J. Wolpaw, N. Birbaumer, D. Mcfarland, G. Pfurtscheller, and T. Vaughan, Brain???computer interfaces for communication and control, Clinical Neurophysiology, vol.113, issue.6, pp.767-779, 2002.
DOI : 10.1016/S1388-2457(02)00057-3

L. Nicolas-alonso and J. Gomez-gil, Brain Computer Interfaces, a Review, Sensors, vol.12, issue.12, pp.1211-79, 2012.
DOI : 10.3390/s120201211

B. Graimann, A. B. Pfurtscheller, and G. , Brain???Computer Interfaces: A Gentle Introduction, Brain-Computer Interfaces, pp.1-27, 2010.
DOI : 10.1007/978-3-642-02091-9_1

G. Pfurtscheller and C. Neuper, Motor Imagery and Direct Brain-Computer Communication. proceedings of the IEEE, pp.1123-1134, 2001.

M. Jdr, R. Rupp, G. 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.4, p.20877434, 2010.

K. Ang and C. Guan, Brain-Computer Interface in Stroke Rehabilitation, Journal of Computing Science and Engineering, vol.7, issue.2, pp.139-146, 2013.
DOI : 10.5626/JCSE.2013.7.2.139

A. Ramos-murguialday, D. Broetz, M. Rea, L. Läer, Ö. Yilmaz et al., Brain-machine interface in chronic stroke rehabilitation: A controlled study, Annals of Neurology, vol.10, issue.1, pp.100-108, 2013.
DOI : 10.1002/ana.23879

A. Lécuyer, F. Lotte, R. Reilly, R. Leeb, M. Hirose et al., Brain-Computer Interfaces, Virtual Reality, and Videogames, Computer, vol.41, issue.10, pp.66-72, 2008.
DOI : 10.1109/MC.2008.410

J. Van-erp, F. Lotte, and M. Tangermann, Brain-Computer Interfaces: Beyond Medical Applications, Computer, vol.45, issue.4, pp.26-34, 2012.
DOI : 10.1109/MC.2012.107
URL : https://hal.archives-ouvertes.fr/hal-00688344

D. Coyle, J. Principe, F. Lotte, and A. Nijholt, Guest Editorial: Brain/neuronal - Computer game interfaces and interaction, IEEE Transactions on Computational Intelligence and AI in Games, vol.5, issue.2, pp.77-81, 2013.
DOI : 10.1109/TCIAIG.2013.2264736
URL : https://hal.archives-ouvertes.fr/hal-00843602

B. Allison and C. Neuper, Could Anyone Use a BCI?, 2010.
DOI : 10.1007/978-1-84996-272-8_3

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)? Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol.11, pp.145-47814481, 2003.

B. Blankertz, C. Sannelli, S. Halder, E. Hammer, A. Kübler et al., Neurophysiological predictor of SMR-based BCI performance, NeuroImage, vol.51, issue.4, pp.1303-1312, 2010.
DOI : 10.1016/j.neuroimage.2010.03.022

E. Hammer, T. Kaufmann, S. Kleih, B. Blankertz, and A. Kübler, Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR), Frontiers in Human Neuroscience, vol.7, p.25147518, 2014.
DOI : 10.3389/fnhum.2013.00478

C. Neuper and G. Pfurtscheller, Neurofeedback Training for BCI Control, Brain-Computer Interfaces, pp.65-78, 2010.
DOI : 10.1007/978-3-642-02091-9_4

F. Lotte, F. Larrue, and C. Mühl, Flaws in current human training protocols for spontaneous BCI: lessons learned from instructional design, Frontiers in Human Neurosciences, vol.7, p.568, 2013.

