@article { gramfort-etal:2012a,
month = {Mar},
year = {2012},
doi = {10.1088/0031-9155/57/7/1937 },
number = {7 },
volume = {57},
journal = {Physics in Medicine and Biology},
pages = {1937-1961},
publisher = {IOP Science},
affiliation = {PARIETAL - INRIA Saclay - Ile de France, LNAO CEA Neurospin, Laboratoire des signaux et systemes (L2S) , Athinoula A. Martinos Center for Biomedical Imaging},
language = {Anglais},
abstract = {Magneto- and electroencephalography (M/EEG) measure the electromagnetic fields produced by the neural electrical currents. Given a conductor model for the head, and the distribution of source currents in the brain, Maxwell's equations allow one to compute the ensuing M/EEG signals. Given the actual M/EEG measurements and the solution of this forward problem, one can localize, in space and in time, the brain regions that have produced the recorded data. However, due to the physics of the problem, the limited number of sensors compared to the number of possible source locations, and measurement noise, this inverse problem is ill-posed. Consequently, additional constraints are needed. Classical inverse solvers, often called minimum norm estimates (MNE), promote source estimates with a small ℓ(2) norm. Here, we consider a more general class of priors based on mixed norms. Such norms have the ability to structure the prior in order to incorporate some additional assumptions about the sources. We refer to such solvers as mixed-norm estimates (MxNE). In the context of M/EEG, MxNE can promote spatially focal sources with smooth temporal estimates with a two-level ℓ(1)/ℓ(2) mixed-norm, while a three-level mixed-norm can be used to promote spatially non-overlapping sources between different experimental conditions. In order to efficiently solve the optimization problems of MxNE, we introduce fast first-order iterative schemes that for the ℓ(1)/ℓ(2) norm give solutions in a few seconds making such a prior as convenient as the simple MNE. Furthermore, thanks to the convexity of the optimization problem, we can provide optimality conditions that guarantee global convergence. The utility of the methods is demonstrated both with simulations and experimental MEG data.},
title = {Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods.},
url = {http://hal.inria.fr/hal-00690774},
hal_id = {hal-00690774},
author = {Gramfort, Alexandre and Kowalski, Matthieu and H{\"a}m{\"a}l{\"a}inen, Matti}
}

@incollection { gramfort-etal:2011c,
year = {2011},
doi = {10.1007/978-3-642-22092-0_49},
url = {http://dx.doi.org/10.1007/978-3-642-22092-0_49},
volume = {6801},
pages = {600-611},
isbn = {},
publisher = {Springer Berlin / Heidelberg},
editor = {Székely, Gábor and Hahn, Horst},
series = {Lecture Notes in Computer Science},
booktitle = {Information Processing in Medical Imaging},
title = {Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries},
affiliation = {INRIA, Parietal team, Saclay, France},
author = {Gramfort, Alexandre and Strohmeier, Daniel and Haueisen, Jens and Hamalainen, Matti and Kowalski, Matthieu}
}

@article { michel-etal:2011b,
url = {http://hal.inria.fr/inria-00563468/en/},
issn = {0278-0062},
doi = {10.1109/TMI.2011.2113378},
keywords = {brain mapping; fMRI; multivariate pattern analysis; predictive diagnosis; total variation regularization; image classification},
pages = {1328-1340},
number = {7},
volume = {30},
month = {july},
year = {2011},
title = {Total Variation Regularization for fMRI-Based Prediction of Behavior},
journal = {Medical Imaging, IEEE Transactions on},
author = {Michel, V. and Gramfort, A. and Varoquaux, G. and Eger, E. and Thirion, B.}
}

