?. , M. Différemment, . Par, and . Décrit-par-le-modèle-dvca-multicanauxàmulticanauxà-la-tropp, Section 3.5.1, les latences des composantes du signal peuvent varieràvarierà travers les essais mais restent fixéesfixéesà travers les canaux. Concrètement, dans la version actuelle de l'algorithme E-AWL (Algorithme 3), la misè a jour des coefficients est effectuée séparémentséparémentà travers les essais. En cas de plusieurs canaux, cette misè a jour doitêtredoitêtre effectuée simultanément sur tous les canaux d'un essai spécifique pour prendre en compte la simultanéité des formes d'ondè a travers les canaux. Ceci peutêtre peutêtre implementé demanì ere similaire aux versions multicanaux existantes de matching pursuit, Bénar et al. Gribonval, 1995.

. Saillet, 2012), ces découvertes pourraientêtrepourraientêtre importantes. Par contre, desétudesdesétudes supplémentaires seront nécessaires pour confirmer la pertinence statistique de ces résultats. De plus, il est important de clarifier si l'activité autour de 1 Hz observée dans le CBF est effectivement causée par la respiration, 2010.

S. Hitziger, M. Clerc, S. Saillet, C. Bénar, and T. Papadopoulo, Adaptive waveform learning -application to single-and multi-modal neurological data

S. Saillet, A. I. Ivanov, P. Quilichini, A. Ghestem, B. Giusiano et al., Interneurons contribute to the hemodynamic/metabolic response to epileptiform discharges, Journal of Neurophysiology, vol.115, issue.3, p.2015
DOI : 10.1152/jn.00994.2014

URL : https://hal.archives-ouvertes.fr/hal-01431280

M. Aharon, M. Elad, and A. Bruckstein, <tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

F. A. Azevedo, L. R. Carvalho, L. T. Grinberg, J. M. Farfel, R. E. Ferretti et al., Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain, The Journal of Comparative Neurology, vol.16, issue.5, pp.513-532, 2009.
DOI : 10.1002/cne.21974

A. D. Back and A. S. Weigend, A First Application of Independent Component Analysis to Extracting Structure from Stock Returns, International Journal of Neural Systems, vol.08, issue.04, pp.473-484, 1997.
DOI : 10.1142/S0129065797000458

D. L. Bailey, D. W. Townsend, P. E. Valk, and M. N. Maisey, Positron emission tomography, p.22, 2005.
DOI : 10.1007/b136169

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

Q. Barthélemy, A. Larue, A. Mayoue, D. Mercier, and J. I. Mars, Shift & 2D Rotation Invariant Sparse Coding for Multivariate Signals, IEEE Transactions on Signal Processing, vol.60, issue.4, pp.1597-1611, 2012.
DOI : 10.1109/TSP.2012.2183129

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
DOI : 10.1016/j.jneumeth.2013.02.001

M. S. Bartlett, Face image analysis by unsupervised learning, p.43, 2001.
DOI : 10.1007/978-1-4615-1637-8

M. Bastiaansen, A. Mazaheri, and O. Jensen, Beyond erps: oscillatory neuronal dynamics. The Oxford handbook of event-related potential components, pp.31-50, 2012.
DOI : 10.1093/oxfordhb/9780195374148.013.0024

URL : http://hdl.handle.net/11858/00-001M-0000-0011-F363-1

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

C. Bénar, T. Papadopoulo, B. Torrésani, and M. Clerc, Consensus Matching Pursuit for multi-trial EEG signals, Journal of Neuroscience Methods, vol.180, issue.1, pp.161-170, 2009.
DOI : 10.1016/j.jneumeth.2009.03.005

H. Berger, ¨ Uber das elektrenkephalogramm des menschen, European Archives of Psychiatry and Clinical Neuroscience, vol.87, issue.1, pp.527-570, 1929.

T. Blanchard, C. Papadopoulo, N. Bénar, M. Voges, H. Clerc et al., Relationship Between Flow and Metabolism in BOLD Signals: Insights from Biophysical Models, Brain Topography, vol.24, issue.7, pp.40-53, 2011.
DOI : 10.1007/s10548-010-0166-6

URL : https://hal.archives-ouvertes.fr/inserm-00613116

T. Blumensath and M. Davies, Sparse and shift-invariant representations of music. Audio, Speech, and Language Processing, IEEE Transactions on, vol.14, issue.1, pp.50-57, 2006.

