Signal processing and Learning Applied to Brain data

Beyond Pham's algorithm for joint diagonalization

Autori: Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Pubblicato in: ESSAN 2019 - 27th European symposium on artificial neural networks, 2019
Editore: ESSAN

Wasserstein regularization for sparse multi-task regression

Autori: Hicham Janati, Marco Cuturi, Alexandre Gramfort
Pubblicato in: Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 2019
Editore: PMLR

Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals

Autori: Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort
Pubblicato in: Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018
Editore: Curran Associates, Inc.

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

Autori: Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Vincent Leclère, Joseph Salmon
Pubblicato in: Journal of Physics: Conference Series, 2017
Editore: IOP Publishing
DOI: 10.1088/1742-6596/904/1/012006

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso

Autori: Jérôme-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon, Bertrand Thirion
Pubblicato in: Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020
Editore: MIT Press

Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

Autori: Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Pubblicato in: Advances in Neural Information Processing Systems 33 (NeurIPS), 2020
Editore: MIT Press

Manifold-regression to predict from MEG/EEG brain signals without source modeling

Autori: David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann
Pubblicato in: 2019
Editore: Curran Associates, Inc.

Stochastic algorithms with descent guarantees for ICA

Autori: Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach
Pubblicato in: Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 2019
Editore: PMLR

A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning

Autori: Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte
Pubblicato in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020
Editore: IEEE
DOI: 10.1109/icassp.2019.8683291

HNPE: Leveraging Global Parameters for Neural Posterior Estimation

Autori: Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort
Pubblicato in: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021
Editore: MIT Press

Learning step sizes for unfolded sparse coding

Autori: Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort
Pubblicato in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
Editore: Curran Associates, Inc.

Implicit differentiation of Lasso-type models for hyperparameter optimization

Autori: Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Proceedings of the 37th International Conference on Machine Learning, 2020
Editore: PMLR

Support recovery and sup-norm convergence rates for sparse pivotal estimation

Autori: Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, 2020
Editore: PMLR

Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates

Autori: Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
Pubblicato in: IPMI 2019 - 26th international conference on Information Processing in Medical Imaging, 2021
Editore: Lecture Notes in Computer Science

Faster ICA Under Orthogonal Constraint

Autori: Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Pubblicato in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Editore: IEEE
DOI: 10.1109/icassp.2018.8461662

Hyperparameter Estimation in Maximum a Posteriori Regression Using Group Sparsity with an Application to Brain Imaging

Autori: Badeau, Roland; Bekhti, Yousra; Gramfort, Alexandre
Pubblicato in: 25th European Signal Processing Conference (EUSIPCO), Numero 5, 2017, ISSN 2076-1465
Editore: IEEE
DOI: 10.5281/zenodo.1159734

Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding

Autori: Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort
Pubblicato in: Advances in Neural Information Processing Systems 30, 2017, Pagina/e 1099--1108
Editore: Curran Associates, Inc.

Parametric estimation of spectrum driven by an exogenous signal

Autori: Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort
Pubblicato in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, Pagina/e 4301-4305, ISBN 978-1-5090-4117-6
Editore: IEEE
DOI: 10.1109/ICASSP.2017.7952968

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Autori: Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, 2018, Pagina/e 998--1007
Editore: PMLR

Celer: a Fast Solver for the Lasso with Dual Extrapolation

Autori: Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Proceedings of the 35th International Conference on Machine Learning, 2018, Pagina/e 3315--3324
Editore: PMLR

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Autori: Massias , Mathurin; Fercoq , Olivier; Gramfort , Alexandre; Salmon , Joseph
Pubblicato in: 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Apr 2018, Lanzarote, Spain, Numero 5, 2018
Editore: AISTATS

Driver Estimation in Non-Linear Autoregressive Models

Autori: Tom Duprela Tour, Yves Grenier, Alexandre Gramfort
Pubblicato in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, Pagina/e 4519-4523, ISBN 978-1-5386-4658-8
Editore: IEEE
DOI: 10.1109/ICASSP.2018.8462268

GAP Safe Screening Rules for Sparse-Group Lasso

Autori: Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Advances in Neural Information Processing Systems 29, 2016, Pagina/e 388--396
Editore: Curran Associates, Inc.

Caveats with stochastic gradient and maximum likelihood based ICA for EEG

Autori: Jair Montoya-Martínez, Jean-François Cardoso, Alexandre Gramfort
Pubblicato in: 2017, Pagina/e 279-289
Editore: Springer International Publishing
DOI: 10.1007/978-3-319-53547-0_27

Debiased Sinkhorn barycenters

Autori: Hicham Janati, Marco Cuturi, Alexandre Gramfort
Pubblicato in: Proceedings of the 37th International Conference on Machine Learning, 2020
Editore: PMLR

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso

Autori: Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
Editore: Curran Associates, Inc.

