.. .. Experimental-setup,

, 94 6.2.3 Experiment #3: Mother Wavelet (Selection)

. .. Temporal-window-selection, 98 6.4.1 Experiment #6: SEWS-1 and SEWS-2 algorithms

, The rst part aims to compare dierent methodologies used by the BCI scientic community. Then, experiments to evaluate the performance of the new methods

. Ledesma-ramirez, The only toolbox used outside the MATLAB distribution is the library for Support Vector Machines, LIBSVM, recorded by the Neuroimaging Laboratory of the Universidad Autónoma Metropolitana (UAM), Mexico, using the P300 speller Bibliography Abootalebi, vol.62, p.309320, 2001.

V. Abootalebi, M. H. Moradi, and M. A. Khalilzadeh, A new approach for EEG feature extraction in P300-based lie detection, Computer Methods and Programs in Biomedicine, vol.94, p.4857, 2009.

A. Aizerman, E. M. Braverman, and L. I. Rozoner, Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, vol.25, p.821837, 1964.

C. Anderson, Taxonomy of feature extraction and translation methods for BCI, 2005.

A. Antoniadis, Wavelet methods in statistics: Some recent developements and their applications, Statistics Surveys, vol.1, p.1655, 2007.

A. Antoniadis, J. Bigot, and T. Sapatinas, Wavelet estimators in nonparametric regression: a comparative simulation study, Journal of Statistical Software, vol.6, issue.6, p.183, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00823485

E. Ba³ar, M. Schürmann, T. Demiralp, C. Ba³ar-eroglu, and A. Ademoglu, Eventrelated oscillations arèreal brain responses' -wavelet analysis and new strategies, International Journal of Psychophysiology, vol.39, p.91127, 2001.

A. Bashashati, M. Fatourechi, R. K. Ward, and G. E. Birch, A survey of signal processing algorithms in braincomputer interfaces based on electrical brain signals, Journal of Neural Engineering, vol.4, pp.32-57, 2007.

J. D. Bayliss and D. H. Ballard, A Virtual Reality Testbed for Brain-Computer Interface Research, IEEE Transaction on Rehabilitation Engineering, vol.8, p.188190, 2000.

R. B. Bendel, A. , and A. A. , Comparison of stopping rules in forward stepwise regression, Journal of the American Statistical Association, vol.72, p.4653, 1977.

H. Berger, Über das elektrenkephalogramm des menschen, European Archives of Psychiatry and Clinical Neuroscience, vol.87, p.527570, 1929.

D. L. Bihan and E. Breton, Imagerie de diusion in-vivo par résonance magnétique nucléaire, C. R. Acad. Sci, vol.301, issue.15, p.11091112, 1985.

G. Bin, X. Gao, Y. Wang, B. Hong, and S. Gao, VEP-based Brain-Computer Interfaces: Time, Frequency, and Code modulations, Comp. Intell. Mag, vol.4, issue.4, p.2226, 2009.

N. Birbaumer, Slow Cortical Potentials: Plasticity, Operant Control, and Behavioral Eects, vol.5, p.7478, 1999.

N. Birbaumer, T. Hinterberger, A. Kübler, and N. Neumann, The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.11, p.120123, 2003.

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 2006.

M. Bocher, Introduction to the Theory of Fourier's Series, Annals of Mathematics, vol.7, issue.3, p.81152, 1906.

R. Boostani, B. Graimann, M. H. Moradi, and G. Pfurtscheller, A comparison approach toward nding the best feature and classier in cue-based BCI, Medical and Biological Engineering and Computing, vol.45, p.403412, 2007.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A Training Algorithm for Optimal Margin Classiers, Proceedings of the fth annual workshop on Computational learning theory, p.144152, 1992.

L. Bougrain, C. Saavedra, and R. Ranta, Finally, what is the best lter for P300 detection?, Proceedings of the 3rd TOBI Workshop, 2012.

S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral, and K. Pourrezaei, Functional Near-Infrared Spectroscopy, IEEE Engineering in Medicine and Biology Magazine, vol.25, p.5462, 2006.

T. Cai and B. Silverman, Incorporating Information on Neighbouring Coecients into Wavelet Estimation, Sankhya: The Indian Journal of Statistics, vol.63, p.127148, 2001.

E. K. Chadwick, D. Blana, J. D. Simeral, J. Lambrecht, S. P. Kim et al., Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia, Journal of Neural Engineering, vol.8, issue.3, p.34003, 2011.