A. Cabasson and O. Meste, Time Delay Estimation: A New Insight Into the Woody's Method, IEEE Signal Processing Letters, vol.15, pp.573-576, 2008.
DOI : 10.1109/LSP.2008.2001558

J. Cardoso, High-Order Contrasts for Independent Component Analysis, Neural Computation, vol.140, issue.1, pp.157-192, 1999.
DOI : 10.1109/78.599941

J. Cardoso and A. Souloumiac, Blind beamforming for non-gaussian signals, IEE Proceedings F (Radar and Signal Processing, pp.362-370, 1993.
DOI : 10.1049/ip-f-2.1993.0054

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

R. Chambolle, N. De-vore, B. Lee, and . Lucier, Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage, IEEE Transactions on Image Processing, vol.7, issue.3, pp.319-335, 1998.
DOI : 10.1109/83.661182

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

. Cohen, Magnetoencephalography: Evidence of Magnetic Fields Produced by Alpha-Rhythm Currents, Science, vol.161, issue.3843, pp.784-786, 1921.
DOI : 10.1126/science.161.3843.784

P. Comon, Independent component analysis, a new concept? Signal processing, pp.287-314, 1994.

R. Coppola, R. Tabor, and M. S. Buchsbaum, Signal to noise ratio and response variability measurements in single trial evoked potentials, Electroencephalography and Clinical Neurophysiology, vol.44, issue.2, pp.214-222, 1978.
DOI : 10.1016/0013-4694(78)90267-5

I. Daubechies, M. Defrise, and C. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004.
DOI : 10.1002/cpa.20042

G. D. Dawson, A summation technique for the detection of small evoked potentials, Electroencephalography and Clinical Neurophysiology, vol.6, issue.34, pp.65-84, 1954.
DOI : 10.1016/0013-4694(54)90007-3

D. Gennaro and M. Ferrara, Sleep spindles: an overview. Sleep medicine reviews, pp.423-440, 2003.

B. Deecke, H. Grözinger, and . Kornhuber, Voluntary finger movement in man: Cerebral potentials and theory, Biological Cybernetics, vol.6, issue.2, pp.99-119, 1976.
DOI : 10.1007/BF00336013

T. Delorme, S. Sejnowski, and . Makeig, Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis, NeuroImage, vol.34, issue.4, pp.1443-1449, 2007.
DOI : 10.1016/j.neuroimage.2006.11.004

URL : https://hal.archives-ouvertes.fr/hal-00135628

A. A. Dingle, R. D. Jones, G. J. Carroll, and W. Fright, A multistage system to detect epileptiform activity in the EEG, IEEE Transactions on Biomedical Engineering, vol.40, issue.12, pp.401260-1268, 1993.
DOI : 10.1109/10.250582

E. Donchin, W. Ritter, and W. C. Mccallum, Cognitive psychophysiology: The endogenous components of the erp. Event-related brain potentials in man, pp.349-411, 1978.

P. Durka and K. Blinowska, Analysis of EEG transients by means of matching pursuit, Annals of Biomedical Engineering, vol.54, issue.5, pp.608-611, 1995.
DOI : 10.1007/BF02584459

P. Durka, A. Matysiak, E. Montes, P. Sosa, and K. Blinowska, Multichannel matching pursuit and EEG inverse solutions, Journal of Neuroscience Methods, vol.148, issue.1, pp.49-59, 2005.
DOI : 10.1016/j.jneumeth.2005.04.001

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression. The Annals of statistics, pp.407-499, 2004.

