S. Baillet, J. Mosher, and R. Leahy, Electromagnetic brain mapping, IEEE Signal Processing Magazine, vol.18, issue.6, pp.14-30, 2001.
DOI : 10.1109/79.962275

T. Balli and R. Palaniappan, Classification of biological signals using linear and nonlinear features, Physiological Measurement, vol.31, issue.7, p.31903, 2010.
DOI : 10.1088/0967-3334/31/7/003

A. Bashashati, M. Fatourechi, R. Ward, and G. Birch, A survey of signal processing algorithms in brain???computer interfaces based on electrical brain signals, Journal of Neural Engineering, vol.4, issue.2, pp.35-57, 2007.
DOI : 10.1088/1741-2560/4/2/R03

K. Bennett and C. Campbell, Support vector machines, ACM SIGKDD Explorations Newsletter, vol.2, issue.2, pp.1-13, 2000.
DOI : 10.1145/380995.380999

M. Besserve, J. Martinerie, L. Garnero, M. Kawanabe, R. Tomioka et al., Improving quantification of functional networks with eeg inverse problem: Evidence from a decoding point of view Invariant common spatial patterns: Alleviating nonstationarities in braincomputer interfacing Optimizing spatial filters for robust EEG single-trial analysis, Advances in Neural Information Processing Systems, pp.41-56, 2008.

B. Blankertz, S. Lemm, M. Treder, S. Haufe, and K. Müller, Single-trial analysis and classification of ERP components ??? A tutorial, NeuroImage, vol.56, issue.2, 2010.
DOI : 10.1016/j.neuroimage.2010.06.048

R. Boostani and M. Moradi, A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier, Journal of Neural Engineering, vol.1, issue.4, pp.212-217, 2004.
DOI : 10.1088/1741-2560/1/4/004

N. Brodu, F. Lotte, and A. Lécuyer, Comparative study of band-power extraction techniques for Motor Imagery classification, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), pp.1-6, 2011.
DOI : 10.1109/CCMB.2011.5952105
URL : https://hal.archives-ouvertes.fr/inria-00609161

N. Brodu, F. Lotte, and A. Lécuyer, Exploring two novel features for EEG-based brain???computer interfaces: Multifractal cumulants and predictive complexity, Neurocomputing, vol.79, issue.1, pp.87-94, 2012.
DOI : 10.1016/j.neucom.2011.10.010
URL : https://hal.archives-ouvertes.fr/inria-00632546

M. Browne, Cross-Validation Methods, Journal of Mathematical Psychology, vol.44, issue.1, pp.108-132, 2000.
DOI : 10.1006/jmps.1999.1279

C. Brunner, M. Naeem, R. Leeb, B. Graimann, and G. Pfurtscheller, Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis, Pattern Recognition Letters, vol.28, issue.8, pp.957-964, 2007.
DOI : 10.1016/j.patrec.2007.01.002

C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

M. Congedo, F. Lotte, and A. Lécuyer, Classification of movement intention by spatially filtered electromagnetic inverse solutions, Physics in Medicine and Biology, vol.51, issue.8, pp.1971-1989, 2006.
DOI : 10.1088/0031-9155/51/8/002
URL : https://hal.archives-ouvertes.fr/inria-00134948

D. G. Devlaminck, B. Blankertz, G. Curio, and K. Müller, Optimization of brain-computer interfaces Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multi-class paradigms, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.993-1002, 2004.

G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio et al., Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing, IEEE Transactions on Biomedical Engineering, vol.53, issue.11, pp.2274-2281, 2006.
DOI : 10.1109/TBME.2006.883649

J. Farquhar, N. Hill, T. Lal, B. Schölkopf, M. Fatourechi et al., Regularised CSP for sensor selection in BCI EMG and EOG artifacts in brain computer interface systems: A survey, Proceedings of the 3rd international BCI workshop, pp.480-494, 2006.

R. Fazel-rezai, A. B. Guger, C. Sellers, E. Kleih, S. Kübler et al., P300 brain computer interface: current challenges and emerging trends, Frontiers in Neuroengineering, vol.5, issue.14, 2012.
DOI : 10.3389/fneng.2012.00014

J. Friedman, On bias, variance, 0/1-loss, and the curse-of-dimensionality, Data Mining and Knowledge Discovery, vol.1, issue.1, pp.55-77, 1997.
DOI : 10.1023/A:1009778005914

E. Friedrich, R. Scherer, C. Neuper, D. Garrett, D. Peterson et al., The effect of distinct mental strategies on classification performance for brain-computer interfaces Statistical Pattern Recognition, second edition Comparison of linear, nonlinear, and feature selection methods for EEG signal classification, International Journal of Psychophysiology IEEE Transactions on Neural System and Rehabilitation Engineering, vol.11, issue.0, pp.141-144, 1990.