C. Chang and C. Lin, LIBSVM: a library for support vector machines, 2001.

O. Chapelle, Training a Support Vector Machine in the Primal, Neural Comput, vol.19, issue.5, p.11551178, 2007.

G. E. Chatrian, L. Bergamini, and M. Dondey, A glossary of terms most commonly used by clinical electroencephalographers, Electroencephalographic Clinical Neurophysiology, vol.37, p.538553, 1974.

G. E. Chatrian, E. Lettich, N. , and P. , Ten percent electrode system for topographic studies of spontaneous and evoked EEG activity, American Journal Of EEG Technology, vol.25, p.8392, 1985.

D. Cohen, Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer, Science, vol.175, p.664666, 1972.

J. Cohen and J. Polich, On the number of trials needed for P300, International Journal of Psychophysiology, vol.25, p.249255, 1997.

G. Cooper, Wavelet-based semblance ltering, Computers & Geosciences, vol.35, issue.10, 2009.

G. Cooper and D. Cowan, Wavelet-based semblance analysis, Computers & Geosciences, vol.34, issue.2, p.95102, 2008.

C. Cortes and V. Vapnik, Support-Vector Networks, Machine Learning, vol.20, p.273297, 1995.

K. Crammer and Y. Singer, On the Algorithmic Implementation of Multiclass Kernelbased Vector Machines, Journal of Machine Learning Research, vol.2, p.265292, 2001.

I. Daubechies, Ten Lectures on Wavelets. pub-siam, 1992.

H. Davis, P. A. Davis, A. L. Loomis, E. N. Hervey, H. et al., Electrical reactions of the human brain to auditory stimulation during sleep, Journal of Neurophysiology, vol.2, pp.500-514, 1939.

J. Davis, Statistics and Data Analysis in Geology, 1986.

S. P. Dear and C. B. Hart, Synchronized Cortical Potentials and Wavelet Packets: A Potential Mechanism for Perceptual Binding and Conveying Information, Brain and Language, vol.66, 1999.

J. M. Delgado, Agression and defense under cerebral radio control, UCLA Forum Med Sci, vol.7, p.171193, 1967.

T. Demiralp, A. Ademoglub, Y. Istefanopulos, C. Basar-eroglu, and E. Basar, Wavelet analysis of oddball P300, International Journal of Psychophysiology, vol.39, p.221227, 2001.

T. G. Dietterich and G. Bakiri, , 1995.

, Solving Multiclass Learning Problems via ErrorCorrecting Output Codes, J. Artif. Int. Res, vol.2, issue.1, p.263286

E. Donchin, K. M. Spencer, and R. Wijesinghe, The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface, IEEE Transaction on Rehabilitation Engineering, vol.8, p.174179, 2000.

D. Donoho and I. Johnstone, Minimax Estimation via Wavelet Shrinkage, 1992.

D. Donoho and I. Johnstone, Ideal spatial adaptation via wavelet shrinkage, Biometrika, vol.81, p.425455, 1994.

D. Donoho and I. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, vol.90, p.12001224, 1995.

D. L. Donoho and I. M. Johnstone, Minimax Estimation via Wavelet Shrinkage, The Annals of Statistics, vol.26, issue.3, p.879921, 1998.

N. Draper and H. Smith, Applied Regression Analysis, 1981.

K. Duan and S. S. Keerthi, Which Is the Best Multiclass SVM Method? An Empirical Study, Proceedings of the Sixth International Workshop on Multiple Classier Systems, 2005.

R. Duda, P. Hart, and D. Stork, , 2001.

J. Fan, Comments on wavelets in statistics: A review by a. antoniadis, Journal of the Italian Statistical Society, vol.6, issue.2, p.131138, 1997.

L. Farwell and E. Donchin, Talking o the top of your head: toward a mental prosthesis utilizing event-related brain potentials, Electroencephalogr Clin Neurophysiol, vol.70, issue.6, p.510523, 1988.

R. Fazel-rezai, Human Error in P300 Speller Paradigm for Brain-Computer Interface, The 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol.29, p.25162519, 2007.

J. Fell, T. Dietl, T. Grunwald, M. Kurthen, P. Klaver et al., Neural Bases of Cognitive ERPs: More than Phase Reset, Journal of Cognitive Neuroscience, vol.16, p.15951604, 2004.

