Alexandre Gramfort

发表

MNE-Python: MEG and EEG data analysis with Python

Denis Engemann, Alexandre Gramfort, A. Gramfort, 2013 .

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

Bertrand Thirion, Marco Cuturi, Alexandre Gramfort, 2019, IPMI.

Celer: a Fast Solver for the Lasso with Dual Extrapolation

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2018, ICML.

OpenMEEG software for forward problems, coping with non-nested geometries

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2018 .

Sparse DSI: Learning DSI Structure for Denoising and Fast Imaging

Maxime Descoteaux, Cyril Poupon, Alexandre Gramfort, 2012, MICCAI.

Brain covariance selection: better individual functional connectivity models using population prior

Jean-Baptiste Poline, Gaël Varoquaux, Bertrand Thirion, 2010, NIPS.

Improved Brain Pattern Recovery through Ranking Approaches

Bertrand Thirion, Gael Varoquaux, Alexandre Gramfort, 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

Faster ICA Under Orthogonal Constraint

Alexandre Gramfort, Jean-François Cardoso, Pierre Ablin, 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning to rank from medical imaging data pdf

Bertrand Thirion, Gael Varoquaux, Alexandre Gramfort, 2012, MLMI.

EM algorithms for ICA pdf

Francis Bach, Alexandre Gramfort, Jean-François Cardoso, 2018, ArXiv.

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

Alexandre Gramfort, Mainak Jas, Eric Larson, 2018, Front. Neurosci..

Wasserstein regularization for sparse multi-task regression pdf

Marco Cuturi, Alexandre Gramfort, Hicham Janati, 2018, AISTATS.

Improved MEG/EEG source localization with reweighted mixed-norms

Jens Haueisen, Alexandre Gramfort, Daniel Strohmeier, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Debiased Sinkhorn barycenters

Marco Cuturi, Alexandre Gramfort, Hicham Janati, 2020, ICML.

Inverse problems with time-frequency dictionaries and non-white Gaussian noise

Matthieu Kowalski, Alexandre Gramfort, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Modeling Shared Responses in Neuroimaging Studies through MultiView ICA pdf

Luigi Gresele, Bertrand Thirion, Alexandre Gramfort, 2020, NeurIPS.

A priori par normes mixtes pour les problèmes inverses. Application à la localisation de sources en M/EEG

Matthieu Kowalski, Alexandre Gramfort, M. Kowalski, 2009, Traitement du Signal.

Dual Extrapolation for Sparse Generalized Linear Models pdf

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2019, ArXiv.

A Deep Learning Architecture to Detect Events in EEG Signals During Sleep

Stanislas Chambon, Valentin Thorey, Emmanuel Mignot, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models

Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

Total Variation Regularization for fMRI-Based Prediction of Behavior

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2011, IEEE Transactions on Medical Imaging.

Anomaly Detection and Localisation using Mixed Graphical Models

François Roueff, Alexandre Gramfort, Romain Laby, 2016, ICML 2016.

Encoding of event timing in the phase of neural oscillations

Alexandre Gramfort, Virginie van Wassenhove, Anne Kösem, 2014, NeuroImage.

An empirical evaluation of free BEM solvers for M/EEG forward modeling

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2010 .

Implicit differentiation of Lasso-type models for hyperparameter optimization pdf

Alexandre Gramfort, Joseph Salmon, Mathieu Blondel, 2020, ICML.

Software - MEG and EEG processing with OpenMEEG

Maureen Clerc, Alexandre Gramfort, Théo Papadopoulo, 2007 .

Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity

Bertrand Thirion, Francis R. Bach, Guillaume Obozinski, 2012, SIAM J. Imaging Sci..

scikit-learn: Just a test

Manoj Kumar, Gilles Louppe, Jake Vanderplas, 2015 .

MNE software for processing MEG and EEG data

Martin Luessi, Eric Larson, Christian Brodbeck, 2014, NeuroImage.

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression pdf

Alexandre Gramfort, Joseph Salmon, Vincent Leclère, 2016, ArXiv.

mne-python v0.10.2

Martin Luessi, Tal Linzen, Christian Brodbeck, 2015 .

