vol.2, pp.1-12, 2010.
DOI : 10.3389/fnpro.2010.00003

M. Wang, I. Daly, B. Z. Allison, J. Jin, Y. Zhang et al., A new hybrid BCI paradigm based on P300 and SSVEP, Journal of Neuroscience Methods, vol.244, pp.16-25, 2015.
DOI : 10.1016/j.jneumeth.2014.06.003

G. Pfurtscheller, C. Brunner, A. Schlögl, F. H. Lopes, and . Silva, Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks, NeuroImage, vol.31, issue.1, pp.153-159, 2006.
DOI : 10.1016/j.neuroimage.2005.12.003

S. Silvoni, A. Ramos-murguialday, M. Cavinato, C. Volpato, G. Cisotto et al., Birbaumer, Brain?computer interface in stroke: a review of progress, Clin. EEG Neurosci, pp.42-245, 2011.

R. Swaminathan and S. Prasad, Brain Computer Interface Used in Health Care Technologies, pp.49-58, 2016.
DOI : 10.1007/978-981-287-670-6_6

]. A. Kübler, F. Nijboer, J. Mellinger, T. M. Vaughan, H. Pawelzik et al., Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface, Neurology, vol.64, issue.10, pp.1775-1777, 2005.
DOI : 10.1212/01.WNL.0000158616.43002.6D

R. Scherer, A. Schlögl, F. Lee, H. Bischof, J. Jan?a et al., The Self-Paced Graz Brain-Computer Interface: Methods and Applications, Computational Intelligence and Neuroscience, vol.2007, pp.2007-79826, 2007.
DOI : 10.1109/TBME.2004.827078
URL : http://downloads.hindawi.com/journals/cin/2007/079826.pdf

Y. Yang, J. Wiart, and I. Bloch, Towards next generation human-computer interaction-brain?computer interfaces: applications and challenges, 1st International Symposium of Chinese CHI, pp.1-2, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00837513

S. Liang, K. Choi, J. Qin, W. Pang, Q. Wang et al., Improving the discrimination of hand motor imagery via virtual reality based visual guidance, Computer Methods and Programs in Biomedicine, vol.132, pp.63-74, 2016.
DOI : 10.1016/j.cmpb.2016.04.023

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Müller, Optimizing Spatial filters for Robust EEG Single-Trial Analysis, IEEE Signal Processing Magazine, vol.25, issue.1, pp.25-41, 2008.
DOI : 10.1109/MSP.2008.4408441
URL : http://ida.first.fhg.de/publications/BlaTomLemKawMue08.pdf

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Automatic selection of the number of spatial filters for motor-imagery BCI, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.109-114, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737242

S. Wang and C. J. James, Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis, Computational Intelligence and Neuroscience, vol.7, issue.6, pp.2007-41468, 2007.
DOI : 10.1023/A:1013903804720
URL : https://doi.org/10.1155/2007/41468

Y. Yang, I. Bloch, S. Chevallier, and J. Wiart, Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain???Computer Interfaces, Cognitive Computation, vol.2014, issue.1, pp.505-518, 2016.
DOI : 10.1109/ICASSP.2013.6637856
URL : https://hal.archives-ouvertes.fr/hal-01351620

Y. Yang, O. Kyrgyzov, J. Wiart, and I. Bloch, Subject-specific channel selection for classification of motor imagery electroencephalographic data, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1277-1280, 2013.
DOI : 10.1109/ICASSP.2013.6637856
URL : https://hal.archives-ouvertes.fr/hal-00837516

L. He, Y. Hu, Y. Li, and D. Li, Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG, Neurocomputing, vol.121, pp.423-433, 2013.
DOI : 10.1016/j.neucom.2013.05.005

M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI, IEEE Transactions on Biomedical Engineering, vol.58, issue.6, pp.58-1865, 2011.
DOI : 10.1109/TBME.2011.2131142