A. Finke, A. Lenhardt, and H. Ritter, The MindGame: A P300-based brain-computer interface game, Neural Networks, vol.22, p.13291333, 2009.

R. A. Fisher, The Use of Multiple Measurements in Taxonomic Problems, Annals of Eugenics, vol.7, p.179188, 1936.

D. Gabor, Theory of communication. Part 1: The analysis of information, Journal of the IEE, vol.93, p.429457, 1946.

H. Gibbons and J. Stahl, Response-time corrected averaging of event-related potentials, Clinical Neurophysiology, vol.118, 2007.

C. Guan, M. Thulasidas, and J. Wu, High performance P300 speller for brain-computer interface, IEEE Biomedical Circuits and Systems, 2004.

Y. Guermeur, A generic model of multi-class support vector machine, International Journal of Intelligent Information and Database Systems (IJIIDS), vol.6, issue.6, p.555577, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00596175

C. Guger, S. Daban, E. Sellers, C. Holzner, G. Krausz et al., How many people are able to control a P300-based brain-computer interface (BCI)?, Neuroscience Letters, vol.462, p.9498, 2009.

C. N. Gupta, Y. U. Khan, R. Palaniappan, and F. Sepulveda, Wavelet Framework for Improved Target Detection in Oddball Paradigms Using P300 and Gamma Band Analysis, 2009.

, Biomedical Soft Computing and Human Sciences, vol.14, issue.2, p.6167

B. Hamadicharef, M. Xu, A. , and S. , Brain-Computer Interface (BCI) based Musical Composition, Proceedings of the 2010 International Conference on Cyberworlds, 2010.

L. R. Hochberg and J. P. Donoghue, Sensors for Brain-Computer Interfaces, IEEE Engineering in Medicine and Biology Magazine, vol.25, issue.5, p.3238, 2006.

R. R. Hocking, The Analysis and Selection of Variables in Linear Regression, Biometrics, vol.32, issue.1, p.149, 1976.

U. Homann, J. Vesin, T. Ebrahimi, and K. Diserens, An ecient P300-based brain-computer interface for disabled subjects, Journal of Neuroscience Methods, vol.167, issue.1, p.125, 2008.

L. Holman and S. S. Tumeh, Single-Photon Emission Computed Tomography (SPECT) Applications and Potential, JAMA, vol.263, issue.4, p.561564, 1990.

C. Hsu and C. Lin, A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions on neural networks, vol.13, p.415425, 2002.

S. A. Huettel and G. Mccarthy, What is odd in the oddball task?: Prefrontal cortex is activated by dynamic changes in response strategy, Neuropsychologia, vol.42, issue.3, p.379386, 2004.

H. Hwang, K. Kwon, and C. Im, Neurofeedback-based motor imagery training for brain-computer interface (BCI), Journal of Neuroscience Methods, vol.179, p.150156, 2009.

I. Iversen, N. Ghanayim, A. Kübler, N. Neumann, N. Birbaumer et al., A brain-computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis, Clinical Neurophysiology, vol.119, p.22142223, 2008.

M. M. Jackson, K. Ozawa, K. Kido, I. Mcclendon, and R. Kerwin, Field Study of an fNIR-Based Brain-Computer Interface for Communication, Proceedings of the Fifth International Brain-Computer Interface Meeting, 2013.

H. H. Jasper, The ten-twenty electrode system of the international federation, Electroencephalography and Clinical Neurophysiology, vol.10, issue.2, p.371375, 1958.

T. Kaufmann, S. Schulz, C. Grünzinger, and A. Kübler, Flashing Characters with Famous Faces Improves ERP-based Brain Computer Interface Performance, Journal of Neural Engineering, vol.8, p.56016, 2011.

P. R. Kennedy, R. A. Bakay, M. M. Moore, K. Adams, and J. Goldwaithe, Direct Control of a Computer from the Human Central Nervous System, IEEE Transactions on Rehabilitation Engineering, vol.8, 2000.

P. R. Kennedy, S. S. Mirra, and R. A. Bakay, The cone electrode: ultrastructural studies following long-term recording in rat and monkey cortex, Neuroscience Letters, vol.142, p.8994, 1992.

J. Kissler, C. Herbert, I. Winkler, and M. Junghofer, Emotion and attention in visual word processing: an ERP study, Biol Psychol, vol.80, issue.1, p.7583, 2009.

