J. Wolpaw and E. Wolpaw, Brain-computer interfaces: principles and practice

F. Lotte, L. Bougrain, and M. Clerc, Electroencephalography (EEG)-Based Brain-Computer Interfaces, 2015.
DOI : 10.1002/047134608X.W8278

URL : https://hal.archives-ouvertes.fr/hal-01167515

N. Thakor, Translating the Brain-Machine Interface, Science Translational Medicine, vol.5, issue.210, pp.210-227, 2013.
DOI : 10.1126/scitranslmed.3007303

F. Lotte, M. Congedo, and A. Lécuyer, 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

B. Allison and C. Neuper, Could Anyone Use a BCI? Brain-Computer Interfaces

S. Makeig, C. Kothe, and T. Mullen, Evolving Signal Processing for Brain–Computer Interfaces, Proceedings of the IEEE, vol.100, issue.Special Centennial Issue, pp.1567-1584, 2012.
DOI : 10.1109/JPROC.2012.2185009

B. Allison, T. Luth, and D. Valbuena, BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.18, issue.2, pp.107-116, 2009.
DOI : 10.1109/TNSRE.2009.2039495

C. Guger, S. Daban, and E. Sellers, How many people are able to control a P300-based brain???computer interface (BCI)?, Neuroscience Letters, vol.462, issue.1, pp.94-98, 2009.
DOI : 10.1016/j.neulet.2009.06.045

F. Lotte, F. Larrue, and C. Mühl, Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design, Frontiers in Human Neuroscience, vol.7, 2013.
DOI : 10.3389/fnhum.2013.00568

URL : https://hal.archives-ouvertes.fr/hal-00862716

R. Scherer, J. Faller, and D. Balderas, Brain-computer interfacing: more than the sum of its parts. Soft computing, pp.317-331, 2013.

R. Scherer and G. Pfurtscheller, Thought-based interaction with the physical world. Trends in cognitive sciences, pp.490-492, 2013.

A. Kübler, E. Holz, and A. Riccio, The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications, PLoS ONE, vol.8, issue.12, p.112392, 2014.
DOI : 10.1371/journal.pone.0112392.t006

A. Kübler, E. Holz, and E. Sellers, Toward Independent Home Use of Brain-Computer Interfaces: A Decision Algorithm for Selection of Potential End-Users, Archives of Physical Medicine and Rehabilitation, vol.96, issue.3, 2015.
DOI : 10.1016/j.apmr.2014.03.036

C. Zickler, S. Halder, and S. Kleih, Brain Painting: Usability testing according to the user-centered design in end users with severe motor paralysis, Artificial Intelligence in Medicine, vol.59, issue.2
DOI : 10.1016/j.artmed.2013.08.003

F. Nijboer, D. Plass-oude-bos, and Y. Blokland, Design requirements and potential target users for brain-computer interfaces ??? recommendations from rehabilitation professionals, Brain-Computer Interfaces, vol.67, issue.1, pp.50-61, 2014.
DOI : 10.1080/10400435.1999.10131981

J. Huggins, P. Wren, and K. Gruis, What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis, Amyotrophic Lateral Sclerosis, vol.55, issue.5, pp.318-324, 2011.
DOI : 10.1016/S0022-510X(97)00253-0

S. Blain-moraes, R. Schaff, and K. Gruis, Barriers to and mediators of brain???computer interface user acceptance: focus group findings, Ergonomics, vol.59, issue.182, pp.516-525, 2012.
DOI : 10.1016/S1388-2457(02)00057-3

E. Andresen, M. Fried-oken, and B. Peters, Initial constructs for patient-centered outcome measures to evaluate brain???computer interfaces, Disability and Rehabilitation: Assistive Technology, vol.9
DOI : 10.1007/s11136-014-0622-y

J. Huggins, A. Moinuddin, and A. Chiodo, What Would Brain-Computer Interface Users Want: Opinions and Priorities of Potential Users With Spinal Cord Injury, Archives of Physical Medicine and Rehabilitation, vol.96, issue.3, pp.38-45, 2015.
DOI : 10.1016/j.apmr.2014.05.028

B. Peters, G. Bieker, and S. Heckman, Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface meeting. Archives of physical medicine and rehabilitation, pp.33-37, 2015.

