P. Comon, Independent component analysis, A new concept?, Signal Processing, vol.36, issue.3, pp.287-314, 1994.
DOI : 10.1016/0165-1684(94)90029-9
URL : https://hal.archives-ouvertes.fr/hal-00417283

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

S. Makeig, T. Jung, A. J. Bell, D. Ghahremani, and T. J. Sejnowski, Blind separation of auditory event-related brain responses into independentcomponents, Proceedings of the National Academy of Sciences, vol.94, issue.20, pp.10-979, 1997.

V. Calhoun, T. Adali, G. Pearlson, and J. Pekar, A method for making group inferences from functional MRI data using independent component analysis, Human Brain Mapping, vol.2, issue.3, pp.140-151, 2001.
DOI : 10.1006/nimg.1995.1023

C. F. Beckmann and S. M. Smith, Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging, IEEE Transactions on Medical Imaging, vol.23, issue.2, pp.137-152, 2004.
DOI : 10.1109/TMI.2003.822821

A. Delorme, J. Palmer, J. Onton, R. Oostenveld, and S. Makeig, Independent EEG Sources Are Dipolar, PLoS ONE, vol.16, issue.3, p.30135, 2012.
DOI : 10.1371/journal.pone.0030135.t001
URL : https://hal.archives-ouvertes.fr/hal-00674521

D. Nuzillard and A. Bijaoui, Blind source separation and analysis of multispectral astronomical images, Astronomy and Astrophysics Supplement Series, vol.147, issue.1, pp.129-138, 2000.
DOI : 10.1051/aas:2000292

D. Maino, A. Farusi, C. Baccigalupi, F. Perrotta, A. Banday et al., All-sky astrophysical component separation with Fast Independent Component Analysis (FASTICA), Monthly Notices of the Royal Astronomical Society, vol.334, issue.1, pp.53-68, 2002.
DOI : 10.1046/j.1365-8711.2002.05425.x

A. Cadavid, J. Lawrence, and A. Ruzmaikin, Principal Components and Independent Component Analysis of Solar and Space Data, Solar Physics, vol.327, issue.A5, pp.247-261, 2008.
DOI : 10.1016/B978-0-12-498150-8.50024-0

V. Vrabie, C. Gobinet, O. Piot, A. Tfayli, P. Bernard et al., Independent component analysis of Raman spectra: Application on paraffin-embedded skin biopsies, Biomedical Signal Processing and Control, vol.2, issue.1, pp.40-50, 2007.
DOI : 10.1016/j.bspc.2007.03.001

D. N. Rutledge and D. J. Bouveresse, Independent Components Analysis with the JADE algorithm, TrAC Trends in Analytical Chemistry, vol.50, pp.22-32, 2013.
DOI : 10.1016/j.trac.2013.03.013
URL : https://hal.archives-ouvertes.fr/hal-01003372

S. Lee and S. Batzoglou, Application of independent component analysis to microarrays, Genome Biology, vol.4, issue.11, p.76, 2003.
DOI : 10.1186/gb-2003-4-11-r76

M. Scholz, S. Gatzek, A. Sterling, O. Fiehn, and J. Selbig, Metabolite fingerprinting: detecting biological features by independent component analysis, Bioinformatics, vol.20, issue.15, pp.2447-2454, 2004.
DOI : 10.1093/bioinformatics/bth270

A. 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

A. Delorme and S. Makeig, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, Journal of Neuroscience Methods, vol.134, issue.1, pp.9-21, 2004.
DOI : 10.1016/j.jneumeth.2003.10.009

A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier et al., MNE software for processing MEG and EEG data, NeuroImage, vol.86, pp.446-460, 2014.
DOI : 10.1016/j.neuroimage.2013.10.027

J. Cardoso, Infomax and maximum likelihood for blind source separation, IEEE Signal Processing Letters, vol.4, issue.4, pp.112-114, 1997.
DOI : 10.1109/97.566704

J. Montoya-martínez, J. Cardoso, and A. Gramfort, Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG, International Conference on Latent Variable Analysis and Signal Separation, pp.279-289, 2017.
DOI : 10.1007/BF01132771

P. Tillet, H. Kung, and D. Cox, Infomax-ICA using Hessian-free optimization, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2537-2541, 2017.
DOI : 10.1109/ICASSP.2017.7952614

D. T. Pham and P. Garat, Blind separation of mixture of independent sources through a quasi-maximum likelihood approach, IEEE Transactions on Signal Processing, vol.45, issue.7, pp.1712-1725, 1997.
DOI : 10.1109/78.599941
URL : https://hal.archives-ouvertes.fr/hal-01485511

S. Amari, T. Chen, and A. Cichocki, Stability Analysis of Learning Algorithms for Blind Source Separation, Neural Networks, vol.10, issue.8, pp.1345-1351, 1997.
DOI : 10.1016/S0893-6080(97)00039-7