@inproceedings { varoquaux-etal:2011,
pdf = {http://hal.inria.fr/inria-00588898/PDF/paper.pdf},
month = {Jul},
year = {2011},
organization = {G{\'a}bor Sz\'ekely, Horst Hahn},
address = {Kaufbeuren, Allemagne},
booktitle = {Information Processing in Medical Imaging},
affiliation = {Laboratoire de Neuroimagerie Assist\'ee par Ordinateur - LNAO - CEA : DSV/I2BM/NEUROSPIN - PARIETAL - INRIA Saclay - Ile de France - INRIA - Neuroimagerie cognitive - INSERM : U992 - Universit\'e Paris Sud - Paris XI - CEA : DSV/I2BM/NEUROSPIN},
keywords = {sparse models, atlas, resting state, segmentation},
title = {Multi-subject dictionary learning to segment an atlas of brain spontaneous activity},
url = {http://hal.inria.fr/inria-00588898/en/},
hal_id = {inria-00588898},
author = {Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Pedregosa, Fabian and Michel, Vincent and Thirion, Bertrand}
}

@article { michel-etal:2011,
pdf = {http://hal.inria.fr/inria-00589201/PDF/supervised\_clustering\_vm\_review.pdf},
month = {Apr},
year = {2011},
doi = {10.1016/j.patcog.2011.04.006 },
journal = {Pattern Recognition},
pages = {epub ahead of print},
publisher = {elsevier},
affiliation = {Laboratoire de Neuroimagerie Assist\'ee par Ordinateur - LNAO - CEA : DSV/I2BM/NEUROSPIN - PARIETAL - INRIA Saclay - Ile de France - INRIA - Neuroimagerie cognitive - INSERM : U992 - Universit\'e Paris Sud - Paris XI - CEA : DSV/I2BM/NEUROSPIN - SELECT - INRIA Saclay - Ile de France - INRIA - Universit\'e Paris Sud - Paris XI - CNRS : UMR - Laboratoire de Math\'ematiques d'Orsay - LM-Orsay - CNRS : UMR8628 - Universit\'e Paris Sud - Paris XI},
keywords = {fMRI; brain reading; prediction; hierarchical clustering; dimension reduction; multi-scale analysis; feature agglomeration},
title = {A supervised clustering approach for fMRI-based inference of brain states},
url = {http://hal.inria.fr/inria-00589201/en/},
hal_id = {inria-00589201},
author = {Michel, Vincent and Gramfort, Alexandre and Varoquaux, Ga{\"e}l and Eger, Evelyn and Keribin, Christine and Thirion, Bertrand}
}

@techreport { jenatton-etal:2011,
pdf = {http://hal.inria.fr/inria-00589785/PDF/sparse\_hierarchical\_fmri\_mining\_HAL.pdf},
month = {May},
year = {2011},
type = {Rapport de recherche},
pages = {16},
affiliation = {Laboratoire d'informatique de l'\'ecole normale sup\'erieure - LIENS - CNRS : UMR8548 - Ecole Normale Sup\'erieure de Paris - ENS Paris - SIERRA - INRIA Paris - Rocquencourt - INRIA : PARIS - ROCQUENCOURT - Ecole Normale Sup\'erieure de Paris - ENS Paris - CNRS : UMR8548 - Laboratoire de Neuroimagerie Assist\'ee par Ordinateur - LNAO - CEA : DSV/I2BM/NEUROSPIN - PARIETAL - INRIA Saclay - Ile de France - INRIA - Neuroimagerie cognitive - INSERM : U992 - Universit\'e Paris Sud - Paris XI - CEA : DSV/I2BM/NEUROSPIN},
keywords = {brain reading; structured sparsity; convex optimization; sparse hierarchical models; inter-subject validation; proximal methods},
title = {Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity},
url = {http://hal.inria.fr/inria-00589785/en/},
hal_id = {inria-00589785},
author = {Jenatton, Rodolphe and Gramfort, Alexandre and Michel, Vincent and Obozinski, Guillaume and Eger, Evelyn and Bach, Francis and Thirion, Bertrand}
}

@article { gramfort-etal:2011b,
keywords = {Functional brain imaging, Tracking, Graph Cuts Optimization, Magnetoencephalography (MEG), Electroencephalography (EEG)},
url = {http://www.sciencedirect.com/science/article/B6WNP-5161PBP-4/2/a788648433443badba4516b1d451b049},
doi = {DOI: 10.1016/j.neuroimage.2010.09.087},
issn = {1053-8119},
month = {Feb},
year = {2011},
pages = {1930-1941},
number = {3},
volume = {54},
journal = {NeuroImage},
title = {Tracking cortical activity from M/EEG using graph-cuts with spatiotemporal constraints},
author = {Alexandre Gramfort and Theodore Papadopoulo and Sylvain Baillet and Maureen Clerc}
}