E. Holz, L. Botrel, and T. Kaufmann, Long-Term Independent Brain-Computer Interface Home Use Improves Quality of Life of a Patient in the Locked-In State: A Case Study, Archives of Physical Medicine and Rehabilitation, vol.96, issue.3, pp.16-26, 2015.
DOI : 10.1016/j.apmr.2014.03.035

E. Sellers, T. Vaughan, and J. Wolpaw, A brain-computer interface for long-term independent home use, Amyotrophic Lateral Sclerosis, vol.177, issue.5, pp.449-455, 2010.
DOI : 10.1212/01.wnl.0000205136.93011.4e

E. Sellers, D. Ryan, and C. Hauser, Noninvasive brain-computer interface enables communication after brainstem stroke, Science Translational Medicine, vol.6, issue.257, pp.257-264, 2014.
DOI : 10.1126/scitranslmed.3007801

B. Blankertz, C. Sannelli, and S. Halder, Neurophysiological predictor of SMR-based BCI performance, NeuroImage, vol.51, issue.4, pp.1303-1309, 2010.
DOI : 10.1016/j.neuroimage.2010.03.022

M. Ahn, S. Ahn, and J. Hong, Gamma band activity associated with BCI performance: simultaneous MEG/EEG study, Frontiers in Human Neuroscience, vol.7, 2013.
DOI : 10.3389/fnhum.2013.00848

Y. Zhang, P. Xu, and D. Guo, Prediction of SSVEP-based BCI performance by the resting-state EEG network, Journal of Neural Engineering, vol.10, issue.6, p.66017, 2013.
DOI : 10.1088/1741-2560/10/6/066017

S. Saeedi, R. Chavarriaga, R. Leeb, and J. Millán, Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability IEEE Computational Intelligence Magazine, pp.32-61, 2015.

S. Saeedi, R. Chavarriaga, and J. Millán, Long-term stable control of motor-imagery BCI by a locked-in user through adaptive assistance, IEEE Transactions on Neural Systems and Rehabilitation Engineering
DOI : 10.1109/TNSRE.2016.2645681

T. Kaufmann, C. Vögele, and S. Sütterlin, Effects of resting heart rate variability on performance in the P300 brain-computer interface, International Journal of Psychophysiology, vol.83, issue.3, pp.336-341, 2012.
DOI : 10.1016/j.ijpsycho.2011.11.018

S. Sutton, M. Braren, and J. Zubin, Evoked-Potential Correlates of Stimulus Uncertainty, Science, vol.150, issue.3700, pp.1187-1188, 1965.
DOI : 10.1126/science.150.3700.1187

S. Halder, A. Furdea, and B. Varkuti, Auditory standard oddball and visual P300 brain-computer interface performance, International Journal of Bioelectromagnetism, vol.13, issue.1, pp.5-6, 2011.
DOI : 10.1371/journal.pone.0053513

URL : http://doi.org/10.1371/journal.pone.0053513

T. Kaufmann, E. Holz, and A. Kubler, Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state, Frontiers in Neuroscience, vol.7, 2013.
DOI : 10.3389/fnins.2013.00129

E. Hammer, S. Halder, and B. Blankertz, 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. Vukovic, Motor imagery questionnaire as a method to detect BCI illiteracy. Paper presented at, 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2010.

C. Jeunet, N. Kaoua, B. , and L. F. , Advances in User-Training for
URL : https://hal.archives-ouvertes.fr/hal-01302138

A. Riccio, L. Simione, and F. Schettini, Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis, Frontiers in Human Neuroscience, vol.7, 2013.
DOI : 10.3389/fnhum.2013.00732

S. Kleih, F. Nijboer, and S. Halder, Motivation modulates the P300 amplitude during brain???computer interface use, Clinical Neurophysiology, vol.121, issue.7, 2010.
DOI : 10.1016/j.clinph.2010.01.034

I. Käthner, C. Ruf, and E. Pasqualotto, A portable auditory P300 brain???computer interface with directional cues, Clinical Neurophysiology, vol.124, issue.2, pp.327-338, 2013.
DOI : 10.1016/j.clinph.2012.08.006

M. Fried-oken, A. Mooney, and B. Peters, A clinical screening protocol for the RSVP Keyboard brain???computer interface, Disability and Rehabilitation: Assistive Technology, vol.3, issue.1
DOI : 10.1080/09602010443000425

T. Zander, K. Ihme, and M. Gärtner, A public data hub for benchmarking common brain???computer interface algorithms, Journal of Neural Engineering, vol.8, issue.2, p.25021, 2011.
DOI : 10.1088/1741-2560/8/2/025021