M. Elad and M. Aharon, Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3745, 2006.
DOI : 10.1109/TIP.2006.881969

K. Engan, S. Aase, and J. H. Husoy, Method of optimal directions for frame design, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), pp.2443-2446, 1999.
DOI : 10.1109/ICASSP.1999.760624

H. Gibbons and J. Stahl, Response-time corrected averaging of event-related potentials, Clinical Neurophysiology, vol.118, issue.1, pp.197-208, 2007.
DOI : 10.1016/j.clinph.2006.09.011

A. Gibson, J. Hebden, and S. R. Arridge, Recent advances in diffuse optical imaging, Physics in Medicine and Biology, vol.50, issue.4, pp.1-22, 2005.
DOI : 10.1088/0031-9155/50/4/R01

. Gramfort, Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision, 1921.
URL : https://hal.archives-ouvertes.fr/tel-00426852

A. Gramfort, R. Keriven, and M. Clerc, Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses, IEEE Transactions on Biomedical Engineering, vol.57, issue.5, pp.1051-1061, 2010.
DOI : 10.1109/TBME.2009.2037139

URL : https://hal.archives-ouvertes.fr/inria-00502697

. Gribonval, Piecewise linear source separation, Wavelets: Applications in Signal and Image Processing X, pp.297-310, 2003.
DOI : 10.1117/12.504790

URL : https://hal.archives-ouvertes.fr/inria-00576207

M. Groppe, S. Makeig, and M. Kutas, Independent component analysis of event-related potentials, Cognitive science online, vol.6, issue.1, pp.1-44, 2008.

R. Grosse, H. Raina, A. Y. Kwong, and . Ng, Shift-Invariant Sparse Coding for Audio Classification, In Uncertainty in Artificial Intelligence, pp.149-158

J. T. Gwin, K. Gramann, S. Makeig, and D. P. Ferris, Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running, Journal of Neurophysiology, vol.103, issue.6, pp.3526-3534, 2010.
DOI : 10.1152/jn.00105.2010

R. Hamner, J. D. Chavarriaga, and . Millán, Learning dictionaries of spatial and temporal eeg primitives for brain-computer interfaces, Workshop on Structured Sparsity: Learning and Inference, ICML 2011, number EPFL-CONF-166740, p.50, 2011.

K. D. Harris, D. A. Henze, J. Csicsvari, H. Hirase, and G. Buzsáki, Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements, Journal of neurophysiology, vol.84, issue.1, pp.401-414, 2000.

M. Hitziger, A. Clerc, S. Gramfort, C. Saillet, T. Bénar et al., Jitter-adaptive dictionary learning-application to multi-trial neuroelectric signals, Proceedings of the International Conference on Learning Representations (ICLR), p.29, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01094619

. Horvath, Variability of cortical auditory evoked response, J Neurophysiol, vol.32, issue.6 2, pp.1056-1063, 1969.

. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, vol.10, issue.3, pp.626-634, 1999.
DOI : 10.1109/72.761722

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

A. Hyvärinen, P. Ramkumar, L. Parkkonen, and R. Hari, Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis, NeuroImage, vol.49, issue.1, pp.257-271, 2010.
DOI : 10.1016/j.neuroimage.2009.08.028

M. Ihrke, H. Schrobsdorff, and J. M. Herrmann, Recurrence-Based Synchronization of Single Trials for EEG-Data Analysis, Intelligent Data Engineering and Automated Learning-IDEAL 2009, pp.118-125, 2009.
DOI : 10.1016/0168-5597(88)90006-8

F. Itakura, Minimum prediction residual principle applied to speech recognition . Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.23, issue.38, pp.67-72, 1975.

M. G. Jafari and M. D. Plumbley, Speech denoising based on a greedy adaptive dictionary algorithm, European Signal Processing Conference, p.50, 2009.

E. R. John, D. S. Ruchkin, and J. J. Vidal, Measurement of event-related potentials. Event-Related Brain Potentials in Man, pp.93-138, 1978.

S. Jost, P. Lesage, R. Vandergheynst, and . Gribonval, Learning redundant dictionaries with translation invariance property: the motif algorithm, p.52, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00816804

P. Jost, S. Vandergheynst, R. Lesage, and . Gribonval, MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, p.52, 2006.
DOI : 10.1109/ICASSP.2006.1661411

URL : https://hal.archives-ouvertes.fr/inria-00544911

S. Jung, M. Makeig, J. Westerfield, E. Townsend, T. Courchesne et al., Analysis and visualization of single-trial event-related potentials, Human Brain Mapping, vol.104, issue.3, pp.166-185, 2001.
DOI : 10.1002/hbm.1050