O. Kyrgyzov, I. Bloch, Y. Yang, J. Wiart, and A. Souloumiac, Data Ranking and Clustering via Normalized Graph Cut Based on Asymmetric Affinity, pp.562-571, 2013.
DOI : 10.1007/978-3-642-41184-7_57

H. Shan, H. Xu, S. Zhu, and B. , A novel channel selection method for optimal classification in different motor imagery BCI paradigms, BioMedical Engineering OnLine, vol.9, issue.2, p.93, 2015.
DOI : 10.1523/JNEUROSCI.3886-06.2007

J. Wang, F. Xue, and H. Li, Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso, BioMed Research International, vol.33, issue.1, pp.2015-703768, 2015.
DOI : 10.1109/TNSRE.2012.2229296

A. Barachant and S. Bonnet, Channel selection procedure using riemannian distance for BCI applications, 2011 5th International IEEE/EMBS Conference on Neural Engineering, pp.348-351, 2011.
DOI : 10.1109/NER.2011.5910558
URL : https://hal.archives-ouvertes.fr/hal-00602707

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels, EURASIP Journal on Advances in Signal Processing, vol.58, issue.6, p.2014, 2014.
DOI : 10.1109/ICDE.2008.4497429
URL : https://hal.archives-ouvertes.fr/hal-01351618

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2744-2747, 2012.
DOI : 10.1109/EMBC.2012.6346532
URL : https://hal.archives-ouvertes.fr/hal-00737280

B. Yang, H. Li, Q. Wang, and Y. Zhang, Subject-based feature extraction by using fisher WPD-CSP in brain???computer interfaces, Computer Methods and Programs in Biomedicine, vol.129, pp.21-28, 2016.
DOI : 10.1016/j.cmpb.2016.02.020

J. Luo, Z. Feng, J. Zhang, and N. Lu, Dynamic frequency feature selection based approach for classification of motor imageries, Computers in Biology and Medicine, vol.75, pp.75-120, 2016.
DOI : 10.1016/j.compbiomed.2016.03.004

C. Ansuini, A. Cavallo, A. Koul, M. Jacono, Y. Yang et al., Predicting Object Size from Hand Kinematics: A Temporal Perspective, PLOS ONE, vol.38, issue.1, p.120432, 2015.
DOI : 10.1371/journal.pone.0120432.g005
URL : https://doi.org/10.1371/journal.pone.0120432

A. Schlögl, F. Lee, H. Bischof, and G. Pfurtscheller, Characterization of four-class motor imagery EEG data for the BCI-competition 2005, Journal of Neural Engineering, vol.2, issue.4, p.14, 2005.
DOI : 10.1088/1741-2560/2/4/L02

G. Pfurtscheller, F. H. Lopes, and . Silva, Event-related EEG/MEG synchronization and desynchronization: basic principles, Clinical Neurophysiology, vol.110, issue.11, pp.1842-1857, 1999.
DOI : 10.1016/S1388-2457(99)00141-8

B. Blankertz, K. R. Müller, D. J. Krusienski, G. Schalk, J. R. Wolpaw et al., The BCI Competition III: Validating Alternative Approaches to Actual BCI Problems, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, issue.2, pp.14-153, 2006.
DOI : 10.1109/TNSRE.2006.875642

M. Grosse-wentrup and M. Buss, Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction, IEEE Transactions on Biomedical Engineering, vol.55, issue.8, 1991.
DOI : 10.1109/TBME.2008.921154

H. Wang, Harmonic mean of Kullback-Leibler divergences for optimizing multi-class EEG spatio-temporal filters, Neural Process, Lett, vol.36, pp.161-171, 2012.

M. Miao, H. Zeng, A. Wang, C. Zhao, and F. Liu, Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Na??ve Bayesian Classifier-based approach, Journal of Neuroscience Methods, vol.278, pp.13-24, 2017.
DOI : 10.1016/j.jneumeth.2016.12.010

M. Mesbah, A. Khorshidtalab, H. Baali, and A. , Al-Ani, Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction Neural Information Processing, pp.1-9, 2015.