C. Ledesma-ramirez, E. Bojorges-valdez, and O. Yáñez-suarez, An Open-Access P300 Speller Database. Paper presented at: Fourth International Brain-Computer Interface Meeting, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00549242

M. Bernabeu, S. Laxe, and R. Lopez, Developing Core Sets for Persons With Traumatic Brain Injury Based on the International Classification of Functioning, Disability, and Health, Neurorehabilitation and Neural Repair, vol.23, issue.5, pp.464-467, 2009.
DOI : 10.1177/1545968308328725

S. Schwarzkopf, T. Ewert, and K. Dreinhofer, Towards an ICF Core Set for chronic musculoskeletal conditions: commonalities across ICF Core Sets for osteoarthritis, rheumatoid arthritis, osteoporosis, low back pain and chronic widespread pain, Clinical Rheumatology, vol.36, issue.S44, pp.1355-1361, 2008.
DOI : 10.1007/s10067-008-0916-y

A. Cieza, T. Ewer, and B. Ustun, Development of ICF core sets for patients with chronic conditions, J Rehabil Med, 2004.

B. Peters, A. Mooney, and B. Oken, Soliciting BCI user experience feedback from people with severe speech and physical impairments, Brain-Computer Interfaces, vol.15, issue.4
DOI : 10.1016/j.jclinepi.2010.04.011

R. Scherer, J. Wagner, and M. Billinger, Augmenting communication, emotion expression and interaction capabilities of individuals with cerebral palsy. Paper presented at: 2014 6th International Brain-Computer Interface Conference, pp.312-315, 2014.

I. Daly, M. Billinger, and J. Laparra-hernández, On the control of brain-computer interfaces by users with cerebral palsy, Clinical Neurophysiology, vol.124, issue.9, pp.1787-1797, 2013.
DOI : 10.1016/j.clinph.2013.02.118

R. Scherer, M. Billinger, and J. Wagner, Thought-based row-column scanning communication board for individuals with cerebral palsy, Annals of Physical and Rehabilitation Medicine, vol.58, issue.1, pp.14-22, 2015.
DOI : 10.1016/j.rehab.2014.11.005

URL : http://dx.doi.org/10.1016/j.rehab.2014.11.005

R. Scherer, A. Schwarz, and G. Müller-putz, Game-based BCI training: Interactive design for individuals with cerebral palsy. Paper presented at, IEEE International Conference on Systems, Man, and Cybernetics, p.2015, 2015.

R. Scherer, A. Schwarz, and G. Müller-putz, Let's play Tic-Tac-Toe: A

N. Neumann and A. Kubler, Training locked-in patients: a challenge for the use of brain~computer interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.11, issue.2, pp.169-172, 2003.
DOI : 10.1109/TNSRE.2003.814431

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

J. Wander, T. Blakely, and K. Miller, Distributed cortical adaptation during learning of a brain-computer interface task, Proceedings of the National Academy of Sciences, vol.110, issue.26, pp.10818-10841, 2013.
DOI : 10.1073/pnas.1221127110

C. Neuper, R. Scherer, and M. Reiner, 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
DOI : 10.1016/j.cogbrainres.2005.08.014

F. Lotte and C. Jeunet, Towards Improved BCI based on Human Learning Principles. Paper presented at: 3rd International Brain-Computer Interfaces Winter Conference, 2015.
DOI : 10.1109/iww-bci.2015.7073024

URL : https://hal.archives-ouvertes.fr/hal-01111843

V. Shute, Focus on Formative Feedback, Review of Educational Research, vol.78, issue.1, pp.153-189, 2008.
DOI : 10.3102/0034654307313795

J. Hattie and H. Timperley, The Power of Feedback, Review of Educational Research, vol.77, issue.1, pp.81-112, 2007.
DOI : 10.3102/003465430298487

M. Merrill, First principles of instruction, instructional design and technology, pp.62-71, 2007.
DOI : 10.1007/BF02505024

C. Jeunet, E. Jahanpour, and L. F. , Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study, Journal of Neural Engineering, vol.13, issue.3, p.36024, 2016.
DOI : 10.1088/1741-2560/13/3/036024

URL : https://hal.archives-ouvertes.fr/hal-01302154

C. Jeunet, N. Kaoua, B. , N. Kambou, and R. , Why and How to Use Intelligent Tutoring Systems to Adapt MI-BCI Training to Each User?, Paper presented at: International BCI meeting, 2016.