@article { cottereau-etal:2011a,
keywords = {Vision, Retinotopy, Magnetoencephalography (MEG), Steady-State Visual, Evoked Response (SSVER), Source Imaging},
url = {http://www.sciencedirect.com/science/article/B6WNP-516M75G-2/2/69bf20b0381f2b20b6e6b6409d67abe0},
doi = {DOI: 10.1016/j.neuroimage.2010.10.004},
issn = {1053-8119},
note = {},
year = {2011},
pages = {1919 - 1929},
number = {3},
volume = {54},
journal = {NeuroImage},
title = {Phase delays within visual cortex shape the response to steady-state visual stimulation},
author = {Benoit Cottereau and Jean Lorenceau and Alexandre Gramfort and Maureen Clerc and Bertrand Thirion and Sylvain Baillet}
}

@article { gramfort-etal:10b,
keywords = {Boundary Element Method, Electromagnetics, quasistatic regime, Electroencephalography, Magnetoencephalography, Forward modeling, opensource software},
abstract = {BACKGROUND:Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to present OpenMEEG, both from the theoretical and the practical point of view, and to compare its performances with other competing software packages.METHODS:We have run a benchmark study in the field of electro- and magneto-encephalography, in order to compare the accuracy of OpenMEEG with other freely distributed forward solvers. We considered spherical models, for which analytical solutions exist, and we designed randomized meshes to assess the variability of the accuracy. Two measures were used to characterize the accuracy. the Relative Difference Measure and the Magnitude ratio. The comparisons were run, either with a constant number of mesh nodes, or a constant number of unknowns across methods. Computing times were also compared.RESULTS:We observed more pronounced differences in accuracy in electroencephalography than in magnetoencephalography. The methods could be classified in three categories: the linear collocation methods, that run very fast but with low accuracy, the linear collocation methods with isolated skull approach for which the accuracy is improved, and OpenMEEG that clearly outperforms the others. As far as speed is concerned, OpenMEEG is on par with the other methods for a constant number of unknowns, and is hence faster for a prescribed accuracy level.CONCLUSIONS:This study clearly shows that OpenMEEG represents the state of the art for forward computations. Moreover, our software development strategies have made it handy to use and to integrate with other packages. The bioelectromagnetic research community should therefore be able to benefit from OpenMEEG with a limited development effort.},
issn = {1475-925X},
pubmedid = {20819204},
doi = {10.1186/1475-925X-9-45},
url = {http://www.biomedical-engineering-online.com/content/9/1/45},
pages = {45},
number = {1},
year = {2010},
volume = {9},
journal = {BioMedical Engineering OnLine},
title = {OpenMEEG: opensource software for quasistatic bioelectromagnetics},
author = {Gramfort, Alexandre and Papadopoulo, Theodore and Olivi, Emmanuel and Clerc, Maureen}
}

@inproceedings { varoquaux-etal:2010,
year = {2010},
title = {Brain covariance selection: better individual functional connectivity models using population prior.},
publisher = {Curran Associates, Inc.},
pages = {2334-2342},
url = {http://books.nips.cc/papers/files/nips23/NIPS2010_1054.pdf},
editor = {Lafferty, John D. and Williams, Christopher K. I. and Shawe-Taylor, John and Zemel, Richard S. and Culotta, Aron},
booktitle = {NIPS},
author = {Varoquaux, Gael and Gramfort, Alexandre and Poline, Jean-Baptiste and Thirion, Bertrand}
}

@unpublished { kowalski-gramfort:2010,
year = {2010},
url = {http://hal.archives-ouvertes.fr/hal-00473970/en/},
affiliation = {Laboratoire des signaux et systèmes (L2S) - UMR8506 CNRS - SUPELEC - Univ Paris-Sud - PARIETAL - INRIA Saclay - Ile de France - INRIA },
keywords = {normes mixtes ; problème inverse ; opérateurs de proximité ; électroencéphalographie ; magnétoencéphalographie},
title = {A priori par normes mixtes pour les problèmes inverses: Application à la localisation de sources en M/EEG},
author = {Kowalski, Matthieu and Gramfort, Alexandre}
}