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2013, ICLR.

New Results - Identifying predictive regions from fMRI with TV-ℓ1 prior

Bertrand Thirion, Alexandre Gramfort, Gaël Varoquaux, 2013 .

Data-driven HRF estimation for encoding and decoding models

Michael Eickenberg, Bertrand Thirion, Philippe Ciuciu, 2014, NeuroImage.

MEG/EEG group study with MNE: recommendations, quality assessments and best practices

Matti Hämäläinen, Denis Engemann, Alexandre Gramfort, 2017, bioRxiv.

OpenMEEG: opensource software for quasistatic bioelectromagnetics

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2010, Biomedical engineering online.

Machine learning for neuroimaging with scikit-learn

Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion, 2014, Front. Neuroinform..

Phase delays within visual cortex shape the response to steady-state visual stimulation

Bertrand Thirion, Maureen Clerc, Benoit Cottereau, 2011, NeuroImage.

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

Alexandre Gramfort, Pierre Ablin, Jean-Franccois Cardoso, 2020, Journal of Neuroscience Methods.

Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition

Laura Gwilliams, Alexandre Gramfort, Jean-Rémi King, 2017 .

Sparse pairwise Markov model learning for anomaly detection in heterogeneous data

François Roueff, Cyrille Enderli, Alexandre Gramfort, 2015 .

The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction

Jens Haueisen, Alexandre Gramfort, Daniel Strohmeier, 2016, IEEE Transactions on Medical Imaging.

The RAMP framework: from reproducibility to transparency in the design and optimization of scientific workflows

Mehdi Cherti, Balázs Kégl, Akin Kazakçi, 2018 .

Software and Platforms - Nilearn

Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion, 2013 .

Supramodal processing optimizes visual perceptual learning and plasticity

Philippe Ciuciu, Alexandre Gramfort, Nicolas Zilber, 2014, NeuroImage.

Heteroscedastic Concomitant Lasso for sparse multimodal electromagnetic brain imaging

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2017 .

Self-Supervised Representation Learning from Electroencephalography Signals

Aapo Hyvärinen, Isabela Albuquerque, Alexandre Gramfort, 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures

Michael Eickenberg, Bertrand Thirion, Mehdi Senoussi, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

Accelerating Likelihood Optimization for ICA on Real Signals

Alexandre Gramfort, Jean-François Cardoso, Pierre Ablin, 2018, LVA/ICA.

Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2011, MLINI.

Learning summary features of time series for likelihood free inference pdf

Alexandre Gramfort, Pedro L. C. Rodrigues, 2020, ArXiv.

Decoding the categorization of visual motion with magnetoencephalography

Alexandre Gramfort, Nicolas Zilber, Virginie van Wassenhove, 2017, bioRxiv.

FAμST: Speeding up linear transforms for tractable inverse problems

Rémi Gribonval, Alexandre Gramfort, Luc Le Magoarou, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Mind the Noise Covariance When Localizing Brain Sources with M/EEG

Eric Larson, Alexandre Gramfort, Denis A. Engemann, 2015, 2015 International Workshop on Pattern Recognition in NeuroImaging.

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

Yves Grenier, Alexandre Gramfort, Virginie van Wassenhove, 2017, bioRxiv.

Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries

Jens Haueisen, Matti S. Hämäläinen, Matthieu Kowalski, 2011, IPMI.

Learning step sizes for unfolded sparse coding

Alexandre Gramfort, Mathurin Massias, Pierre Ablin, 2019, NeurIPS.

The symmetric BEM: bringing in more variables for better accuracy

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2010 .

Faster Independent Component Analysis by Preconditioning With Hessian Approximations

Alexandre Gramfort, Jean-François Cardoso, Pierre Ablin, 2017, IEEE Transactions on Signal Processing.

Mind the duality gap: safer rules for the Lasso pdf

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2015, ICML.

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

Robert Oostenveld, Krzysztof J. Gorgolewski, Guillaume Flandin, 2018, Scientific Data.

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

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2020, AISTATS.

