S. Badillo, G. Varoquaux, and P. Ciuciu, Hemodynamic Estimation Based on Consensus Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.211-215, 2013.
DOI : 10.1109/PRNI.2013.61

URL : https://hal.archives-ouvertes.fr/hal-00854621

S. Badillo, T. Vincent, and P. Ciuciu, Group-level impacts of within- and between-subject hemodynamic variability in fMRI, NeuroImage, vol.82, pp.433-448, 2013.
DOI : 10.1016/j.neuroimage.2013.05.100

URL : https://hal.archives-ouvertes.fr/hal-00854481

Y. Bekhti, N. Zilber, F. Pedregosa, P. Ciuciu, V. Van-wassenhove et al., Decoding perceptual thresholds from MEG/EEG, 2014 International Workshop on Pattern Recognition in Neuroimaging, 2014.
DOI : 10.1109/PRNI.2014.6858510

URL : https://hal.archives-ouvertes.fr/hal-01032909

G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. Heeger, Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1, pp.16-4207, 1996.

R. Casanova, S. Ryali, J. Serences, L. Yang, R. Kraft et al., The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis, NeuroImage, vol.40, issue.4, pp.1606-1624, 2008.
DOI : 10.1016/j.neuroimage.2008.01.011

L. Chaari, F. Forbes, T. Vincent, and P. Ciuciu, Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework, International Conference on Medical Image Computing and Computer-Assisted Intervention, vol.15, pp.180-188, 2012.
DOI : 10.1007/978-3-642-33454-2_23

URL : https://hal.archives-ouvertes.fr/hal-00859388

P. Ciuciu, J. Poline, G. Marrelec, J. Idier, C. Pallier et al., Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment, IEEE Transactions on Medical Imaging, vol.22, issue.10, pp.1235-51, 2003.
DOI : 10.1109/TMI.2003.817759

URL : https://hal.archives-ouvertes.fr/cea-00333694

D. D. Cox and R. L. Savoy, Functional magnetic resonance imaging (fMRI) ???brain reading???: detecting and classifying distributed patterns of fMRI activity in human visual cortex, NeuroImage, vol.19, issue.2, pp.261-270, 2003.
DOI : 10.1016/S1053-8119(03)00049-1

A. M. Dale, Optimal experimental design for event-related fMRI, Human Brain Mapping, vol.6, issue.2-3, pp.109-123, 1999.
DOI : 10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W

D. Degras and M. A. Lindquist, A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies, NeuroImage, vol.98, pp.61-72, 2014.
DOI : 10.1016/j.neuroimage.2014.04.052

O. M. Doyle, J. Ashburner, F. O. Zelaya, S. C. Williams, and M. Mehta, Multivariate decoding of brain images using ordinal regression, NeuroImage, vol.81, pp.347-57, 2013.
DOI : 10.1016/j.neuroimage.2013.05.036

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical parametric mapping: The analysis of functional brain images: The analysis of functional brain images, 2011.

K. J. Friston, A. P. Holmes, and J. P. Poline, Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, 1995.
DOI : 10.1002/hbm.460020402

K. J. Friston, O. Josephs, G. Rees, and R. Turner, Nonlinear event-related responses in fMRI, Magnetic Resonance in Medicine, vol.4, issue.1, pp.41-52, 1998.
DOI : 10.1002/mrm.1910390109

G. H. Glover, Deconvolution of Impulse Response in Event-Related BOLD fMRI1, NeuroImage, vol.9, issue.4, pp.416-445, 1999.
DOI : 10.1006/nimg.1998.0419

G. H. Golub, M. Heath, and G. Wahba, Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter, Technometrics, vol.5, issue.2, pp.215-223, 1979.
DOI : 10.1080/03610927508827223

C. Goutte, F. Nielsen, and L. K. Hansen, Modeling the hemodynamic response in fMRI using smooth FIR filters, IEEE Transactions on Medical Imaging, vol.19, issue.12, pp.1188-201, 2000.
DOI : 10.1109/42.897811