@inproceedings { gramfort-etal:10c,
doi = {10.3389/conf.fnins.2010.06.00111},
url = {http://hal.archives-ouvertes.fr/inria-00468592/en/},
note = {Winning Paper of the Young Investigator Award},
booktitle = {Biomag: International Conference on Biomagnetism},
year = {2010},
affiliation = {PARIETAL - INRIA Saclay - Ile de France - INRIA},
keywords = {MEG, EEG, Inverse Problem, Sparse prior, IRLS},
title = {Multi-condition M/EEG inverse modeling with sparsity assumptions: how to estimate what is common and what is specific in multiple experimental conditions},
author = {Gramfort, Alexandre}
}

@inproceedings { gramfort-etal:10d,
doi = {10.3389/conf.fnins.2010.06.00065},
url = {files/poster_openmeeg_biomag_2010.pdf},
booktitle = {Biomag: International Conference on Biomagnetism},
year = {2010},
affiliation = {PARIETAL - INRIA Saclay - Ile de France - INRIA},
keywords = {MEG, EEG, Forward Problem, BEM},
title = {An empirical evaluation of free BEM solvers for accurate M/EEG forward modeling},
author = {Gramfort, Alexandre and Papadopoulo, Th\'eodore and Olivi, Emmanuel and Clerc, Maureen}
}

@article { gramfort-etal:10,
url = {http://www.ncbi.nlm.nih.gov/pubmed/20142163},
keywords = {Electroencephalography, Electroencephalography (EEG), Estimation, Evoked potentials, Graph Cuts Optimization, Graph Laplacian, Latency estimation, Magnetoencephalography (MEG), Manifold learning, Principal component analysis, Single-trial analysis, Time series analysis},
doi = {10.1109/TBME.2009.2037139},
issn = {0018-9294},
pages = {1051 -1061},
number = {5},
volume = {57},
month = {may },
year = {2010},
journal = {Biomedical Engineering, IEEE Transactions on},
title = {Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses},
author = {Gramfort, A. and Keriven, R. and Clerc, M.}
}

@inproceedings { kowalski-gramfort:09,
url = {http://hal.archives-ouvertes.fr/hal-00424039/},
keywords = {Magnetoencephalographie, Electroencephalographie, Problème inverse, Elitist-Lasso, Operateurs de proximité},
month = {sept},
booktitle = {GRETSI},
topic = {Odyssee},
year = {2009},
title = {A priori par normes mixtes pour les problèmes inverses Application à la localisation de sources en M/EEG},
author = {Mathieu Kowalski and Alexandre Gramfort}
}

@inproceedings { cottereau-laurenceau-etal:09,
keywords = {MEG, retinotopy, chronometry, human vision},
month = {jun},
booktitle = {Human Brain Mapping},
topic = {Odyssee},
year = {2009},
title = {Fine chronometric mapping of human visual areas},
author = {B. Cottereau, J. Lorenceau, A. Gramfort, M. Clerc, S. Baillet}
}

@inproceedings { gramfort-kowalski:09,
url = {http://hal.archives-ouvertes.fr/hal-00424029/},
keywords = {Magnetoencephalography, Electroencephalography, Inverse problem, Elitist-Lasso, Proximal iterations},
month = {jun},
booktitle = {IEEE International Symposium on Biomedical Imaging},
topic = {Odyssee},
year = {2009},
title = {Improving M/EEG source localization with an inter-condition sparse prior},
author = {Alexandre Gramfort and Mathieu Kowalski}
}

@inproceedings { gramfort-papadopoulo-etal:08,
url = {http://hal.inria.fr/inria-00336887/fr/},
keywords = {MEG, somatosensory, graph-cuts, tracking},
month = {aug},
booktitle = {Biomag: International Conference on Biomagnetism},
topic = {Odyssee},
year = {2008},
title = {Tracking cortical activity with spatio-temporal constraints using graph-cuts},
author = {Alexandre Gramfort and Théodore Papadopoulo and Benoit Cottereau and Sylvain Baillet and Maureen Clerc}
}

