B. Allison and C. Neuper, Could Anyone Use a BCI?, Brain-Computer Interfaces. Human-Computer Interaction Series, pp.35-54, 2010.
DOI : 10.1007/978-1-84996-272-8_3

P. Nunez and R. Srinivasan, Electric Fields of the Brain, 2006.
DOI : 10.1093/acprof:oso/9780195050387.001.0001

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, 2007.
DOI : 10.1088/1741-2560/4/2/R01
URL : https://hal.archives-ouvertes.fr/inria-00134950

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2008.

F. Cincotti, D. Mattia, A. F. Bufalari, S. Astolfi, L. Fallani et al., High-resolution EEG techniques for brain???computer interface applications, Journal of Neuroscience Methods, vol.167, issue.1, 2008.
DOI : 10.1016/j.jneumeth.2007.06.031
URL : https://infoscience.epfl.ch/record/146167/files/millan_2007_jnm.pdf

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

R. Grech, T. Cassar, J. Muscat, K. Camilleri, S. Fabri et al., Review on solving the inverse problem in EEG source analysis, Journal of NeuroEngineering and Rehabilitation, vol.5, issue.1, 2008.
DOI : 10.1186/1743-0003-5-25

S. Makeig, C. Kothe, T. Mullen, N. Shamlo, Z. Zhang et al., Evolving Signal Processing for Brain???Computer Interfaces, Proceedings of the IEEE, vol.100, issue.Special Centennial Issue, pp.1567-1584
DOI : 10.1109/JPROC.2012.2185009
URL : http://ieeexplore.ieee.org:80/stamp/stamp.jsp?tp=&arnumber=6169943

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, 2008.
DOI : 10.1109/TBME.2007.913986

G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, EEG-based discrimination between imagination of right and left hand movement, Electroencephalography and Clinical Neurophysiology, vol.103, issue.6, pp.642-651, 1997.
DOI : 10.1016/S0013-4694(97)00080-1

J. Wolpaw and D. Mcfarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans, Proceedings of the National Academy of Sciences, vol.114, issue.7, pp.17849-17854, 2004.
DOI : 10.1016/S1388-2457(03)00067-1

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

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

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

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, 2006.
DOI : 10.1088/0031-9155/51/8/002
URL : https://hal.archives-ouvertes.fr/inria-00134948

R. Lehembre, Q. Noirhomme, and B. Macq, Inverse Problem Applied to BCIs : Keeping Track of the EEG's Brain Dynamics Using Kalman Filtering, Proc. of the 3rd International Brain Computer Interface Workshop, pp.32-33, 2006.

F. Babiloni, F. Cincotti, M. Marciani, S. Salinari, L. Astolfi et al., The Estimation of Cortical Activity for Brain-Computer Interface: Applications in a Domotic Context, Computational Intelligence and Neuroscience, vol.41, issue.4, 2007.
DOI : 10.1038/nn947

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

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
URL : https://hal.archives-ouvertes.fr/inria-00368282

J. Fruitet and M. Clerc, Reconstruction of cortical sources activities for online classification of electroencephalographic signals., 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp.6317-6320, 2010.
DOI : 10.1109/IEMBS.2010.5627713
URL : https://hal.archives-ouvertes.fr/inria-00498768

M. Besserve, J. Martinerie, and L. Garnero, Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view, NeuroImage, vol.55, issue.4, pp.1536-1547, 2011.
DOI : 10.1016/j.neuroimage.2011.01.056
URL : https://hal.archives-ouvertes.fr/hal-00795718

M. Goel, R. Chavarriaga, and J. Millán, Cortical current density vs. surface EEG for event-related potential-based Brain-Computer Interface, 2011 5th International IEEE/EMBS Conference on Neural Engineering, pp.430-433, 2011.
DOI : 10.1109/NER.2011.5910578

M. Goel, R. Chavarriaga, and J. Millán, Comparing BCI performance using scalp EEG-and inverse solution-based features, 6th International Brain-Computer Interface Conference

M. Ahn, H. Cho, and S. Jun, Calibration Time Reduction through Source Imaging in Brain Computer Interface (BCI), pp.269-273, 2011.
DOI : 10.1088/0031-9155/40/3/001

M. Ahn, J. Hong, and S. Jun, Feasibility of approaches combining sensor and source features in brain???computer interface, Journal of Neuroscience Methods, vol.204, issue.1, pp.168-178, 2012.
DOI : 10.1016/j.jneumeth.2011.11.002