D. Handwerker, J. M. Ollinger, and M. Esposito, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, NeuroImage, vol.21, issue.4, pp.1639-51, 2004.
DOI : 10.1016/j.neuroimage.2003.11.029

J. Haynes and G. Rees, Decoding mental states from brain activity in humans, Nature Reviews Neuroscience, vol.16, issue.7, pp.523-557, 2006.
DOI : 10.1038/nrn1931

R. N. Henson, C. J. Price, M. D. Rugg, R. Turner, and K. J. Friston, Detecting Latency Differences in Event-Related BOLD Responses: Application to Words versus Nonwords and Initial versus Repeated Face Presentations, NeuroImage, vol.15, issue.1, pp.83-97, 2002.
DOI : 10.1006/nimg.2001.0940

R. A. Horn and C. R. Johnson, Topics in matrix analysis, 1991.
DOI : 10.1017/CBO9780511840371

K. N. Kay, . Naselaris, and J. L. Gallant, fMRI of human visual areas in response to natural images, 2011.

K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant, Identifying natural images from human brain activity, Nature, vol.79, issue.7185, pp.352-357, 2008.
DOI : 10.1038/nature06713

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556484

M. G. Kendall, A NEW MEASURE OF RANK CORRELATION, Biometrika, vol.30, issue.1-2, pp.81-93, 1938.
DOI : 10.1093/biomet/30.1-2.81

N. Kriegeskorte, M. Mur, and P. Bandettini, Representational similarity analysis ??? connecting the branches of systems neuroscience, Frontiers in systems neuroscience 2, 2008.
DOI : 10.3389/neuro.06.004.2008

Y. Lei, L. Tong, and B. Yan, A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments. Computational and mathematical methods in medicine, 2013.

M. A. Lindquist and T. D. Wager, Validity and power in hemodynamic response modeling: A comparison study and a new approach, Human Brain Mapping, vol.17, issue.8, pp.764-784, 2007.
DOI : 10.1002/hbm.20310

D. C. Liu and J. Nocedal, On the limited memory BFGS method for large scale optimization, Mathematical Programming, vol.32, issue.2, pp.1-3, 1989.
DOI : 10.1007/BF01589116

S. Makni, C. Beckmann, S. Smith, and M. Woolrich, Bayesian deconvolution fMRI data using bilinear dynamical systems, NeuroImage, vol.42, issue.4, pp.1381-96, 2008.
DOI : 10.1016/j.neuroimage.2008.05.052

S. Makni, P. Ciuciu, J. Idier, and J. Poline, Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution, IEEE Transactions on Signal Processing, vol.53, issue.9, pp.3488-3502, 2005.
DOI : 10.1109/TSP.2005.853303

G. Marrelec, H. Benali, P. Ciuciu, M. Pélégrini-issac, and J. Poline, Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information, Human Brain Mapping, vol.2, issue.1, pp.1-17, 2003.
DOI : 10.1002/hbm.10100

URL : https://hal.archives-ouvertes.fr/cea-00333748

Y. Miyawaki, H. Uchida, O. Yamashita, M. Sato, Y. Morito et al., Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders, Neuron, vol.60, issue.5, pp.915-944, 2008.
DOI : 10.1016/j.neuron.2008.11.004

M. Miranda, J. Friston, K. J. Brammer, and M. , Dynamic discrimination analysis: A spatial???temporal SVM, NeuroImage, vol.36, issue.1, pp.88-99, 2007.
DOI : 10.1016/j.neuroimage.2007.02.020

J. Mumford, B. O. Turner, F. G. Ashby, R. Poldrack, and . Feb, Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses, NeuroImage, vol.59, issue.3, pp.2636-2679, 2012.
DOI : 10.1016/j.neuroimage.2011.08.076

T. Naselaris, K. N. Kay, S. Nishimoto, and J. L. Gallant, Encoding and decoding in fMRI, NeuroImage, vol.56, issue.2, pp.400-410, 2011.
DOI : 10.1016/j.neuroimage.2010.07.073