M. Zibulevsky, Blind source separation with relative newton method, Proc. ICA, pp.897-902, 2003.

J. A. Palmer, S. Makeig, K. Kreutz-delgado, and B. D. Rao, Newton method for the ICA mixture model, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1805-1808, 2008.
DOI : 10.1109/ICASSP.2008.4517982

H. Choi and S. Choi, A relative trust-region algorithm for independent component analysis, Neurocomputing, vol.70, issue.7-9, pp.1502-1510, 2007.
DOI : 10.1016/j.neucom.2006.03.018

R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A Limited Memory Algorithm for Bound Constrained Optimization, SIAM Journal on Scientific Computing, vol.16, issue.5, pp.1190-1208, 1995.
DOI : 10.1137/0916069

J. Cardoso and B. H. Laheld, Equivariant adaptive source separation, IEEE Transactions on Signal Processing, vol.44, issue.12, pp.3017-3030, 1996.
DOI : 10.1109/78.553476
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.7924

W. C. Davidon, Variable Metric Method for Minimization, SIAM Journal on Optimization, vol.1, issue.1, pp.1-17, 1991.
DOI : 10.1137/0801001

R. Fletcher, A new approach to variable metric algorithms, The Computer Journal, vol.13, issue.3, pp.317-322, 1970.
DOI : 10.1093/comjnl/13.3.317

R. Fletcher and M. J. Powell, A Rapidly Convergent Descent Method for Minimization, The Computer Journal, vol.6, issue.2, pp.163-168, 1963.
DOI : 10.1093/comjnl/6.2.163

C. G. Broyden, The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations, IMA Journal of Applied Mathematics, vol.6, issue.1, pp.76-90, 1970.
DOI : 10.1093/imamat/6.1.76

D. Goldfarb, A family of variable-metric methods derived by variational means, Mathematics of Computation, vol.24, issue.109, pp.23-26, 1970.
DOI : 10.1090/S0025-5718-1970-0258249-6

D. F. Shanno, Conditioning of quasi-Newton methods for function minimization, Mathematics of Computation, vol.24, issue.111, pp.647-656, 1970.
DOI : 10.1090/S0025-5718-1970-0274029-X

L. Jiang, R. H. Byrd, E. Eskow, and R. B. Schnabel, A preconditioned L-BFGS algorithm with application to molecular energy minimization, 2004.
DOI : 10.21236/ADA444850

P. Wolfe, Convergence Conditions for Ascent Methods, SIAM Review, vol.11, issue.2, pp.226-235, 1969.
DOI : 10.1137/1011036

J. J. Moré and D. J. Thuente, Line search algorithms with guaranteed sufficient decrease, ACM Transactions on Mathematical Software, vol.20, issue.3, pp.286-307, 1994.
DOI : 10.1145/192115.192132

A. 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

J. F. 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=10.1.1.8.5684

F. R. Bach and M. I. Jordan, Kernel independent component analysis, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., pp.1-48, 2002.
DOI : 10.1109/ICASSP.2003.1202783

L. Bottou, F. E. Curtis, and J. Nocedal, Optimization methods for largescale machine learning, 2016.

J. A. Palmer, K. Kreutz-delgado, and S. Makeig, AMICA: An adaptive mixture of independent component analyzers with shared components, 2012.

S. Van-der-walt, S. Colbert, and G. Varoquaux, The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, vol.13, issue.2, pp.22-30, 2011.
DOI : 10.1109/MCSE.2011.37
URL : https://hal.archives-ouvertes.fr/inria-00564007

T. Jung, C. Humphries, T. Lee, S. Makeig, M. J. Mckeown et al., Extended ICA removes artifacts from electroencephalographic recordings, Proceedings of the 10th International Conference on Neural Information Processing Systems, ser. NIPS'97, pp.894-900, 1997.

A. Consortium, The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience, Frontiers in Systems Neuroscience, vol.6, 2012.
DOI : 10.3389/fnsys.2012.00062

G. Varoquaux, S. Sadaghiani, P. Pinel, A. Kleinschmidt, J. Poline et al., A group model for stable multi-subject ICA on fMRI datasets, NeuroImage, vol.51, issue.1, pp.288-299, 2010.
DOI : 10.1016/j.neuroimage.2010.02.010
URL : https://hal.archives-ouvertes.fr/hal-00489507

A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller et al., Machine learning for neuroimaging with scikit-learn, Frontiers in Neuroinformatics, vol.8, 2014.
DOI : 10.3389/fninf.2014.00014
URL : https://hal.archives-ouvertes.fr/hal-01093971

A. Oliva and A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, International Journal of Computer Vision, vol.42, issue.3, pp.145-175, 2001.
DOI : 10.1023/A:1011139631724