J. Fruitet, Interfaces Cerveau-Machines basees sur l'imagination de mouvements brefs: vers des boutons controles par la pensee, 2012.
URL : https://hal.archives-ouvertes.fr/tel-00838704

A. Zaitcev, G. Cook, W. Liu, M. Paley, and E. Milne, Feature extraction for BCIs based on electromagnetic source localization and multiclass Filter Bank Common Spatial Patterns, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.2015-1773
DOI : 10.1109/EMBC.2015.7318722

B. Edelman, B. Baxter, and B. He, EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks, IEEE Transactions on Biomedical Engineering, vol.63, issue.1, pp.4-14, 2016.
DOI : 10.1109/TBME.2015.2467312

H. Hallez, B. Vanrumste, R. Grech, J. Muscat, W. Clercq et al., Review on solving the forward problem in EEG source analysis, Journal of NeuroEngineering and Rehabilitation, vol.4, issue.1, 2007.
DOI : 10.1186/1743-0003-4-46

S. Cotter, B. Rao, K. Engan, and K. Kreutz-delgado, Sparse solutions to linear inverse problems with multiple measurement vectors, IEEE Transactions on Signal Processing, vol.53, issue.7, pp.2477-2488, 2005.
DOI : 10.1109/TSP.2005.849172

C. Michel, M. Murray, G. Lantz, S. Gonzales, 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

A. Gramfort, M. Kowalski, and M. Hämäläinen, Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Physics in Medicine and Biology, vol.57, issue.7, pp.1937-1961, 2012.
DOI : 10.1088/0031-9155/57/7/1937
URL : https://hal.archives-ouvertes.fr/hal-00690774

D. Wipf, S. A. Nagarajan, M. /. Unified-bayesian-framework-for, . Eeg, and . Imaging, A unified Bayesian framework for MEG/EEG source imaging, NeuroImage, vol.44, issue.3, pp.947-966, 2009.
DOI : 10.1016/j.neuroimage.2008.02.059

H. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems, 2000.

M. Hämäläinen and R. Ilmoniemi, Interpreting measured magnetic fields of the brain: estimates of current distributions. Helsinki University of Technology, 1984.

I. Gorodnitsky and B. Rao, Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm, IEEE Transactions on Signal Processing, vol.45, issue.3, pp.600-616, 1997.
DOI : 10.1109/78.558475
URL : http://dsp.rice.edu/files/cs/Rao3.pdf

R. Pascual-marqui, C. Michel, and D. Lehmann, Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain, International Journal of Psychophysiology, vol.18, issue.1, pp.49-65, 1994.
DOI : 10.1016/0167-8760(84)90014-X

M. Fuchs, M. Wagner, T. Köhler, and H. Wischmann, Linear and Nonlinear Current Density Reconstructions, Journal of Clinical Neurophysiology, vol.16, issue.3, pp.267-295, 1999.
DOI : 10.1097/00004691-199905000-00006

R. Pascual-marqui, Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, Methods Find Exp Clin Pharmacol, vol.24, pp.5-12, 2002.
DOI : 10.1016/s0013-4694(97)88020-4

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

J. Müller-gerking, G. Pfurtscheller, and H. Flyvbjerg, Designing optimal spatial filters for single-trial EEG classification in a movement task, Clinical Neurophysiology, vol.110, issue.5, pp.787-798, 1999.
DOI : 10.1016/S1388-2457(98)00038-8

B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain???Computer Interface, IEEE Transactions on Biomedical Engineering, vol.56, issue.8, 2009.
DOI : 10.1109/TBME.2009.2012869
URL : https://hal.archives-ouvertes.fr/hal-00454568

R. Fazel-rezai, B. Allison, C. Guger, E. Sellers, S. Kleih 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
URL : https://doi.org/10.3389/fneng.2012.00014

R. Rubinstein, A. Bruckstein, and M. Elad, Dictionaries for Sparse Representation Modeling, Proceedings of the IEEE, pp.1045-1057, 2010.
DOI : 10.1109/JPROC.2010.2040551
URL : https://hal.archives-ouvertes.fr/inria-00565811

B. Hamner, R. Chavarriaga, and J. Millán, Learning Dictionaries of Spatial and Temporal EEG Primitives for Brain-Computer Interfaces, Proc. Workshop Structured Sparsity: Learning and Inference, 2011.