W. Klimesch, P. Sauseng, S. Hanslmayr, W. Gruber, and R. Freunberger, Eventrelated phase reorganization may explain evoked neural dynamics, Neuroscience and Biobehavioral Reviews, vol.31, p.10031016, 2007.
DOI : 10.1016/j.neubiorev.2007.03.005

M. Koivisto and A. Revonsuo, Event-related brain potential correlates of visual awareness, Neuroscience & Biobehavioral Reviews, vol.34, p.922934, 2010.

V. Kolev, T. Demiralp, J. Yordanova, A. Ademoglu, and U. Isoglu-alkaç, TimeFrequency Analysis Reveals Multiple Functional Components During Oddball P300, NeuroReport, vol.8, p.20612065, 1997.
DOI : 10.1097/00001756-199705260-00050

D. Krusienski, E. Sellers, F. Cabestaing, S. Bayoudh, D. Mcfarland et al., A comparison of classication techniques for the P300 speller, Journal of neural engineering, vol.3, p.299305, 2006.

D. Krusienski, E. Sellers, D. Mcfarland, T. Vaughan, and J. Wolpaw, Toward enhanced P300 speller performance, Journal of Neuroscience Methods, vol.167, p.1521, 2008.
DOI : 10.1016/j.jneumeth.2007.07.017

URL : http://europepmc.org/articles/pmc2349091?pdf=render

D. J. Krusienski, G. Schalk, D. J. Mcfarland, and J. R. Wolpaw, A mu-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface, IEEE Transactions on Biomedical Engineering, vol.54, p.273280, 2007.

A. Kübler, B. Kotchoubey, T. Hinterberger, N. Ghanayim, J. Perelmouter et al., The thought translation device: a neurophysiological approach to communication in total motor paralysis, Experimental Brain Research, vol.124, p.223232, 1999.

A. Kübler, B. Kotchoubey, H. Salzmann, N. Ghanayim, J. Perelmouter et al., Self-regulation of slow cortical potentials in completely paralyzed human patients, Neuroscience Letters, vol.252, p.171174, 1998.

T. N. Lal, T. Hinterberger, G. Widman, M. Schroeder, J. Hill et al., Methods Towards Invasive Human Brain Computer Interfaces. Advances in Neural Information Processing System, vol.17, p.737744, 2005.

C. Ledesma-ramirez, E. Bojorges-valdez, O. Yanez-suarez, C. Saavedra, L. Bougrain et al., An open-Access P300 Speller Database, BCI meeting, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00549242

Y. Lee, Y. Lin, and G. Wahba, Multicategory Support Vector Machines: Theory and Application to the Classication of Microarray Data and Satellite Radiance Data, Journal of the American Statistical Association, vol.99, issue.465, p.6781, 2004.

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, p.25011, 2011.

S. Lemm, K. Müller, and G. Curio, A Generalized Framework for Quantify, 2009.

, ing the Dynamics of EEG Event-Related Desynchronization, PLOS Computational Biology, vol.5, issue.8, p.1000453

M. S. Lewicki, A review of methods for spike sorting: the detection and classication of neural action potentials, Network: Computation in Neural Systems, vol.9, p.5378, 1998.

J. Liu and W. T. Newsome, Local Field Potential in Cortical Area MT: Stimulus Tuning and Behavioral Correlations, The Journal of neuroscience : the ocial journal of the Society for Neuroscience, vol.26, p.77797790, 2006.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, A. et al., A review of classication algorithms for EEG-based brain-computer interfaces, Journal of Neural Engineering, vol.4, pp.1-13, 2007.

F. Lotte, R. Scherer, and A. Lécuyer, A:8 Teaching the BCI Skill: Feedback and Human Training Approaches. BCI meeting 2013 workshop, 2013.

S. J. Luck, An Introduction to the Event-Related Potential Technique, 2005.

W. Luo, W. Feng, W. He, N. Wang, and Y. Luo, Three stages of facial expression processing: ERP study with rapid serial visual presentation, NeuroImage, vol.49, issue.2, p.18571867, 2010.

J. N. Mak and J. R. Wolpaw, Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects, IEEE Reviews in Biomedical Engineering, vol.2, p.187199, 2009.