C. Jeunet, A. Cellard, S. Subramanian, M. Hachet, N. Kaoua et al., How well can we learn with standard BCI training approaches? A pilot study, 6th International Brain-Computer Interface Conference, pp.332-367, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01052692

M. Grosse-wentrup, B. Schölkopf, and J. Hill, Causal influence of gamma oscillations on the sensorimotor rhythm, NeuroImage, vol.56, issue.2, pp.837-879, 2011.
DOI : 10.1016/j.neuroimage.2010.04.265

M. Grosse-wentrup and B. Schölkopf, High gamma-power predicts performance in sensorimotor-rhythm brain???computer interfaces, Journal of Neural Engineering, vol.9, issue.4, p.22713543, 2012.
DOI : 10.1088/1741-2560/9/4/046001

M. Ahn, H. Cho, S. Ahn, and S. Jun, High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery, PLoS ONE, vol.43, issue.11, 2013.
DOI : 10.1371/journal.pone.0080886.t001

M. Ahn, S. Ahn, J. Hong, H. Cho, K. Kim et al., Gamma band activity associated with BCI performance: simultaneous MEG/EEG study. Frontiers in human neuroscience, p.24367322, 2013.

A. Bamdadian, C. Guan, K. Ang, and J. Xu, The predictive role of pre-cue EEG rhythms on MI-based BCI classification performance, Journal of Neuroscience Methods, vol.235, p.24979726, 2014.
DOI : 10.1016/j.jneumeth.2014.06.011

E. Combrisson and K. Jerbi, Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy, Journal of Neuroscience Methods, vol.250, p.25596422, 2015.
DOI : 10.1016/j.jneumeth.2015.01.010

I. Daum, B. Rockstroh, N. Birbaumer, T. Elbert, A. Canavan et al., Behavioural treatment of slow cortical potentials in intractable epilepsy: neuropsychological predictors of outcome., Journal of Neurology, Neurosurgery & Psychiatry, vol.56, issue.1, pp.94-97, 1993.
DOI : 10.1136/jnnp.56.1.94

N. Neumann and N. Birbaumer, Predictors of successful self control during brain-computer communication, Journal of Neurology, Neurosurgery & Psychiatry, vol.74, issue.8, pp.1117-1138, 2003.
DOI : 10.1136/jnnp.74.8.1117

F. Nijboer, A. Furdea, I. Gunst, J. Mellinger, D. Mcfarland et al., An auditory brain???computer interface (BCI), Journal of Neuroscience Methods, vol.167, issue.1, p.17399797, 2008.
DOI : 10.1016/j.jneumeth.2007.02.009

W. Burde and B. Blankertz, Is the locus of control of reinforcement a predictor of brain-computer interface performance? Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course, pp.108-117, 2006.

F. Nijboer, N. Birbaumer, and A. Kübler, The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study, Frontiers in Neuroscience, vol.4, p.20700521, 2010.
DOI : 10.3389/fnins.2010.00055

M. Witte, S. Kober, M. Ninaus, C. Neuper, and G. Wood, Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training. Frontiers in human neuroscience, 2013.

E. Hammer, S. Halder, B. Blankertz, C. Sannelli, T. Dickhaus et al., Psychological predictors of SMR-BCI performance, Biological Psychology, vol.89, issue.1, pp.80-86, 2012.
DOI : 10.1016/j.biopsycho.2011.09.006

A. Harris, Harris Tests of Lateral DominancePsychological Corporation, New York, 1958.

E. Friedrich, C. Neuper, and R. Scherer, Whatever Works: A Systematic User-Centered Training Protocol to Optimize Brain-Computer Interfacing Individually, PLoS ONE, vol.127, issue.9, p.24086710, 2013.
DOI : 10.1371/journal.pone.0076214.t002

M. Bradley and P. Lang, Measuring emotion: The self-assessment manikin and the semantic differential, Journal of Behavior Therapy and Experimental Psychiatry, vol.25, issue.1, pp.49-59, 1994.
DOI : 10.1016/0005-7916(94)90063-9

F. Zijlstra, Efficiency in work behaviour: A design approach for modern tools, 1993.

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

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

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

D. Wechsler, Wechsler adult intelligence scale?Fourth Edition (WAIS?IV), NCS Pearson, 2008.

D. Berch, R. Krikorian, and E. Huha, The Corsi Block-Tapping Task: Methodological and Theoretical Considerations, Brain and Cognition, vol.38, issue.3, pp.317-355, 1998.
DOI : 10.1006/brcg.1998.1039

A. Benton, The revised visual retention test: clinical and experimental applications. State University of Iowa; Distributor for the USA, Psychological Corporation, 1963.