T. Jung, S. Makeig, C. Humphries, T. Lee, M. J. Mckeown et al., Removing electroencephalographic artifacts by blind source separation, Psychophysiology, vol.37, issue.2, pp.37163-178, 2000.
DOI : 10.1111/1469-8986.3720163

E. J. Keogh and M. J. Pazzani, Derivative Dynamic Time Warping, SDM, pp.5-7, 2001.
DOI : 10.1137/1.9781611972719.1

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

M. A. Kisley and G. L. Gerstein, Trial-to-trial variability and statedependent modulation of auditory-evoked responses in cortex, The Journal of neuroscience, vol.19, issue.10, pp.10451-10460, 1999.

H. Knuth, A. S. Shah, W. A. Truccolo, M. Ding, S. L. Bressler et al., Differentially Variable Component Analysis: Identifying Multiple Evoked Components Using Trial-to-Trial Variability, Journal of Neurophysiology, vol.95, issue.5, pp.3257-3276, 2006.
DOI : 10.1152/jn.00663.2005

H. Kornhuber and L. Deecke, Hirnpotentialänderungen bei willkürbewegungen und passiven bewegungen des menschen: Bereitschaftspotential und reafferente potentiale. Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere, pp.1-17, 1965.

S. Krstulovic and R. Gribonval, Mptk: Matching Pursuit Made Tractable, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp.496-499, 0123.
DOI : 10.1109/ICASSP.2006.1660699

URL : https://hal.archives-ouvertes.fr/inria-00544919

M. Kutas and A. Dale, Electrical and magnetic readings of mental functions, Cognitive neuroscience, p.25, 1997.

T. Lagerlund, F. Sharbrough, and N. Busacker, Spatial Filtering of Multichannel Electroencephalographic Recordings Through Principal Component Analysis by Singular Value Decomposition, Journal of Clinical Neurophysiology, vol.14, issue.1, pp.73-82, 1997.
DOI : 10.1097/00004691-199701000-00007

G. Larsson, J. Wilson, and O. Eeg-olofsson, A New Method for Quantification and Assessment of Epileptiform Activity in EEG with Special Reference to Focal Nocturnal Epileptiform Activity, Brain Topography, vol.49, issue.Suppl 1, pp.52-59, 2009.
DOI : 10.1007/s10548-008-0072-3

N. K. Logothetis, The underpinnings of the bold functional magnetic resonance imaging signal, The Journal of Neuroscience, vol.23, issue.10, pp.3963-3971, 2003.

J. Luck, Event-related potentials APA handbook of research methods in psychology, pp.523-546, 2012.

J. Luck, An introduction to the event-related potential technique, p.25, 2014.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp.281-297, 1967.

S. Mailhé, R. Lesage, F. Gribonval, P. Bimbot, and . Vandergheynst, Shift-invariant dictionary learning for sparse representations: extending k-svd, 16th EUropean SIgnal Processing COnference (EUSIPCO'08), p.52, 2008.

B. Mailhé, R. Gribonval, F. Bimbot, M. Lemay, P. Vandergheynst et al., Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.465-468, 2009.
DOI : 10.1109/ICASSP.2009.4959621

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Supervised dictionary learning, p.50, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00322431

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online Learning for Matrix Factorization and Sparse Coding, pp.19-60, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00408716

A. Makeig, T. Bell, T. Jung, and . Sejnowski, Independent component analysis of electroencephalographic data Advances in neural information processing systems, pp.145-151, 1996.

. Mallat, A wavelet tour of signal processing Academic press, p.48, 1999.

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

M. Marwan, N. Thiel, and . Nowaczyk, Cross recurrence plot based synchronization of time series. arXiv preprint physics, p.40, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00302123

M. Michel and M. M. Murray, Towards the utilization of EEG as a brain imaging tool, NeuroImage, vol.61, issue.2, pp.371-385, 2012.
DOI : 10.1016/j.neuroimage.2011.12.039

J. Möcks, W. Köhler, T. Gasser, and D. T. Pham, Novel Approaches to the Problem of Latency Jitter, Psychophysiology, vol.5, issue.2, pp.217-226, 1988.
DOI : 10.1016/0013-4694(77)90238-3

M. Mørup, M. N. Schmidt, and L. K. Hansen, Shift invariant sparse coding of image and music data, p.52, 2008.

S. Ogawa, T. Lee, A. S. Nayak, and P. Glynn, Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields, Magnetic Resonance in Medicine, vol.45, issue.1, pp.68-78, 1990.
DOI : 10.1002/mrm.1910140108

. Olivi, Coupling of numerical methods for the forward problem in Magnetoand Electro-EncephaloGraphy, p.23, 2011.