S. Makeig, T. Jung, A. J. Bell, D. Ghahremani, and T. J. Sejnowski, Blind separation of auditory event-related brain responses into independent components, Proc. Natl. Acad. Sci, vol.94, p.1097910984, 1997.

S. Mallat, Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.7, p.674693, 1989.

S. Mallat, A Wavelet Tour of Signal Processing, 2008.

J. Malmivuo and R. Robert-plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields, 1995.

M. Marchetti, F. Piccione, S. Silvoni, and K. Priftis, Exogenous and endogenous orienting of visuospatial attention in P300-guided brain computer interfaces: A pilot study on healthy participants, Clinical Neurophysiology, vol.123, p.774779, 2012.

S. G. Mason, A. Bashashati, M. Fatourechi, K. F. Navarro, and G. E. Birch, A Comprehensive Survey of Brain Interface Technology Designs, Annals of Biomedical Engineering, vol.35, p.137169, 2007.

S. G. Mason and G. E. Birch, A General Framework for Brain-Computer Interface Design, EEE Transactions on Neural Systems and Rehabilitation Engineering, vol.11, p.7085, 2003.

S. Matsuoka, Theta Rhythms: State of Consciousness, Brain Topography, vol.3, p.203208, 1990.

D. J. Mcfarland, C. W. Anderson, K. Müller, A. Schlögl, and D. J. Krusienski, , 2006.

, BCI Meeting 2005Workshop on BCI Signal Processing: Feature Extraction and Translation

, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, issue.2, p.135137

J. Mellinger, G. Schalk, C. Braun, H. Preissl, W. Rosenstiel et al., An MEG-based brain-computer interface (BCI), NeuroImage, vol.36, p.581593, 2007.

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K. R. Mullers, Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX, Proceedings of the 1999 IEEE Signal Processing Society Workshop, p.4148, 1999.

S. Mori and P. B. Barker, Diusion Magnetic Resonance Imaging: Its Principle and Applications, The Anatomical Record, vol.257, p.102109, 1999.

E. Mugler, M. Bensch, S. Halder, W. Rosenstiel, M. Bogdan et al., Control of an Internet Browser Using the P300 Event-Related Potential, International Journal of Bioelectromagnetism, vol.10, p.5663, 2008.

R. Mukamel and I. Fried, Human Intracranial Recordings and Cognitive Neuroscience, Annual Review of Psychology, vol.63, p.511537, 2012.

K. Müller, M. Krauledat, G. Dornhege, G. Curio, and B. Blankertz, Machine Learning Techniques for Brain-computer Interfaces, vol.49, p.1122, 2004.

J. I. Münssinger, S. Halder, S. C. Kleih, A. Furdea, V. Raco et al., Brain Painting: First Evaluation of a New Brain-Computer Interface Application with ALS-Patients and Healthy Volunteers, Front. Neurosci, vol.4, p.182, 2010.

A. J. Myrden, A. Kushki, E. Sejdic, A. Guerguerian, C. et al., , 2011.

, Brain-Computer Interface Based on Bilateral Transcranial Doppler Ultrasound. PLoS ONE, vol.6, issue.9, p.24170

R. Näätänen, Attention and Brain Function, 1992.

D. R. Nair, R. Burgess, C. C. Mcintyre, and H. Lüders, Chronic subdural electrodes in the management of epilepsy, Clinical Neurophysiology, vol.119, p.1128, 2008.

C. S. Nam, Y. Jeon, Y. Li, Y. Kim, and H. Yoon, Usability of the P300 Speller: Towards a More Sustainable Brain-Computer Interface, vol.1, p.111125, 2009.

A. Y. Ng and M. I. Jordan, On Discriminative vs. Generative classiers: A comparison of logistic regression and naive Bayes, NIPS, p.841848, 2001.

E. Niedermeyer and F. L. Da-silva, Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 2005.

F. Nijboer, N. Birbaumer, and A. Kübler, The inuence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis -a longitudinal study, 2010.

A. Nijholt, BCI for Games: A `State of the Art' Survey, Proceedings of the 7th International Conference on Entertainment Computing, p.225228, 2009.

A. A. Nooh, J. Yunus, and S. M. Daud, A Review of Asynchronous, 2011.

, Electroencephalogram-based Brain Computer Interface Systems, Biomedical Engineering and Technology IPCBEE, vol.11, p.5559

M. Nuwer, C. Comi, R. Emerson, A. Fuglsang-frederiksen, J. Guérit et al., IFCN standards for digital recording of clinical EEG, Electroencephalography and clinical Neurophysiology, vol.106, p.259261, 1998.