C. Jeunet, N. Kaoua, B. Subramanian, and S. , Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns, PLOS ONE, vol.25, issue.1, p.143962, 2015.
DOI : 10.1371/journal.pone.0143962.g008

URL : https://hal.archives-ouvertes.fr/hal-01177685

S. Kleih and A. Kübler, Psychological Factors Influencing Brain-Computer Interface (BCI) Performance. Paper presented at, IEEE International Conference on Systems, pp.3192-3196, 2015.
DOI : 10.1109/smc.2015.554

C. Jeunet, C. Vi, and D. Spelmezan, Continuous Tactile Feedback for Motor-Imagery based Brain-Computer Interaction in a Multitasking Context. Paper presented at, Proc. Interact, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01159146

M. Gomez-rodriguez, J. Peters, and J. Hill, Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery, Journal of Neural Engineering, vol.8, issue.3, p.36005, 2011.
DOI : 10.1088/1741-2560/8/3/036005

T. Sollfrank, A. Ramsay, and S. Perdikis, The effect of multimodal and enriched feedback on SMR-BCI performance, Clinical Neurophysiology, vol.127, issue.1, pp.490-498, 2016.
DOI : 10.1016/j.clinph.2015.06.004

F. Lotte, J. Faller, and C. Guger, Combining BCI with Virtual Reality: Towards New Applications and Improved BCI Towards Practical Brain-Computer Interfaces, pp.197-220, 2013.
DOI : 10.1007/978-3-642-29746-5_10

URL : https://hal.inria.fr/hal-00735932/file/Chap_BCI_VR.pdf

N. Birbaumer, N. Ghanayim, and T. Hinterberger, A spelling device for the paralysed, Nature, vol.398, issue.6725, pp.297-298, 1999.
DOI : 10.1038/18581

?. Brain and . Computer-interface, Fast acquisition of effective performance in untrained subjects, NeuroImage, vol.37, issue.2, pp.539-550, 2007.

S. Mason, A. Bashashati, and M. Fatourechi, A Comprehensive Survey of Brain Interface Technology Designs, Annals of Biomedical Engineering, vol.10, issue.3, pp.137-69, 2007.
DOI : 10.1007/s10439-006-9170-0

C. Vidaurre, M. Kawanabe, V. Bünau, and P. , Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces, IEEE Transactions on Biomedical Engineering, vol.58, issue.3, pp.587-97, 2011.
DOI : 10.1109/TBME.2010.2093133

J. Faller, C. Vidaurre, and T. Solis-escalante, Autocalibration and Recurrent Adaptation: Towards a Plug and Play Online ERD-BCI, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.20, issue.3, pp.313-319, 2012.
DOI : 10.1109/TNSRE.2012.2189584

J. Faller, R. Scherer, and U. Costa, A Co-Adaptive Brain-Computer Interface for End Users with Severe Motor Impairment, PLoS ONE, vol.43, issue.7
DOI : 10.1371/journal.pone.0101168.t002

URL : http://doi.org/10.1371/journal.pone.0101168

W. Samek, F. Meinecke, and K. Müller, 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

S. Perdikis, R. Leeb, and J. Millán, Subject-oriented training for motor imagery brain-computer interfaces. Paper presented at, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1259-1262, 2014.
DOI : 10.1109/embc.2014.6943826

URL : http://infoscience.epfl.ch/record/200202

R. Kobler and R. Scherer, Restricted Boltzmann Machines in Sensory Motor Rhythm Brain-Computer Interfacing: A study on inter-subject transfer and co-adaptation, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016.
DOI : 10.1109/SMC.2016.7844284

H. Cecotti, A Self-Paced and Calibration-Less SSVEP-Based Brain–Computer Interface Speller, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.18, issue.2, pp.127-133, 2010.
DOI : 10.1109/TNSRE.2009.2039594

P. J. Kindermans, M. Schreuder, B. Schrauwen, K. R. Müller, and M. Tangermann, True Zero-Training Brain-Computer Interfacing ??? An Online Study, PLoS ONE, vol.2010, issue.7
DOI : 10.1371/journal.pone.0102504.g009

E. Baykara, C. A. Ruf, C. Fioravanti, I. Käthner, N. Simon et al., Effects of training and motivation on auditory P300 brain???computer interface performance, Clinical Neurophysiology, vol.127, issue.1, pp.379-387, 2016.
DOI : 10.1016/j.clinph.2015.04.054

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2009.