B. A. Olshausen and D. J. Field, Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision research, pp.3311-3336, 1997.

J. Onton, M. Westerfield, J. Townsend, and S. Makeig, Imaging human EEG dynamics using independent component analysis, Neuroscience & Biobehavioral Reviews, vol.30, issue.6, pp.808-822, 2006.
DOI : 10.1016/j.neubiorev.2006.06.007

C. Papageorgakis, Dictionary learning for multidimensional data, p.119, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01243284

R. Pati, P. Rezaiifar, and . Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465

D. T. Pham, J. Möcks, W. Köhler, and T. Gasser, Variable latencies of noisy signals: estimation and testing in brain potential data, Biometrika, pp.525-533, 1987.

T. Picton, M. Hunt, R. Mowrey, R. Rodriguez, and J. Maru, Evaluation of brain-stem auditory evoked potentials using dynamic time warping, Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, vol.71, issue.3, pp.212-225, 1988.
DOI : 10.1016/0168-5597(88)90006-8

T. Picton, S. Bentin, P. Berg, E. Donchin, S. Hillyard et al., Guidelines for using human eventrelated potentials to study cognition: recording standards and publication criteria, Psychophysiology, issue.02, pp.37127-152, 2000.

M. D. Plumbley, S. A. Abdallah, T. Blumensath, and M. E. Davies, Sparse representations of polyphonic music, Signal Processing, vol.86, issue.3, pp.417-431, 2006.
DOI : 10.1016/j.sigpro.2005.06.007

J. Polich, Overview of p3a and p3b. In Detection of change: event-related potential and fMRI findings, J Polich, pp.83-98, 2003.

J. Polich, Updating P300: An integrative theory of P3a and P3b, Clinical Neurophysiology, vol.118, issue.10, pp.2128-2148, 2007.
DOI : 10.1016/j.clinph.2007.04.019

W. S. Pritchard, Psychophysiology of P300., Psychological Bulletin, vol.89, issue.3, pp.506-530, 1981.
DOI : 10.1037/0033-2909.89.3.506

L. R. Rabiner and B. Juang, Fundamentals of speech recognition, p.39, 1993.

T. Ristaniemi and J. Joutsensalo, On the performance of blind source separation in cdma, Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA 99), pp.437-441, 1999.

F. Rösler and D. Manzey, Principal components and varimax-rotated components in event-related potential research: Some remarks on their interpretation, Biological Psychology, vol.13, pp.3-26, 1981.
DOI : 10.1016/0301-0511(81)90024-7

S. Saillet, A. I. Ivanov, P. Quilichini, A. Ghestem, B. Giusiano et al., Interneurons contribute to the hemodynamic/metabolic response to epileptiform discharges. submitted to, Journal of neurophysiology, vol.113, pp.2015-2044
URL : https://hal.archives-ouvertes.fr/hal-01431280

H. Sakoe and S. Chiba, A dynamic programming approach to continuous speech recognition, Proceedings of the seventh international congress on acoustics, pp.65-69, 1971.

H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition. Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.26, issue.1, pp.43-49, 1978.

S. Salvador and P. Chan, Toward accurate dynamic time warping in linear time and space, Intelligent Data Analysis, vol.11, issue.5, pp.561-580, 2007.

L. Sörnmo and P. Laguna, Bioelectrical signal processing in cardiac and neurological applications, p.23, 2005.

E. Speckmann and C. E. Elger, Introduction to the neurophysiological basis of the eeg and dc potentials. Electroencephalography: Basic principles, clinical applications, and related fields, pp.17-29, 2005.

