E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning) Massachusetts, 2004.

H. Alwaisiti, I. Aris, and A. Jantan, Brain computer interface design and applications: challenges and future, World Appl. Sci. J, vol.11, pp.819-825, 2010.

A. Barachant, Winning Solution at the BCI Challenge @ NER, 2015.

D. Barrack, BCI challenge: error potential detection with cross-subject generalisation, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015.

S. Bhattacharyya, D. Basu, A. Konar, and D. Tibarewala, Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm, Robotics and Autonomous Systems, vol.68, pp.104-115, 2015.
DOI : 10.1016/j.robot.2015.01.007

S. Bhattacharyya, A. Konar, and D. Tibarewala, Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose, Medical & Biological Engineering & Computing, vol.18, issue.6, pp.1007-1017, 2014.
DOI : 10.1002/9780470644560

Y. Chae, J. Jeong, J. , and S. , Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI, IEEE Transactions on Robotics, vol.28, issue.5, pp.1131-1144, 2012.
DOI : 10.1109/TRO.2012.2201310

R. Chavarriaga, A. Sobolewski, and J. D. And-millán, Errare machinale est: the use of error-related potentials in brain-machine interfaces, Frontiers in Neuroscience, vol.37, issue.88, 2014.
DOI : 10.1016/j.patrec.2013.05.020

H. Chen, C. , and S. , A moving average based filtering system with its application to real-time QRS detection, Computers in Cardiology, 2003, pp.585-588, 2003.
DOI : 10.1109/CIC.2003.1291223

H. Chen, P. Tino, and X. Yao, Probabilistic Classification Vector Machines, IEEE Transactions on Neural Networks, vol.20, issue.6, pp.901-914, 2009.
DOI : 10.1109/TNN.2009.2014161

A. Combaz, N. Chumerin, N. Manyakov, A. Robben, J. Suykens et al., Towards the detection of error-related potentials and its integration in the context of a P300 speller brain???computer interface, Neurocomputing, vol.80, pp.73-82, 2012.
DOI : 10.1016/j.neucom.2011.09.013

D. Devlaminck, B. Wyns, M. Grosse-wentrup, G. Otte, and P. Santens, Multisubject learning for common spatial patterns in motorimagery BCI, Comput. Intell. Neurosci, pp.217987-217997, 2011.

T. G. Dietterich, Ensemble Methods in Machine Learning, Proceedings of the First International Workshop on Multiple Classifier Systems, pp.1-15, 2000.
DOI : 10.1007/3-540-45014-9_1

G. Dornhege, Toward Brain-Computer Interfacing. A Bradford Book, 2007.

M. Falkenstein, J. Hoormann, S. Christ, and J. Hohnsbein, ERP components on reaction errors and their functional significance: a tutorial, Biological Psychology, vol.51, issue.2-3, pp.87-107, 2000.
DOI : 10.1016/S0301-0511(99)00031-9

URL : http://www.sfu.ca/~jmcd/courses/925/papers/Falkenstein(2000).pdf

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

M. Fatourechi, R. Ward, S. Mason, J. Huggins, A. Schlogl et al., Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets, 2008 Seventh International Conference on Machine Learning and Applications, pp.8-777, 2008.
DOI : 10.1109/ICMLA.2008.34

S. Fazli, C. Grozea, M. Danoczy, B. Blankertz, F. Popescu et al., Subject independent EEG-based BCI decoding, Advances in Neural Information Processing Systems, pp.513-521, 2009.

P. W. Ferrez and J. D. Millan, Simultaneous real-time detection of motor imagery and error-related potentials for improved BCI accuracy, Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course, pp.197-202, 2008.

C. Goutte and E. Gaussier, A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation, Proceedings of the 27th European Conference on Advances in Information Retrieval Research, ECIR'05, pp.345-359, 2005.
DOI : 10.1007/978-3-540-31865-1_25

J. A. Hanley and B. J. Mcneil, The meaning and use of the area under a receiver operating characteristic (ROC) curve., Radiology, vol.143, issue.1, pp.29-36, 1982.
DOI : 10.1148/radiology.143.1.7063747

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

T. Hinterberger, S. Schmidt, N. Neumann, J. Mellinger, B. Blankertz et al., Brain-Computer Communication and Slow Cortical Potentials, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.1011-1018, 2004.
DOI : 10.1109/TBME.2004.827067

T. Jung, S. Makeig, C. Humphries, T. Lee, M. J. Mckeown et al., Removing electroencephalographic artifacts by blind source separation, Psychophysiology, vol.37, issue.2, pp.163-178, 2000.
DOI : 10.1111/1469-8986.3720163

URL : http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA455940&Location=U2&doc=GetTRDoc.pdf