J. V. Odom, M. Bach, C. Barber, M. Brigell, M. F. Marmor et al., Visual evoked potentials standard, vol.108, p.115123, 2004.

J. K. Olofsson, S. Nordin, H. Sequeira, and J. Polich, Aective picture processing: an integrative review of ERP ndings, Biol Psychol, vol.77, issue.3, p.247265, 2008.

M. A. Pastor, J. Artieda, J. Arbizu, M. Valencia, and J. C. Masdeu, Human Cerebral Activation during Steady-State Visual-Evoked Responses, The Journal of Neuroscience, vol.23, p.1162111627, 2003.

S. H. Patel and P. N. Azzam, Characterization of N200 and P300: Selected Studies of the Event-related Potential, Int. J. Med. Sci, vol.2, issue.4, p.147154, 2005.

S. A. Patil, Brain Gate as an Assistive and Solution Providing Technology for Disabled People, ICBME Proceedings, vol.23, p.12321235, 2008.

G. Pfurtscheller, Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest, Electroencephalography and clinical Neurophysiology, vol.83, p.6269, 1992.

G. Pfurtscheller, B. Z. Allison, C. Brunner, G. Bauernfeind, T. Solis-escalante et al., The hybrid BCI, Front. Neurosci, issue.30, p.4, 2010.

G. Pfurtscheller and F. L. Da-silva, Event-related EEG/MEG synchronization and desynchronization: basic principles, Clinical Neurophysiology, vol.110, p.18421857, 1999.

G. Pfurtscheller, A. S. Jr, and C. Neuper, Event-related synchronization (ERS) in the alpha band -an electrophysiological correlate of cortical idling: A review, International Journal of Psychophysiology, vol.24, p.3946, 1996.

G. Pfurtscheller and C. Neuper, Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man, Neuroscience Letters, vol.174, p.9396, 1994.

G. Pfurtscheller, C. Neupera, D. Flotzingera, and M. Pregenzerb, EEG-based discrimination between imagination of right and left hand movement, Electroencephalography and Clinical Neurophysiology, vol.103, p.642651, 1997.

W. T. Picton, The P300 wave of the human event-related potential, Journal of Clinical Neurophysiology, vol.9, issue.4, p.456479, 1992.

J. C. Platt, N. Cristianini, and J. Shawe-taylor, Large Margin DAGs for Multiclass Classication, Advances in Neural Information Processing Systems, vol.12, p.547553, 2000.

J. Polich, Updating P300: An integrative theory of P3a and P3b, Clinical Neurophysiology, vol.118, p.21282148, 2007.

J. Polich and A. Kok, Cognitive and biological determinants of P300: an integrative review, Biological Psychology, vol.41, p.103146, 1995.

R. Polikar, A. Topalis, D. Green, J. Kounios, and C. M. Clark, Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classiers approach for early diagnosis of Alzheimer's disease, Computers in Biology and Medicine, vol.37, p.542558, 2007.

R. Quiroga and H. Garcia, Single-trial event-related potentials with wavelet denoising, Clinical Neurophysiology, vol.114, issue.2, p.376390, 2003.

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, p.441446, 2000.

R. Ranta and V. Louis-dorr, Hysteresis Thresholding: A Graph-Based Wavelet Block Denoising Algorithm, The Open Signal Processing Journal, vol.3, p.612, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00468034

K. Rauss, S. Schwartz, and G. Pourtois, Top-down eects on early visual processing in humans: A predictive coding framework, Neuroscience & Biobehavioral Reviews, vol.35, pp.1237-1253, 2011.

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, Teleoperators and virtual environments, vol.19, p.3553, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00477153

I. Rentzeperis, A. R. Nikolaev, D. C. Kiper, and C. V. Leeuwen, Relationship between neural response and adaptation selectivity to form and color: an ERP study, Front Hum Neurosci, vol.6, p.89, 2012.

B. Ripley, Pattern Recognition and Neural Networks, 1995.

B. Rivet, H. Cecotti, A. Souloumiac, E. Maby, and J. Mattout, Theoretical Analysis of XDAWN Algorithm: Application to an Ecient Sensor Selection in a P300 BCI, 2011.