P. Stoica and Y. Selen, Model-order selection, IEEE Signal Processing Magazine, vol.21, issue.4, pp.36-47, 2004.
DOI : 10.1109/MSP.2004.1311138

O. Tallon-baudry and . Bertrand, Oscillatory gamma activity in humans and its role in object representation, Trends in Cognitive Sciences, vol.3, issue.4, pp.151-162, 1999.
DOI : 10.1016/S1364-6613(99)01299-1

. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

I. Tosic and P. Frossard, Dictionary Learning, IEEE Signal Processing Magazine, vol.28, issue.2, pp.27-38, 1950.
DOI : 10.1109/MSP.2010.939537

I. Tosic, I. Jovanovic, P. Frossard, M. Vetterli, and N. Duric, Ultrasound tomography with learned dictionaries, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.5502-5505, 2010.
DOI : 10.1109/ICASSP.2010.5495211

URL : http://infoscience.epfl.ch/record/143735

J. A. Tropp, A. C. Gilbert, and M. J. Strauss, Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit, Signal Processing, vol.86, issue.3, pp.572-588, 2006.
DOI : 10.1016/j.sigpro.2005.05.030

W. Truccolo, K. H. Knuth, A. Shah, S. L. Bressler, C. E. Schroeder et al., Estimation of single-trial multicomponent ERPs: Differentially variable component analysis (dVCA), Biological Cybernetics, vol.89, issue.6, pp.426-438, 2003.
DOI : 10.1007/s00422-003-0433-7

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, issue.2, pp.206-226, 2002.
DOI : 10.1016/S1388-2457(01)00739-8

. Vallaghé, Modélisation duprobì eme direct de la magnéto etélecetélectroencéphalographie: méthodes numériques et calibration, p.19, 2008.

I. Vanzetta, C. Flynn, A. I. Ivanov, C. Bernard, and C. G. Bénar, Investigation of Linear Coupling Between Single-Event Blood Flow Responses and Interictal Discharges in a Model of Experimental Epilepsy, Journal of Neurophysiology, vol.103, issue.6, pp.3139-3152, 2010.
DOI : 10.1152/jn.01048.2009

R. Vigário, V. Jousmäki, M. Haemaelaeninen, R. Haft, and E. Oja, Independent component analysis for identification of artifacts in magnetoencephalographic recordings Advances in neural information processing systems, pp.229-235, 1998.

N. Voges, S. Blanchard, F. Wendling, O. David, H. Benali et al., Modeling of the Neurovascular Coupling in Epileptic Discharges, Brain Topography, vol.2, issue.5, pp.136-156, 2012.
DOI : 10.1007/s10548-011-0190-1

URL : https://hal.archives-ouvertes.fr/inserm-00613123

N. Vongsavan and B. Matthews, Some aspects of the use of laser Doppler flow meters for recording tissue blood flow, Experimental Physiology, vol.78, issue.1, pp.1-14, 1993.
DOI : 10.1113/expphysiol.1993.sp003664

T. Wang and . Gasser, Alignment of curves by dynamic time warping. The Annals of Statistics, pp.1251-1276, 1997.

H. Wersing, J. Eggert, and E. Körner, Sparse Coding with Invariance Constraints, Artificial Neural Networks and Neural Information Processing -ICANN/ICONIP 2003, pp.385-392, 2003.
DOI : 10.1007/3-540-44989-2_46

C. Wood and G. Mccarthy, Principal component analysis of event-related potentials: Simulation studies demonstrate misallocation of variance across components, Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, vol.59, issue.3, pp.249-260, 1984.
DOI : 10.1016/0168-5597(84)90064-9

. Woody, Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals, Medical & Biological Engineering, vol.7, issue.6, pp.539-554, 1967.
DOI : 10.1007/BF02474247

L. Xu, P. Stoica, J. Li, S. L. Bressler, X. Shao et al., Aseo: a method for the simultaneous estimation of single-trial event-related potentials and ongoing brain activities, Biomedical Engineering IEEE Transactions on, vol.56, issue.1, pp.111-121, 2009.

J. Zimmerman, P. Thiene, and J. Harding, Design and Operation of Stable rf???Biased Superconducting Point???Contact Quantum Devices, and a Note on the Properties of Perfectly Clean Metal Contacts, Journal of Applied Physics, vol.41, issue.4, pp.1572-1580, 1970.
DOI : 10.1063/1.1659074

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998