H. Kang, Y. Nam, and S. Choi, Composite Common Spatial Pattern for Subject-to-Subject Transfer, IEEE Signal Processing Letters, vol.16, issue.8, pp.683-686, 2009.
DOI : 10.1109/LSP.2009.2022557

Z. Li, M. Hayashibe, C. Fattal, and D. Guiraud, Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics, IEEE Computational Intelligence Magazine, vol.9, issue.2, pp.38-46, 2014.
DOI : 10.1109/MCI.2014.2307224

URL : https://hal.archives-ouvertes.fr/lirmm-00980641

F. Lotte and C. Guan, Learning from other subjects helps reducing Brain-Computer Interface calibration time, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.614-617, 2010.
DOI : 10.1109/ICASSP.2010.5495183

URL : https://hal.archives-ouvertes.fr/inria-00441670

S. Mason, A. Bashashati, M. Fatourechi, K. Navarro, and G. Birch, A Comprehensive Survey of Brain Interface Technology Designs, Annals of Biomedical Engineering, vol.10, issue.3, pp.137-169, 2007.
DOI : 10.1089/cpb.2004.7.694

M. T. Mccann, D. E. Thompson, Z. H. Syed, and J. E. Huggins, Electrode subset selection methods for an EEG-based P300 brain-computer interface, Disability and Rehabilitation: Assistive Technology, vol.51, issue.3, 2015.
DOI : 10.1088/1741-2560/7/5/056013

J. Millán, R. Rupp, G. R. Müller-putz, R. Murray-smith, C. Giugliemma et al., Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges, Frontiers in Neuroscience, vol.1, p.161, 2010.
DOI : 10.3389/fnins.2010.00161

J. Millán, F. Renkens, J. Mourino, and W. Gerstner, Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.1026-1033, 2004.
DOI : 10.1109/TBME.2004.827086

G. Müller-putz, R. Scherer, C. Neuper, and G. Pfurtscheller, Steady-State Somatosensory Evoked Potentials: Suitable Brain Signals for Brain???Computer Interfaces?, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, issue.1, pp.30-37863842, 2005.
DOI : 10.1109/TNSRE.2005.863842

A. Y. Ng, regularization, and rotational invariance, Twenty-first international conference on Machine learning , ICML '04, p.78, 2004.
DOI : 10.1145/1015330.1015435

URL : http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/354.pdf

L. Nicolas-alonso and J. Gomez-gil, Brain Computer Interfaces, a Review, Sensors, vol.18, issue.12, pp.1211-1279, 2012.
DOI : 10.3109/10673229.2010.496623

URL : https://doi.org/10.3390/s120201211

A. V. Oppenheim, R. W. Schafer, and B. , Discrete-Time Signal Processing, J. R, 1999.

M. Perrin, E. Maby, R. Bouet, O. Bertrand, and J. Mattout, Detecting and interpreting responses to feedback in BCI, Proceedings of the 5th International Brain-Computer Interface Workshop and Training Course, pp.116-119, 2011.

M. Perrin, E. Maby, S. Daligault, O. Bertrand, and J. Mattout, Objective and subjective evaluation of online error correction during p300- based spelling, Adv. Hum. Comput. Interact, p.578295, 2012.

W. Samek, F. Meinecke, and K. Muller, Transferring Subspaces Between Subjects in Brain--Computer Interfacing, IEEE Transactions on Biomedical Engineering, vol.60, issue.8, pp.2289-2298, 2013.
DOI : 10.1109/TBME.2013.2253608

URL : http://arxiv.org/pdf/1209.4115

A. Savitzky and M. J. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures., Analytical Chemistry, vol.36, issue.8, pp.1627-1639, 1964.
DOI : 10.1021/ac60214a047

R. Schafer, What Is a Savitzky-Golay Filter? [Lecture Notes], IEEE Signal Processing Magazine, vol.28, issue.4, pp.111-117, 2011.
DOI : 10.1109/MSP.2011.941097

G. Schalk, Sensor Modalities for Brain-Computer Interfacing, Human- Computer Interaction. Novel Interaction Methods and Techniques, pp.616-622, 2009.
DOI : 10.1088/1741-2560/5/1/P01

G. Schalk, J. R. Wolpaw, D. J. Mcfarland, and G. Pfurtscheller, EEG-based communication: presence of an error potential, Clinical Neurophysiology, vol.111, issue.12, pp.2138-2144, 2000.
DOI : 10.1016/S1388-2457(00)00457-0

B. D. Seno, M. Matteucci, and L. Mainard, Online detection of p300 and error potentials in a BCI speller, Comput. Intell. Neurosci, p.307254, 2010.

H. T. Van-schie, R. B. Mars, M. G. Coles, H. Bekkering, N. R. Waytowich et al., Modulation of activity in medial frontal and motor cortices during error observation, Nature Neuroscience, vol.26, issue.5, pp.549-554, 1239.
DOI : 10.1111/j.1469-8986.1989.tb03159.x