T. Naselaris, R. J. Prenger, K. N. Kay, M. Oliver, and J. L. Gallant, Bayesian Reconstruction of Natural Images from Human Brain Activity, Neuron, vol.63, issue.6, pp.902-915, 2009.
DOI : 10.1016/j.neuron.2009.09.006

J. Nocedal and S. Wright, Numerical optimization, series in operations research and financial engineering, 2006.

F. Pedregosa, M. Eickenberg, B. Thirion, and A. Gramfort, HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.3-6, 2013.
DOI : 10.1109/PRNI.2013.50

URL : https://hal.archives-ouvertes.fr/hal-00821946

F. Pedregosa, O. Grisel, R. Weiss, A. Passos, and M. Brucher, Scikit-learn : Machine learning in python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. A. Poldrack, J. A. Mumford, and T. E. Nichols, Handbook of Functional MRI Data Analysis, 2011.
DOI : 10.1017/CBO9780511895029

J. Poline and M. Brett, The general linear model and fMRI: Does love last forever?, NeuroImage, vol.62, issue.2, pp.871-80, 2012.
DOI : 10.1016/j.neuroimage.2012.01.133

J. Röhmel and U. Mansmann, Unconditional Non-Asymptotic One-Sided Tests for Independent Binomial Proportions When the Interest Lies in Showing Non-Inferiority and/or Superiority, Biometrical Journal, vol.41, issue.2, pp.149-170, 1999.
DOI : 10.1002/(SICI)1521-4036(199905)41:2<149::AID-BIMJ149>3.0.CO;2-E

A. Savitzky and M. J. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures., Analytical Chemistry, vol.36, issue.8, pp.1627-1639, 1964.
DOI : 10.1021/ac60214a047

S. Schoenmakers, M. Barth, T. Heskes, and M. Van-gerven, Linear reconstruction of perceived images from human brain activity, NeuroImage, vol.83, pp.951-961, 2013.
DOI : 10.1016/j.neuroimage.2013.07.043

S. M. Tom, C. R. Fox, C. Trepel, and R. Poldrack, The Neural Basis of Loss Aversion in Decision-Making Under Risk, Science, vol.315, issue.5811, pp.315-515, 2007.
DOI : 10.1126/science.1134239

B. O. Turner, J. Mumford, R. Poldrack, and F. G. Ashby, Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs, NeuroImage, vol.62, issue.3, pp.1429-1467, 2012.
DOI : 10.1016/j.neuroimage.2012.05.057

T. Vincent, L. Risser, and P. Ciuciu, Spatially adaptive mixture modeling for analysis of fMRI time series, NeuroImage, vol.47, issue.4, pp.1059-1074, 2010.
DOI : 10.1016/S1053-8119(09)71791-4

URL : https://hal.archives-ouvertes.fr/cea-00470594

V. Q. Vu, P. Ravikumar, T. Naselaris, K. N. Kay, J. L. Gallant et al., Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models, The annals of applied statistics 5 (2B), p.1159, 2011.
DOI : 10.1214/11-AOAS476

J. Wang, H. Zhu, J. Fan, K. Giovanello, and W. Lin, Multiscale adaptive smoothing models for the hemodynamic response function in fMRI, The Annals of Applied Statistics, vol.7, issue.2, pp.904-935, 2013.
DOI : 10.1214/12-AOAS609SUPP

M. W. Woolrich, T. E. Behrens, and S. M. Smith, Constrained linear basis sets for HRF modelling using Variational Bayes, NeuroImage, vol.21, issue.4, pp.1748-61, 2004.
DOI : 10.1016/j.neuroimage.2003.12.024

T. Zhang, F. Li, L. Beckes, C. Brown, and J. A. Coan, Nonparametric inference of the hemodynamic response using multi-subject fMRI data, NeuroImage, vol.63, issue.3, pp.1754-65, 2012.
DOI : 10.1016/j.neuroimage.2012.08.014

T. Zhang, F. Li, L. Beckes, and J. Coan, A semi-parametric model of the hemodynamic response for multi-subject fMRI data, NeuroImage, vol.75, pp.136-181, 2013.
DOI : 10.1016/j.neuroimage.2013.02.048