D. Kolb, Learning style inventory. McBer and Company, 1999.

R. Cattell and H. Cattell, Personality Structure and the New Fifth Edition of the 16PF, Educational and Psychological Measurement, vol.23, issue.3, pp.926-963, 1995.
DOI : 10.1177/0013164495055006002

H. Levenson, Activism and Powerful Others: Distinctions within the Concept of Internal-External Control, Journal of Personality Assessment, vol.5, issue.4, pp.377-83, 1974.
DOI : 10.1080/00224545.1965.9919655

C. Spielberger, R. Gorsuch, and R. Lushene, Manual for the state-trait anxiety inventory. Palo Alto, CA: consulting psychologists press, 1970.

R. Bruininks, Bruininks-Oseretsky test of motor proficiency: Examiner's manual. American Guidance Service Circle Pines, MN, 1978.

S. Vandenberg and A. Kuse, Mental rotations, a group test of three-dimensional spatial visualization. Perceptual and motor skills, pp.599-604, 1978.

W. Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis, Brain Research Reviews, vol.29, issue.2-3, pp.169-95, 1999.
DOI : 10.1016/S0165-0173(98)00056-3

L. Aftanas and S. Golocheikine, Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation, Neuroscience Letters, vol.310, issue.1, pp.57-60, 2001.
DOI : 10.1016/S0304-3940(01)02094-8

G. Müller-putz, R. Scherer, C. Brunner, R. Leeb, and G. Pfurtscheller, Better than random? A closer look on BCI results, International Journal of Bioelectromagnetism, vol.10, pp.52-55, 2008.

A. Kübler, D. Mattia, H. George, B. Doron, and C. Neuper, How much learning is involved in BCI-control? In: International BCI Meeting, p.1000153, 2010.

W. Noble, How does multiple testing correction work?, Nature Biotechnology, vol.18, issue.12, pp.1135-1172, 2009.
DOI : 10.1038/nbt1209-1135

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

S. Derksen and H. Keselman, Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables, British Journal of Mathematical and Statistical Psychology, vol.45, issue.2, pp.265-82, 1992.
DOI : 10.1111/j.2044-8317.1992.tb00992.x

M. Whittingham, P. Stephens, R. Bradbury, and R. Freckleton, Why do we still use stepwise modelling in ecology and behaviour?, Journal of Animal Ecology, vol.17, issue.5, pp.1182-89, 2006.
DOI : 10.1111/j.1365-2656.2006.01141.x

S. Poltrock and P. Brown, Individual Differences in visual imagery and spatial ability, Intelligence, vol.8, issue.2, pp.93-138, 1984.
DOI : 10.1016/0160-2896(84)90019-9

B. Rourke and M. Finlayson, Neuropsychological significance of variations in patterns of academic performance: Verbal and visual-spatial abilities, Journal of Abnormal Child Psychology, vol.6, issue.1, pp.121-154, 1978.
DOI : 10.1007/BF00915788

A. Vromen, J. Verbunt, S. Rasquin, and D. Wade, Motor imagery in patients with a right hemisphere stroke and unilateral neglect, Brain Injury, vol.95, issue.4, pp.387-93, 2011.
DOI : 10.1080/01688638908400940

N. Hara, STUDENT DISTRESS IN A WEB-BASED DISTANCE EDUCATION COURSE, Information, Communication & Society, vol.3, issue.4, pp.557-79, 2001.
DOI : 10.1080/13691180010002297

M. Moore, Learner autonomy: The second dimension of independant lerning, Convergence, vol.5, pp.76-88, 1972.

N. Leboutillier and D. Marks, Mental imagery and creativity: A meta-analytic review study, British Journal of Psychology, vol.94, issue.1, pp.29-44, 2003.
DOI : 10.1348/000712603762842084

R. Felder and L. Silverman, Learning and teaching styles in engineering education. Engineering education, pp.674-81, 1988.

C. Neuper, R. Scherer, M. Reiner, and G. Pfurtscheller, Imagery of motor actions: Differential effects of kinesthetic and visual???motor mode of imagery in single-trial EEG, Cognitive Brain Research, vol.25, issue.3, pp.668-77, 2005.
DOI : 10.1016/j.cogbrainres.2005.08.014

N. Kambou, R. Bourdeau, J. Mizoguchi, and R. , Advances in intelligent tutoring systems, 2010.