C. Gouy-pailler, S. Achard, B. Rivet, C. Jutten, E. Maby et al., Topographical Dynamics of Brain Connections for the Design of Asynchronous Brain-Computer Interfaces, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2520-2523, 2007.
DOI : 10.1109/IEMBS.2007.4352841
URL : https://hal.archives-ouvertes.fr/hal-00174738

M. Grosse-wentrup, Understanding brain connectivity patterns during motor imagery for brain-computer interfacing, Advances in neural information processing systems (NIPS) 21, 2009.

M. Grosse-wentrup and M. Buss, Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction, IEEE Transactions on Biomedical Engineering, vol.55, issue.8, pp.1991-2000, 2008.
DOI : 10.1109/TBME.2008.921154

M. Grosse-wentrup, K. Gramann, E. Wascher, and M. Buss, EEG source localization for brain-computer-interfaces, 2nd International IEEE EMBS Conference on Neural Engineering, pp.128-131, 2005.

M. Grosse-wentrup, C. Liefhold, K. Gramann, and M. Buss, Beamforming in Noninvasive Brain–Computer Interfaces, IEEE Transactions on Biomedical Engineering, vol.56, issue.4, pp.1209-1219, 2009.
DOI : 10.1109/TBME.2008.2009768

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

M. Hall, Correlation-based feature selection for discrete and numeric class machine learning, Proc. 17th International Conf. on Machine Learning, pp.359-366, 2000.

S. Haufe, F. Meinecke, K. Görgen, S. Dähne, J. Haynes et al., On the interpretation of weight vectors of linear models in multivariate neuroimaging, NeuroImage, vol.87, pp.96-110, 2014.
DOI : 10.1016/j.neuroimage.2013.10.067

P. Herman, G. Prasad, T. Mcginnity, and D. Coyle, Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.16, issue.4, pp.317-326, 2008.
DOI : 10.1109/TNSRE.2008.926694

U. Hoffmann, J. Vesin, and T. Ebrahimi, Spatial filters for the classification of event-related potentials, European Symposium on Artificial Neural Networks, 2006.

U. Hoffmann, J. Vesin, T. Ebrahimi, and K. Diserens, An efficient P300-based brain???computer interface for disabled subjects, Journal of Neuroscience Methods, vol.167, issue.1, pp.115-125, 2008.
DOI : 10.1016/j.jneumeth.2007.03.005

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

A. Jain, R. Duin, and J. Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.1, pp.4-37, 2000.
DOI : 10.1109/34.824819

A. Kachenoura, L. Albera, L. Senhadji, and P. Comon, Ica: a potential tool for bci systems, IEEE Signal Processing Magazine, vol.25, issue.1, pp.57-68, 2008.
DOI : 10.1109/MSP.2008.4408442
URL : https://hal.archives-ouvertes.fr/inserm-00202706

B. Kamousi, Z. Liu, and B. He, Classification of Motor Imagery Tasks for Brain-Computer Interface Applications by Means of Two Equivalent Dipoles Analysis, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.13, issue.2, pp.166-171, 2005.
DOI : 10.1109/TNSRE.2005.847386

D. Krusienski, E. Sellers, F. Cabestaing, S. Bayoudh, D. Mcfarland et al., A comparison of classification techniques for the P300 Speller, Journal of Neural Engineering, vol.3, issue.4, pp.299-305, 2006.
DOI : 10.1088/1741-2560/3/4/007
URL : https://hal.archives-ouvertes.fr/hal-00521054

D. Krusienski, D. Mcfarland, and J. Wolpaw, Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain???computer interface, Brain Research Bulletin, vol.87, issue.1, pp.130-134, 2012.
DOI : 10.1016/j.brainresbull.2011.09.019

T. Lal, M. Schröder, T. Hinterberger, J. Weston, M. Bogdan et al., Support Vector Channel Selection in BCI, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, p.10031010, 2004.
DOI : 10.1109/TBME.2004.827827

T. Lan, D. Erdogmus, A. Adami, S. Mathan, and M. Pavel, Feature and channel selection for cognitive state estimation using ambulatory EEG A well-conditioned estimator for large-dimensional covariance matrices, Computational Intelligence and Neuroscience Ledoit O, Wolf M Journal of Multivariate Analysis, vol.88, issue.2, pp.365-411, 2004.