H. Baali, A. Khorshidtalab, M. Mesbah, and M. J. Salami, A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification, IEEE Journal of Translational Engineering in Health and Medicine, vol.3, pp.1-8, 2015.
DOI : 10.1109/JTEHM.2015.2485261

C. Ansuini, A. Cavallo, and A. Koul, Grasping others??? movements: Rapid discrimination of object size from observed hand movements., Journal of Experimental Psychology: Human Perception and Performance, vol.42, issue.7, p.918, 2016.
DOI : 10.1037/xhp0000169

J. Deng, J. Yao, and J. P. Dewald, Classification of the intention to generate a shoulder versus elbow torque by means of a time???frequency synthesized spatial patterns BCI algorithm, Journal of Neural Engineering, vol.2, issue.4, 2005.
DOI : 10.1088/1741-2560/2/4/009

C. Vidaurre, N. Kramer, B. Blankertz, and A. Schlögl, Time Domain Parameters as a feature for EEG-based Brain???Computer Interfaces, Neural Networks, vol.22, issue.9, pp.1313-1319, 2009.
DOI : 10.1016/j.neunet.2009.07.020

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 1992.

K. V. Mardia, Measures of multivariate skewness and kurtosis with applications, Biometrika, vol.57, issue.3, pp.519-530, 1970.
DOI : 10.1093/biomet/57.3.519

H. Suk and S. Lee, A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.2, pp.286-299, 2013.
DOI : 10.1109/TPAMI.2012.69

F. Lotte and C. Guan, Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms, IEEE Transactions on Biomedical Engineering, vol.58, issue.2, pp.355-362, 2011.
DOI : 10.1109/TBME.2010.2082539
URL : https://hal.archives-ouvertes.fr/inria-00476820

C. Park, D. Looney, N. Ur-rehman, A. Ahrabian, and D. P. Mandic, Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.21, issue.1, pp.10-22, 2013.
DOI : 10.1109/TNSRE.2012.2229296

]. A. Khorshidtalab, M. J. Salami, and A. Rini, Motor imagery task classification using transformation based features, Biomedical Signal Processing and Control, vol.33, pp.213-219, 2017.
DOI : 10.1016/j.bspc.2016.12.006
URL : http://irep.iium.edu.my/53662/1/2017_aida_biomedical.pdf

K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b, Frontiers in Neuroscience, vol.6, issue.39, 2012.
DOI : 10.3389/fnins.2012.00039
URL : http://journal.frontiersin.org/article/10.3389/fnins.2012.00039/pdf

B. Lou, B. Hong, X. Gao, and S. Gao, Bipolar electrode selection for a motor imagery based brain???computer interface, Journal of Neural Engineering, vol.5, issue.3, pp.342-349, 2008.
DOI : 10.1088/1741-2560/5/3/007

T. Solis-escalante, G. Müller-putz, and G. Pfurtscheller, Overt foot movement detection in one single Laplacian EEG derivation, Journal of Neuroscience Methods, vol.175, issue.1, pp.148-153, 2008.
DOI : 10.1016/j.jneumeth.2008.07.019

S. Fitzgibbon, D. Delosangeles, T. Lewis, D. Powers, E. Whitham et al., Surface Laplacian of scalp electrical signals and independent component analysis resolve EMG contamination of electroencephalogram, International Journal of Psychophysiology, vol.97, issue.3, pp.97-277, 2015.
DOI : 10.1016/j.ijpsycho.2014.10.006

A. Jain and D. Zongker, Feature selection: evaluation, application, and small sample performance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.2, pp.153-158, 1997.
DOI : 10.1109/34.574797
URL : http://www.doc.ic.ac.uk/~xh1/Referece/Current-Reading/Feature-selection-evaluation-application-and-small-sample-performance.pdf

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

B. Hjorth, An on-line transformation of EEG scalp potentials into orthogonal source derivations, Electroencephalography and Clinical Neurophysiology, vol.39, issue.5, pp.526-530, 1975.
DOI : 10.1016/0013-4694(75)90056-5