, European Signal Processing Conference

B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain Computer Interface, IEEE Trans. Biomed. Engineering, vol.56, issue.8, p.20352043, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00454568

C. Roy and C. Sherrington, On the regulation of the blood-supply of the brain, The Journal of Physiology, vol.11, issue.1-2, p.85117, 1890.

F. D. Russo, A. Martínez, M. I. Sereno, S. Pitzalis, and S. A. Hillyard, Cortical sources of the early components of the visual evoked potential, Hum. Brain Mapp, vol.15, p.95111, 2002.

C. Saavedra and L. Bougrain, Wavelet denoising for p300 single-trial detection, Proceedings of 5th french conference in computational neuroscience, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00549218

C. Saavedra and L. Bougrain, Processing stages of visual stimuli and event-related potentials, Neurocomp/KEOpS'12 workshop, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00756795

C. Saavedra and L. Bougrain, Denoising and time-window selection using waveletbased semblance for improving erp detection, Proceedings of the Fifth International BrainComputer Interface Meeting, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00857523

C. Saavedra and L. Bougrain, Wavelt-based semblance for p300 single-trial detection, International Conference on Bio-Inspired Systems and Signal Processing, 2013.

C. Saavedra, L. Bougrain, and R. Ranta, Hysteresis thresholding for wavelet denoising applied to p300 single-trial detection, INRIA technical report, 2011.

M. Salvaris and F. Sepulveda, Visual modications on the P300 speller BCI paradigm, J Neural Eng, vol.6, p.46011, 2009.

P. Sauseng, W. Klimesch, W. R. Gruber, S. Hanslmaryr, R. Freunberger et al., Are event-related potentials components generated by phase resetting of brain oscillations? A critical discussion, Neuroscience, vol.146, p.14351444, 2007.

G. Schalk, D. Mcfarland, T. Hinterberger, N. Birbaumer, and J. Wolpaw, BCI2000: a general-purpose brain-computer interface (BCI) system, IEEE Transactions on Biomedical Engineering, vol.51, issue.6, p.10341043, 2004.

B. Schölkopf and A. J. Smola, Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond, 2002.

E. W. Sellers and E. Donchin, A P300-based brain-computer interface: Initial tests by ALS patients, Clinical Neurophysiology, vol.117, p.538548, 2006.

E. W. Sellers, D. J. Krusienski, D. J. Mcfarland, T. M. Vaughan, and W. , J. R, 2006.

, A P300 event-related potential brain-computer interface (BCI): The eects of matrix size and inter stimulus interval on performance, Biological Psychology, vol.73, p.242252

K. Squires and E. Donchin, Beyond averaging: the use of discriminant functions to recognize event related potentials elicited by single auditory stimuli, Electroencephalogr Clin. Neurophysiol, vol.41, issue.5, p.44959, 1976.

N. Squires, K. Squires, and S. Hillyard, Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man, Electroencephalogr Clin. Neurophysiol, vol.38, p.387401, 1975.

S. Sutton, M. Braren, J. Zubin, J. , and E. R. , Evoked-Potential Correlates of Stimulus Uncertainty, Science, vol.150, p.11871188, 1965.

B. A. Taheri, R. T. Knight, and R. L. Smith, A dry electrode for EEG recording, Electroencephalography and Clinical Neurophysiology, vol.90, p.376383, 1994.

K. Takano, T. Komatsu, N. Hata, Y. Nakajima, and K. Kansaku, Visual stimuli for the P300 brain-computer interface: A comparison of white/gray and green/blue icker matrices, Clinical Neurophysiology, vol.120, p.15621566, 2009.

M. Tangermann, M. Krauledat, K. Grzeska, M. Sagebaum, B. Blankertz et al., Playing pinball with non-invasive BCI, Advances in Neural Information Processing Systems, vol.21, p.16411648, 2009.

M. M. Ter-pogossian, Positron Emission Tomography (PET), Journal of Medical Systems, vol.6, p.569577, 1982.

A. R. Thornton, Evaluation of a technique to measure latency jitter in event-related potentials, Journal of Neuroscience Methods, vol.168, p.248255, 2008.

C. Torrence and G. Compo, A Practical Guide to Wavelet Analysis, Bulletin of the American Meteorological Society, vol.79, issue.1, p.6178, 1998.

G. Townsend, B. K. Lapallo, C. B. Boulay, D. J. Krusienski, G. E. Frye et al., A novel P300-based brain-computer interface stimulus presentation paradigm: Moving beyond rows and columns, Clin Neurophysiol, vol.7, p.11091120, 2010.