O. Ledoit and M. Wolf, Honey, I Shrunk the Sample Covariance Matrix, The Journal of Portfolio Management, vol.30, issue.4, pp.110-119, 2004.
DOI : 10.3905/jpm.2004.110

S. Lemm, B. Blankertz, T. Dickhaus, and K. Müller, Introduction to machine learning for brain imaging, NeuroImage, vol.56, issue.2, pp.387-399, 2011.
DOI : 10.1016/j.neuroimage.2010.11.004

F. Lotte, Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces, Proceedings of the IEEE, vol.103, issue.6, pp.871-890, 2015.
DOI : 10.1109/JPROC.2015.2404941

B. Blankertz, R. Tomioka, and S. Lemm, Optimizing Spatial filters for Robust EEG Single-Trial Analysis, IEEE Signal Processing Magazine, vol.25, issue.1, pp.41-56, 2008.
DOI : 10.1109/MSP.2008.4408441

R. Xu, N. Jiang, N. Mrachacz-kersting, K. Dremstrup, and D. Farina, Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation, Frontiers in Neuroscience, vol.61, issue.1033, 2015.
DOI : 10.1109/TBME.2014.2312397

F. Lotte, A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain???Computer Interfaces, Guide to Brain-Computer Music Interfacing, pp.133-161, 2014.
DOI : 10.1007/978-1-4471-6584-2_7

URL : https://hal.archives-ouvertes.fr/hal-01055103

H. Ramoser, J. Müller-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

W. Samek, M. Kawanabe, and K. Muller, Divergence-Based Framework for Common Spatial Patterns Algorithms, IEEE Reviews in Biomedical Engineering, vol.7, pp.50-72, 2013.
DOI : 10.1109/RBME.2013.2290621

M. Seeber, R. Scherer, and J. Wagner, EEG beta suppression and low gamma modulation are different elements of human upright walking, Frontiers in Human Neuroscience, vol.8, 2014.

M. Seeber, R. Scherer, and J. Wagner, High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle, NeuroImage, vol.112, pp.318-344, 2015.
DOI : 10.1016/j.neuroimage.2015.03.045

E. Friedrich, R. Scherer, and C. Neuper, The effect of distinct mental strategies on classification performance for brain???computer interfaces, International Journal of Psychophysiology, vol.84, issue.1, pp.86-94, 2012.
DOI : 10.1016/j.ijpsycho.2012.01.014

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, 2013.
DOI : 10.1371/journal.pone.0076214.t002

R. Scherer, J. Faller, and E. Friedrich, Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability, PLOS ONE, vol.25, issue.1
DOI : 10.1371/journal.pone.0123727.t001

URL : http://doi.org/10.1371/journal.pone.0123727

R. Scherer, J. Faller, and E. Opisso, Bring mental activity into action! Self-tuning brain-computer interfaces, Paper presented at: 2015 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015.
DOI : 10.1109/embc.2015.7318858

M. Fatourechi, A. Bashashati, and R. Ward, EMG and EOG artifacts in brain computer interface systems: A survey, Clinical Neurophysiology, vol.118, issue.3, 2007.
DOI : 10.1016/j.clinph.2006.10.019

A. Schlögl, C. Keinrath, and D. Zimmermann, A fully automated correction method of EOG artifacts in EEG recordings, Clinical Neurophysiology, vol.118, issue.1, pp.98-104, 2007.
DOI : 10.1016/j.clinph.2006.09.003

I. Daly, R. Scherer, and M. Billinger, FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.23, issue.5, pp.725-736, 2015.
DOI : 10.1109/TNSRE.2014.2346621

G. Pfurtscheller, A. B. Bauernfeind, and G. , The hybrid BCI Front
DOI : 10.3389/fnpro.2010.00003

URL : http://doi.org/10.3389/fnpro.2010.00003

R. Leeb, H. Sagha, R. Chavarriaga, and J. Millán, A hybrid brain???computer interface based on the fusion of electroencephalographic and electromyographic activities, Journal of Neural Engineering, vol.8, issue.2, p.25011, 2011.
DOI : 10.1088/1741-2560/8/2/025011

G. Müller-putz, R. Leeb, and M. Tangermann, Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond, Proceedings of the IEEE, vol.103, issue.6, pp.926-943, 2015.
DOI : 10.1109/JPROC.2015.2411333

J. Urigüen and B. Garcia-zapirain, EEG artifact removal???state-of-the-art and guidelines, Journal of Neural Engineering, vol.12, issue.3, p.31001, 2015.
DOI : 10.1088/1741-2560/12/3/031001