Spatio-Temporal Alignments: Optimal transport through space and time

Autori: Hicham Janati, Marco Cuturi, Alexandre Gramfort
Pubblicato in: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, 2020
Editore: PMLR

Shared Independent Component Analysis for Multi-Subject Neuroimaging

Autori: Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
Pubblicato in: Advances in Neural Information Processing Systems 34 (NeurIPS), 2020
Editore: MIT Press

DiCoDiLe: Distributed Convolutional Dictionary Learning

Autori: Thomas Moreau, Alexandre Gramfort
Pubblicato in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, ISSN 1939-3539
Editore: IEEE
DOI: 10.1109/tpami.2020.3039215

Spectral Independent Component Analysis with noise modeling for M/EEG source separation

Autori: Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Pubblicato in: Journal of Neuroscience Methods, 2021, ISSN 0165-0270
Editore: Elsevier BV
DOI: 10.1016/j.jneumeth.2021.109144

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis

Autori: Appelhoff, Stefan and Sanderson, Matthew and Brooks, Teon and Vliet, Marijn van and Quentin, Romain and Holdgraf, Chris and Chaumon, Maximilien and Mikulan, Ezequiel and Tavabi, Kambiz and Höchenberger, Richard and Welke, Dominik and Brunner, Clemens and Rockhill, Alexander and Larson, Eric and Gramfort, Alexandre and Jas, Mainak
Pubblicato in: Journal of Open Source Software, 2019, ISSN 2475-9066
Editore: The Open Journal
DOI: 10.21105/joss.01896

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

Autori: Denis A Engemann, Oleh Kozynets, David Sabbagh, Guillaume Lemaître, Gael Varoquaux, Franziskus Liem, Alexandre Gramfort
Pubblicato in: eLife, 2019, ISSN 2050-084X
Editore: eLife Sciences Publications
DOI: 10.7554/elife.54055

A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging

Autori: Yousra Bekhti, Felix Lucka, Joseph Salmon, Alexandre Gramfort
Pubblicato in: Inverse Problems, Numero 34/8, 2018, Pagina/e 085010, ISSN 0266-5611
Editore: Institute of Physics Publishing
DOI: 10.1088/1361-6420/aac9b3

Gap Safe screening rules for sparsity enforcing penalties

Autori: Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Salmon, Joseph
Pubblicato in: Journal of Machine Learning Research, Numero 5, 2017, ISSN 1532-4435
Editore: MIT Press

Non-linear auto-regressive models for cross-frequency coupling in neural time series

Autori: Tom Dupré la Tour, Lucille Tallot, Laetitia Grabot, Valérie Doyère, Virginie van Wassenhove, Yves Grenier, Alexandre Gramfort
Pubblicato in: PLOS Computational Biology, Numero 13/12, 2017, Pagina/e e1005893, ISSN 1553-7358
Editore: PLOS Computational Biology
DOI: 10.1371/journal.pcbi.1005893

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography

Autori: Guiomar Niso, Krzysztof J. Gorgolewski, Elizabeth Bock, Teon L. Brooks, Guillaume Flandin, Alexandre Gramfort, Richard N. Henson, Mainak Jas, Vladimir Litvak, Jeremy T. Moreau, Robert Oostenveld, Jan-Mathijs Schoffelen, Francois Tadel, Joseph Wexler, Sylvain Baillet
Pubblicato in: Scientific Data, Numero 5, 2018, Pagina/e 180110, ISSN 2052-4463
Editore: Springer Nature
DOI: 10.1038/sdata.2018.110

Faster Independent Component Analysis by Preconditioning With Hessian Approximations

Autori: Pierre Ablin, Jean-Francois Cardoso, Alexandre Gramfort
Pubblicato in: IEEE Transactions on Signal Processing, Numero 66/15, 2018, Pagina/e 4040-4049, ISSN 1053-587X
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/TSP.2018.2844203

Autoreject: Automated artifact rejection for MEG and EEG data

Autori: Mainak Jas, Denis A. Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort
Pubblicato in: NeuroImage, Numero 159, 2017, Pagina/e 417-429, ISSN 1053-8119
Editore: Academic Press
DOI: 10.1016/j.neuroimage.2017.06.030

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices

Autori: Mainak Jas, Eric Larson, Denis A. Engemann, Jaakko Leppäkangas, Samu Taulu, Matti Hämäläinen, Alexandre Gramfort
Pubblicato in: Frontiers in Neuroscience, Numero 12, 2018, ISSN 1662-453X
Editore: Frontiers
DOI: 10.3389/fnins.2018.00530

Multi-subject MEG/EEG source imaging with sparse multi-task regression

Autori: Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
Pubblicato in: NeuroImage, 2020, ISSN 1053-8119
Editore: Academic Press
DOI: 10.1016/j.neuroimage.2020.116847

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Autori: David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A Engemann
Pubblicato in: NeuroImage, 2020, ISSN 1053-8119
Editore: Academic Press
DOI: 10.1016/j.neuroimage.2020.116893

From safe screening rules to working sets for faster Lasso-type solvers

Autori: Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Pubblicato in: Workshop NIPS OPTML, 2017
Editore: Arxiv

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