W. Zhou, Y. Yang, and Z. Yu, Discriminative dictionary learning for EEG signal classification in Brain-computer interface, 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp.1582-1585
DOI : 10.1109/ICARCV.2012.6485382

Q. Barthélemy, C. Gouy-pailler, Y. Isaac, A. Souloumiac, A. Larue et al., Multivariate temporal dictionary learning for EEG, Journal of Neuroscience Methods, vol.215, issue.1, pp.19-28, 2013.
DOI : 10.1016/j.jneumeth.2013.02.001

F. Perrin, O. Bertrand, and J. Pernier, Scalp Current Density Mapping: Value and Estimation from Potential Data, IEEE Transactions on Biomedical Engineering, vol.34, issue.4, pp.283-288, 1987.
DOI : 10.1109/TBME.1987.326089

B. Burle, L. Spieser, C. Roger, L. Casini, T. Hasbroucq et al., Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view, International Journal of Psychophysiology, vol.97, issue.3, pp.210-220, 2015.
DOI : 10.1016/j.ijpsycho.2015.05.004
URL : https://hal.archives-ouvertes.fr/hal-01309952

H. Liu, P. Schimpf, G. Dong, X. Gao, F. Yang et al., Standardized Shrinking LORETA-FOCUSS (SSLOFO): A New Algorithm for Spatio-Temporal EEG Source Reconstruction, IEEE Transactions on Biomedical Engineering, vol.52, issue.10, pp.1681-1691, 2005.
DOI : 10.1109/TBME.2005.855720

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, 2004.

Z. Koles and A. Soong, EEG source localization: implementing the spatio-temporal decomposition approach, Electroencephalography and Clinical Neurophysiology, vol.107, issue.5, pp.343-353, 1998.
DOI : 10.1016/S0013-4694(98)00084-4

K. Kobayashi, T. Akiyama, T. Nakahori, H. Yoshinaga, and J. Gotman, Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC, Clinical Neurophysiology, vol.113, issue.5, pp.725-734, 2002.
DOI : 10.1016/S1388-2457(02)00047-0

J. Onton, M. Westerfield, J. Townsend, and S. Makeig, Imaging human EEG dynamics using independent component analysis, Neuroscience & Biobehavioral Reviews, vol.30, issue.6, p.808822, 2006.
DOI : 10.1016/j.neubiorev.2006.06.007

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, 2015.
DOI : 10.1109/JPROC.2015.2404941

Z. Acar and S. Makeig, Effects of Forward Model Errors on EEG Source Localization, Brain Topography, vol.6, issue.4, pp.378-396, 2013.
DOI : 10.1007/BF01211174

J. Song, C. Davey, C. Poulsen, P. Luu, S. Turovets et al., EEG source localization: Sensor density and head surface coverage, Journal of Neuroscience Methods, vol.256, pp.9-21, 2015.
DOI : 10.1016/j.jneumeth.2015.08.015
URL : https://doi.org/10.1016/j.jneumeth.2015.08.015

W. Tam, K. Tong, F. Meng, and S. Gao, A Minimal Set of Electrodes for Motor Imagery BCI to Control an Assistive Device in Chronic Stroke Subjects: A Multi-Session Study, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.19, issue.6, pp.617-627, 2011.

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

G. Pfurtscheller and C. Neuper, Motor imagery activates primary sensorimotor area in humans, Neuroscience Letters, vol.239, issue.2-3, 1997.
DOI : 10.1016/S0304-3940(97)00889-6

A. Liu, A. Dale, and J. Belliveau, Monte Carlo simulation studies of EEG and MEG localization accuracy, Human Brain Mapping, vol.85, issue.1, pp.47-62, 2002.
DOI : 10.1212/WNL.38.5.778
URL : http://cogsci.ucsd.edu/~sereno/276/readings/liu2002.pdf

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.767-791, 2002.
DOI : 10.1016/S1388-2457(02)00057-3

P. Shenoy, M. Krauledat, B. Blankertz, R. Rao, and K. Müller, Towards adaptive classification for BCI, Journal of Neural Engineering, vol.3, issue.1, 2006.
DOI : 10.1088/1741-2560/3/1/R02
URL : http://www.cs.washington.edu/homes/rao/neng-bci06.pdf