M. S. Treder and B. Blankertz, ). (C)overt attention and visual speller design in an ERP-based brain-computer interface, Behavioral and Brain Functions, vol.6, issue.28, p.113, 2010.

W. A. Truccolo, M. Ding, K. H. Knuth, R. Nakamura, and S. L. Bressler, Trial-to-trial variability of cortical evoked responses: implications for the analysis of functional connectivity, Clinical Neurophysiology, vol.113, p.206226, 2002.

C. S. Tsui, J. Q. Gan, and S. J. Roberts, A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman lter based algorithm for online training, Medical and Biological Engineering and Computing, vol.47, p.257265, 2009.

R. Van-den-berg, J. B. Roerdink, and F. W. Cornelissen, On the generality of crowding: Visual crowding in size, saturation, and hue compared to orientation, Journal of Vision, vol.7, issue.2, pp.14-111, 2007.

V. Vapnik, Estimation of Dependences Based on Empirical Data: Springer Series in Statistics, 1982.

V. Vapnik, An Overview of Statistical Learning Theory, IEEE Transaction on Neural Networks, vol.10, issue.5, p.988999, 1999.

T. M. Vaughan, D. J. Mcfarland, G. Schalk, W. A. Sarnacki, D. J. Krusienski et al., The Wadsworth BCI Research and Development Program: At Home With BCI, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, p.229233, 2006.

J. J. Vidal, Toward Direct Brain-Computer Communication, Annual Review of Biophysics and Bioengineering, vol.2, p.157180, 1973.
DOI : 10.1146/annurev.bb.02.060173.001105

Y. Wang, X. Gao, B. Hong, C. Jia, and S. Gao, Brain-Computer Interfaces Based on Visual Evoked Potentials, IEEE Engineering in Medicine and Biology Magazine, vol.27, p.6471, 2008.

E. M. Whitham, K. J. Pope, S. P. Fitzgibbon, T. Lewis, C. R. Clark et al.,

J. O. Willoughby, Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG, Clinical Neurophysiology, vol.118, p.18771888, 2007.

J. Wolpaw, N. Birbaumer, D. Mcfarland, G. Pfurtscheller, and T. Vaughan, Braincomputer interfaces for communication and control, Clinical Neurophysiology, vol.113, issue.6, p.767791, 2002.

J. R. Wolpaw, Brain-computer interfaces as new brain output pathways, The Journal of Physiology, vol.579, p.613619, 2007.
DOI : 10.1113/jphysiol.2006.125948

URL : https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1113/jphysiol.2006.125948

J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. Mcfarland, P. H. Peckham et al., Brain-Computer Interface Technology: A Review of the First International Meeting, IEEE Transactions on Rehabilitation Engineering, vol.8, p.164173, 2000.

J. R. Wolpaw and D. J. Mcfarland, Control of a two-dimensional movement signal by a noninvasive braincomputer interface in humans, PNAS, vol.101, issue.51, p.1784917854, 2004.

J. R. Wolpaw and E. W. Wolpaw, Brain-Computer Interfaces: Principles and Practice, 2012.

C. D. Woody, Characterization of an adaptive lter for the analysis of variable latency neuroelectric signals, Medical and Biological Engineering and Computing, vol.5, p.539554, 1967.

Y. Yong, N. Hurley, and G. Silvestre, Single-trial EEG classication for brain-computer interface using wavelet decomposition, European Signal Processing Conference, EUSIPCO 2005, 2005.

N. Yoshimura and N. Itakura, Usability of Transient VEPs in BCIs, Recent Advances in Brain-Computer Interfaces, vol.6, p.119134, 2011.

D. Zhu, J. Bieger, G. G. Molina, and R. M. Aarts, A survey of stimulation methods used in SSVEP-based BCIs, Calculer la similarité avec l'équation S = cos n (?), vol.8, 2010.

. |w-x-?-w-y-*-?-|, Calculer l'écart-type ?(D) de D et le normaliser entre 0 et 1. 11: La borne t low correspond à l'instant t à partir du début de la fenêtre pour lequel ?(D(t)) > ? w 12: La borne t up correspond à l, vol.10

[. Selon and . Kolev, les diérences les plus signicatives entre les réponses "cible" et "non cibles, 1997.