@inproceedings { cottereau-gramfort-etal:08,
owner = { agramfor },
keywords = {MEG, retinotopy},
group = {Odyssee},
month = {jun},
booktitle = {Human Brain Mapping},
topic = {Odyssee},
year = {2008},
title = {Fast retinotopic mapping of visual fields using MEG},
author = {Benoit Cottereau and Alexandre Gramfort and Jean Lorenceau and Bertrand Thirion and Maureen Clerc and Sylvain Baillet}
}

@inproceedings { gramfort-cottereau-etal:07,
owner = { agramfor },
url = {ftp://ftp-sop.inria.fr/odyssee/Publications/2007/gramfort-cottereau-etal:07.pdf},
annote = {Detection of activity from the primary visual cortex is a difficult challenge to magneto-encephalography (MEG) source imaging techniques: the geometry of the visual cortex is intricate, with structured visual field maps extending deeper along the calcarine fissure. This questions the very sensitivity of MEG to the corresponding neural responses of visual stimuli and the usage of MEG source imaging for innovative retinotopic explorations. In this context, we compare two imaging models of MEG generators in realistic simulations of activations within the visual cortex. Localization and spatial extent of neural activity in the visual cortex were extracted from retinotopic maps obtained in fMRI. We prove that the suggested approaches are robust and succeed in accurately recovering the activation patterns with satisfactory match with fMRI results. These results suggest that fast retinotopic exploration of the visual cortex could be obtained from MEG as a complementary alternative to more standard fMRI approaches. The excellent time resolution of MEG imaging further opens interesting perspectives on the temporal and spectral processes sustained by the human visual system.},
keywords = {Retinotopy, MEG, fMRI, Visual Cortex},
group = {Odyssee},
month = {aug},
pages = {4945-4948},
booktitle = {EMBC 2007: IEEE, Engineering in Medicine and Biology Society},
topic = {Odyssee},
year = {2007},
title = {Challenging the estimation of cortical activity from MEG with simulated fMRI-constrained retinotopic maps},
author = {Gramfort, Alexandre and Cottereau, Benoit and Clerc, Maureen and Thirion, Bertrand and Baillet, Sylvain}
}

@inproceedings { gramfort-clerc:07,
owner = { agramfor },
keywords = {EEG, Event-related potentials, Laplacian eigenmaps, P300, dimensionality reduction},
url = {ftp://ftp-sop.inria.fr/odyssee/Publications/2007/gramfort-clerc:07.pdf},
annote = {Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts of data and lead to the manipulations of high dimensional objects like time series or topographies. In the past, essentially in the last decade, various methods for extracting the structure in complex data have been developed and successfully exploited for visualization or classification purposes. Here we propose to use one of these methods, the Laplacian eigenmaps, on EEG data and prove that it provides an powerful approach to visualize and understand the underlying structure of evoked potentials or multitrial time series.},
group = {Odyssee},
month = {oct},
pages = {169-172},
booktitle = {NFSI 2007: Symposium on Noninvasive Functional Source Imaging},
topic = {Misc},
year = {2007},
title = {Low dimensional representations of {MEG/EEG} data using laplacian eigenmaps},
author = {Gramfort, Alexandre and Clerc, Maureen}
}

@inproceedings { clerc-gramfort-etal:07,
owner = { papadop },
keywords = {EEG, MEG, forward modeling, boundary element method},
group = {Odyssee},
booktitle = {Proceedings of Neuromath},
topic = {Odyssee},
year = {2007},
title = {MEG and EEG processing with OpenMEEG},
author = {Clerc, Maureen and Gramfort, Alexandre and Landreau, Perrine and Papadopoulo, Théodore}
}

@inproceedings { cottereau-laurenceau-etal:07,
owner = { papadop },
keywords = {MEG, retinotopy, human vision},
group = {Odyssee},
booktitle = {Proceedings of Neuromath},
topic = {Misc},
year = {2007},
title = {Fast Retinotopic Mapping of Visual Fields using MEG},
author = {Benoit Cottereau and Jean Lorenceau and Alexandre Gramfort and Bertrand Thirion and Maureen Clerc and Sylvain Baillet}
}