S. Lemm, B. Blankertz, G. Curio, and K. Mller, Spatio-Spectral Filters for Improving the Classification of Single Trial EEG, IEEE Transactions on Biomedical Engineering, vol.52, issue.9, pp.1541-1548, 2005.
DOI : 10.1109/TBME.2005.851521

F. Lotte, A new feature and associated optimal spatial filter for EEG signal classification: Waveform length, International Conference on Pattern Recognition (ICPR), pp.1302-1305, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00717617

F. Lotte and C. Guan, An efficient P300-based brain-computer interface with minimal calibration time, Assistive Machine Learning for People with Disabilities symposium (NIPS'09 Symposium, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00430563

F. Lotte and C. Guan, Learning from other subjects helps reducing braincomputer interface calibration time, International Conference on Audio, Speech and Signal Processing (ICASSP'2010), pp.614-617, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00441670

F. Lotte and C. Guan, Spatially Regularized Common Spatial Patterns for EEG Classification, 2010 20th International Conference on Pattern Recognition, 2010.
DOI : 10.1109/ICPR.2010.904
URL : https://hal.archives-ouvertes.fr/inria-00447435

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

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, A review of classification algorithms for EEG-based brain???computer interfaces, Journal of Neural Engineering, vol.4, issue.2, pp.1-13, 2007.
DOI : 10.1088/1741-2560/4/2/R01
URL : https://hal.archives-ouvertes.fr/inria-00134950

F. Lotte, J. Fujisawa, H. Touyama, R. Ito, M. Hirose et al., Towards ambulatory brain-computer interfaces, Proceedings of the International Conference on Advances in Computer Enterntainment Technology, ACE '09, pp.336-339, 2009.
DOI : 10.1145/1690388.1690452
URL : https://hal.archives-ouvertes.fr/inria-00411284

F. Lotte, A. Lécuyer, and B. Arnaldi, FuRIA: An Inverse Solution Based Feature Extraction Algorithm Using Fuzzy Set Theory for Brain–Computer Interfaces, IEEE Transactions on Signal Processing, vol.57, issue.8, pp.3253-3263, 2009.
DOI : 10.1109/TSP.2009.2020752

F. Lotte, A. Langhenhove, F. Lamarche, T. Ernest, Y. Renard et al., Exploring Large Virtual Environments by Thoughts Using a Brain???Computer Interface Based on Motor Imagery and High-Level Commands, Presence: Teleoperators and Virtual Environments, vol.9, issue.4, pp.54-70, 2010.
DOI : 10.1016/j.patrec.2007.10.009
URL : https://hal.archives-ouvertes.fr/inria-00445614

S. Mason and G. Birch, A general framework for brain-computer interface design, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.11, issue.1, pp.70-85, 2003.
DOI : 10.1109/TNSRE.2003.810426

D. Mcfarland and J. Wolpaw, Sensorimotor Rhythm-Based Brain???Computer Interface (BCI): Feature Selection by Regression Improves Performance, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.13, issue.3, pp.372-379, 2005.
DOI : 10.1109/TNSRE.2005.848627

D. Mcfarland, L. Mccane, S. David, and J. Wolpaw, Spatial filter selection for EEG-based communication, Electroencephalography and Clinical Neurophysiology, vol.103, issue.3, pp.386-394, 1997.
DOI : 10.1016/S0013-4694(97)00022-2

D. Mcfarland, C. Anderson, K. Müller, A. Schlögl, and D. Krusienski, BCI Meeting 2005???Workshop on BCI Signal Processing: Feature Extraction and Translation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, issue.2, pp.135-138, 2006.
DOI : 10.1109/TNSRE.2006.875637

J. Mellinger and G. Schalk, Toward Brain-Computer Interfacing, in: g. dornhege, j.r. millán, BCI2000: A General-Purpose Software Platform for BCI Research, pp.372-381, 2007.

C. Michel, M. Murray, G. Lantz, S. Gonzalez, L. Spinelli et al., EEG source imaging, Clinical Neurophysiology, vol.115, issue.10, pp.2195-2222, 2004.
DOI : 10.1016/j.clinph.2004.06.001
URL : https://hal.archives-ouvertes.fr/hal-00617795

J. Millán, J. No, M. Franzé, F. Cincotti, M. Varsta et al., A local neural classifier for the recognition of EEG patterns associated to mental tasks, IEEE Transactions on Neural Networks, vol.13, issue.3, pp.678-686, 2002.
DOI : 10.1109/TNN.2002.1000132

E. Miranda, W. Magee, J. Wilson, J. Eaton, and R. Palaniappan, Brain-Computer Music Interfacing (BCMI): From Basic Research to the Real World of Special Needs, Music and Medicine, vol.3, issue.3, pp.134-140, 2011.
DOI : 10.1177/1943862111399290

N. Caramia and S. Lotte, Optimizing spatial filter pairs for EEG classification based on phase-synchronization, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
DOI : 10.1109/ICASSP.2014.6853959
URL : https://hal.archives-ouvertes.fr/hal-01053189

Q. Noirhomme, R. Kitney, and B. Macq, Single-Trial EEG Source Reconstruction for Brain–Computer Interface, IEEE Transactions on Biomedical Engineering, vol.55, issue.5, pp.1592-1601, 2008.
DOI : 10.1109/TBME.2007.913986

B. Obermeier, C. Guger, C. Neuper, and G. Pfurtscheller, Hidden markov models for online classification of single trial EEG. Pattern recognition letters pp, pp.1299-1309, 2001.