T. Fawcett, An introduction to ROC analysis Pattern Recognition Letters, pp.861-874, 2006.
DOI : 10.1016/j.patrec.2005.10.010

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

J. Wolpaw, N. Birbaumer, and W. Heetderks, Brain-computer interface technology: a review of the first international meeting, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.2, pp.164-173, 2000.
DOI : 10.1109/TRE.2000.847807

L. Quitadamo, M. Abbafati, and G. Cardarilli, Evaluation of the performances of different P300 based brain???computer interfaces by means of the efficiency metric, Journal of Neuroscience Methods, vol.203, issue.2, pp.361-368, 2012.
DOI : 10.1016/j.jneumeth.2011.10.010

E. Thomas, M. Dyson, and M. Clerc, An analysis of performance evaluation for motor-imagery based BCI, Journal of Neural Engineering, vol.10, issue.3, p.31001, 2013.
DOI : 10.1088/1741-2560/10/3/031001

URL : https://hal.archives-ouvertes.fr/hal-00821971

J. Antelis, L. Montesano, and A. Ramos-murguialday, On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals, PLoS ONE, vol.25, issue.4, p.61976, 2013.
DOI : 10.1371/journal.pone.0061976.g009

M. Spüler, A. Sarasola-sanz, and N. Birbaumer, Comparing metrics to evaluate performance of regression methods for decoding of neural signals, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI : 10.1109/EMBC.2015.7318553

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

E. Maris and R. Oostenveld, Nonparametric statistical testing of EEG- and MEG-data, Journal of Neuroscience Methods, vol.164, issue.1, pp.177-190, 2007.
DOI : 10.1016/j.jneumeth.2007.03.024

R. Wasserstein and N. Lazar, The ASA's statement on p-values: context, process, and purpose The American Statistician, pp.129-133, 2016.

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.55, 2010.
DOI : 10.3389/fnins.2010.00055

L. Mccane, E. Sellers, and D. Mcfarland, Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. Amyotrophic lateral sclerosis and frontotemporal degeneration, pp.3-4207, 2014.

A. Schlögl, J. Kronegg, and J. Huggins, Evaluation Criteria for BCI Research Toward brain-computer interfacing, 2007.

D. Seno, B. Matteucci, M. Mainardi, and L. , The Utility Metric: A Novel Method to Assess the Overall Performance of Discrete Brain–Computer Interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.18, issue.1, pp.20-28, 2010.
DOI : 10.1109/TNSRE.2009.2032642

N. Hill, A. Häuser, and G. Schalk, A general method for assessing brain???computer interface performance and its limitations, Journal of Neural Engineering, vol.11, issue.2, p.26018, 2014.
DOI : 10.1088/1741-2560/11/2/026018

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4113089

A. Riccio, F. Leotta, and L. Bianchi, Workload measurement in a communication application operated through a P300-based brain???computer interface, Journal of Neural Engineering, vol.8, issue.2, p.25028, 2011.
DOI : 10.1088/1741-2560/8/2/025028

S. Hart and L. Staveland, Development of NASA-TLX (Task Load Index

R. Nuzzo, How scientists fool themselves ??? and how they can stop, Nature, vol.526, issue.7572, pp.182-185, 2015.
DOI : 10.1038/526182a

A. Brouwer, T. Zander, and J. Van-erp, Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls, Frontiers in Neuroscience, vol.9, p.136, 2015.
DOI : 10.3389/fnins.2015.00136

URL : http://doi.org/10.3389/fnins.2015.00136

C. Duncan, R. Barry, and J. Connolly, Event-related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400, Clinical Neurophysiology, vol.120, issue.11, pp.1883-1908, 2009.
DOI : 10.1016/j.clinph.2009.07.045

R. Kass, B. Caffo, and M. Davidian, Ten Simple Rules for Effective Statistical Practice, PLOS Computational Biology, vol.531, issue.151, p.1004961, 2016.
DOI : 10.1371/journal.pcbi.1004961

URL : http://doi.org/10.1371/journal.pcbi.1004961

M. Scherer and G. Craddock, Matching person & technology (MPT) assessment process, Technology and Disability, vol.14, issue.3, pp.125-131, 2002.
DOI : 10.1037/e315622004-001

M. Scherer, J. Jutai, and M. Fuhrer, A framework for modelling the selection of assistive technology devices (ATDs), Disability and Rehabilitation: Assistive Technology, vol.50, issue.2, pp.1-8, 2007.
DOI : 10.1037/10629-000