P. Ofner, G. Muller-putz, C. Neuper, and C. Brunner, Comparison of feature extraction methods for brain-computer interfaces, International BCI Conference, 2011.

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1238, 2005.
DOI : 10.1109/TPAMI.2005.159

W. Penny, S. Roberts, E. Curran, and M. Stokes, EEG-based communication: a pattern recognition approach, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.2, pp.214-215, 2000.
DOI : 10.1109/86.847820

G. Pfurtscheller and C. Neuper, Motor imagery and direct brain-computer communication, Proceedings of the IEEE, vol.89, issue.7, pp.1123-1134, 2001.
DOI : 10.1109/5.939829

G. Pfurtscheller and F. Da-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

P. Pudil, F. Ferri, and J. Kittler, Floating search methods for feature selection with nonmonotonic criterion functions, Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), pp.279-283, 1994.
DOI : 10.1109/ICPR.1994.576920

L. Qin, L. Ding, and B. He, Motor imagery classification by means of source analysis for brain???computer interface applications, Journal of Neural Engineering, vol.1, issue.3, pp.135-141, 2004.
DOI : 10.1088/1741-2560/1/3/002

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

H. Ramoser, J. Muller-gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.4, pp.441-446, 2000.
DOI : 10.1109/86.895946

S. Raudys and A. 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

B. Reuderink, M. Poel, A. Souloumiac, V. Attina, and G. Gibert, Robustness of the common spatial patterns algorithm in the BCI-pipeline. Tech. rep., HMI) xdawn algorithm to enhance evoked potentials: Application to brain computer interface, IEEE Transactions on Biomedical Engineering, vol.56, issue.8, pp.2035-2043, 2008.

W. Samek, C. Vidaurre, K. Müller, and M. Kawanabe, Stationary common spatial patterns for braincomputer interfacing, Journal of Neural Engineering, vol.9, issue.2, 2012.

C. Sannelli, T. Dickhaus, S. Halder, E. Hammer, K. Müller et al., On optimal channel configurations for SMR-based brain-computer interfaces Towards Brain-Computer Interfacing , g, Brain Topography Schlögl A, pp.347-358, 2007.

M. Schröder, T. Lal, T. Hinterberger, M. Bogdan, N. Hill et al., Robust EEG channel selection across subjects for braincomputer interfaces, EURASIP J Appl Signal Process pp, pp.3103-3112, 2005.

M. Tangermann, I. Winkler, S. Haufe, and B. Blankertz, Classification of artifactual ICA components, Int J Bioelectromagnetism, vol.11, issue.2, pp.110-114, 2009.

K. Thomas, C. Guan, T. Chiew, V. Prasad, K. Ang et al., New discriminative common spatial pattern method for motor imagery brain computer interfaces An iterative algorithm for spatio-temporal filter optimization, Proceedings of the 3rd International Brain- Computer Interface Workshop and Training Course, pp.22-23, 2006.

F. Varela, J. Lachaux, E. Rodriguez, and J. Martinerie, The brainweb: phase synchronization and large-scale integration, Nature Reviews Neuroscience, vol.2, issue.4, pp.229-239, 2001.
DOI : 10.1038/35067550

F. Vialatte, M. M. Dauwels, J. Cichocki, and A. , Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives, Progress in Neurobiology, vol.90, issue.4, pp.418-438, 2010.
DOI : 10.1016/j.pneurobio.2009.11.005

C. Vidaurre, N. Krämer, 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

N. Xu, X. Gao, B. Hong, X. Miao, S. Gao et al., BCI Competition 2003???Data Set IIb: Enhancing P300 Wave Detection Using ICA-Based Subspace Projections for BCI Applications, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, pp.1067-1072, 2004.
DOI : 10.1109/TBME.2004.826699

M. Zhong, F. Lotte, M. Girolami, and A. Lécuyer, Classifying EEG for brain computer interfaces using Gaussian processes, Pattern Recognition Letters, vol.29, issue.3, pp.354-359, 2008.
DOI : 10.1016/j.patrec.2007.10.009