Calibration of One-Class SVM for MV set estimation

Albert Thomas, Alexandre Gramfort, Vincent Feuillard, 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

nibabel: 2.1.0

Samuel St-Jean, Marc-Alexandre Côté, Stephan Gerhard, 2016 .

Non-negative matrix factorization for single-channel EEG artifact rejection

Antoine Liutkus, Slim Essid, Alexandre Gramfort, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Learning from M/EEG Data with Variable Brain Activation Delays

Wojciech Zaremba, Matthew B. Blaschko, M. Pawan Kumar, 2013, IPMI.

M/EEG source localization with multi-scale time-frequency dictionaries

Roland Badeau, Alexandre Gramfort, Mainak Jas, 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

The Strength of Alpha–Beta Oscillatory Coupling Predicts Motor Timing Precision

Alexandre Gramfort, Virginie van Wassenhove, Tom Dupré la Tour, 2019, The Journal of Neuroscience.

Independent consultant pdf

Gilles Louppe, Gaël Varoquaux, Vlad Niculae, 2013 .

Biological and Computer Vision

Rachid Deriche, Christophe Lenglet, Carl-Fredrik Westin, 2005 .

OpenMEEG software for forward problems handling non-nested geometries

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2017 .

Benchmarking solvers for TV-ℓ1 least-squares and logistic regression in brain imaging

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations

Jens Haueisen, Matti S. Hämäläinen, Matthieu Kowalski, 2013, NeuroImage.

Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2011, IPMI.

Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity

Bertrand Thirion, Francis R. Bach, Guillaume Obozinski, 2011, 2011 International Workshop on Pattern Recognition in NeuroImaging.

GAP Safe screening rules for sparse multi-task and multi-class models

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2015, NIPS.

Software - Scikit learn

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2011 .

Multilayer Scattering Image Analysis Fits fMRI Activity in Visual Areas

Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort, 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

A supervised clustering approach for fMRI-based inference of brain states

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2011, Pattern Recognit..

Spatio-Temporal Alignments: Optimal transport through space and time pdf

Marco Cuturi, Alexandre Gramfort, Hicham Janati, 2020, AISTATS.

Gap Safe screening rules for sparsity enforcing penalties

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2016, J. Mach. Learn. Res..

Improving M/EEG source localizationwith an inter-condition sparse prior

Matthieu Kowalski, Alexandre Gramfort, M. Kowalski, 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

MNE: Software for Acquiring, Processing,and Visualizing MEG/EEG Data

Alexandre Gramfort, Mainak Jas, Denis A. Engemann, 2019, Magnetoencephalography.

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

Bertrand Thirion, Marco Cuturi, Alexandre Gramfort, 2019, NeuroImage.

Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals pdf

Alexandre Gramfort, Thomas Moreau, T. Moreau, 2019, ArXiv.

Fast retinotopic mapping of visual fields using MEG

Bertrand Thirion, Maureen Clerc, Jean Lorenceau, 2008 .

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

Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann, 2019, NeuroImage.

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

Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann, 2019, NeurIPS.

Tracking cortical activity from M/EEG using graph cuts with spatiotemporal constraints

Théodore Papadopoulo, Maureen Clerc, Sylvain Baillet, 2011, NeuroImage.

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

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2017, ArXiv.

Comparison of beamformer implementations for MEG source localization

Robert Oostenveld, Jan-Mathijs Schoffelen, A. K. Jaiswal, 2019, NeuroImage.

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2019, NeurIPS.

Domain adaptation with optimal transport improves EEG sleep stage classifiers

Stanislas Chambon, Alexandre Gramfort, Mathieu N. Galtier, 2018, 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

Robert Oostenveld, Krzysztof J. Gorgolewski, Guillaume Flandin, 2017 .

Gap Safe screening rules for faster complex-valued multi-task group Lasso

Alexandre Gramfort, Mathurin Massias, J. Salmon, 2017 .

Convolutional Network Layers Map the Function of the Human Visual Cortex

Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion, 2017, ERCIM News.

Two Distinct Dynamic Modes Subtend the Detection of Unexpected Sounds

Stanislas Dehaene, Alexandre Gramfort, Aaron Schurger, 2014, PloS one.

Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy pdf

Mehdi Cherti, Balázs Kégl, Alexandre Gramfort, 2017, ArXiv.

MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain

Jens Haueisen, Alexandre Gramfort, Daniel Strohmeier, 2015, 2015 International Workshop on Pattern Recognition in NeuroImaging.

Learning Hyperparameters for Unsupervised Anomaly Detection.

Stéphan Clémençon, Albert Thomas, Alexandre Gramfort, 2016, International Conference on Machine Learning.

Driver Estimation in Non-Linear Autoregressive Models

Yves Grenier, Alexandre Gramfort, Tom Dupré la Tour, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Deep learning-based electroencephalography analysis: a systematic review

Tiago H. Falk, Isabela Albuquerque, Alexandre Gramfort, 2019, Journal of neural engineering.

Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research

Riitta Salmelin, Cyril Pernet, Alexandre Gramfort, 2020, Nature Neuroscience.

MEG/EEG source localization based on structured sparsity in the time-frequency domain

Jens Haueisen, Matti Hämäläinen, Matthieu Kowalski, 2011 .

Scikit-learn: Machine Learning in Python

Gaël Varoquaux, Bertrand Thirion, Ron Weiss, 2011, J. Mach. Learn. Res..

Model identification and local linear convergence of coordinate descent pdf

Alexandre Gramfort, Joseph Salmon, Samuel Vaiter, 2020, ArXiv.

Seeing it all: Convolutional network layers map the function of the human visual system

Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion, 2017, NeuroImage.

MEG and EEG data analysis with MNE-Python

Martin Luessi, Matti Hämäläinen, Eric Larson, 2013, Front. Neuroinform..

Non-negative Tensor Factorization for single-channel EEG artifact rejection

Antoine Liutkus, Slim Essid, Alexandre Gramfort, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Parametric estimation of spectrum driven by an exogenous signal

Yves Grenier, Alexandre Gramfort, Tom Dupré la Tour, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Automated rejection and repair of bad trials in MEG/EEG

Alexandre Gramfort, Denis A. Engemann, Mainak Jas, 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2012, ICML.

Automated Measurement and Prediction of Consciousness in Vegetative and Minimally Conscious Patients

Stanislas Dehaene, Alexandre Gramfort, Denis A. Engemann, 2015, ICML 2015.

An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

Slim Essid, Alexandre Gramfort, Michel Le Van Quyen, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

DiCoDiLe: Distributed Convolutional Dictionary Learning

Alexandre Gramfort, Thomas Moreau, T. Moreau, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision. (Localisation et suivi d'activité fonctionnelle cérébrale en électro et magnétoencéphalographie: Méthodes et applications au système visuel humain)

Alexandre Gramfort, Alexandre Gramfort, 2009 .

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

Umut Simsekli, Alexandre Gramfort, Mainak Jas, 2017, NIPS.

DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal

Stanislas Chambon, Valentin Thorey, Emmanuel Mignot, 2018, Journal of Neuroscience Methods.

A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning

Alexandre Gramfort, Cédric Févotte, Herwig Wendt, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Hyperparameter estimation in maximum a posteriori regression using group sparsity with an application to brain imaging

Roland Badeau, Alexandre Gramfort, Yousra Bekhti, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Software and Platforms - Scikit learn

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2013 .

Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses

Maureen Clerc, Renaud Keriven, Alexandre Gramfort, 2010, IEEE Transactions on Biomedical Engineering.

GAP Safe Screening Rules for Sparse-Group Lasso

Alexandre Gramfort, Joseph Salmon, Olivier Fercoq, 2016, NIPS.

Oscillatory multiplexing indexes precision

Alexandre Gramfort, Virginie van Wassenhove, Tom Dupré la Tour, 2017, bioRxiv.

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

Bertrand Thirion, Alexandre Gramfort, Joseph Salmon, 2020, ArXiv.

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Stanislas Chambon, Gilles Wainrib, Alexandre Gramfort, 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Maxime Descoteaux, Cyril Poupon, Alexandre Gramfort, 2014, Medical Image Anal..

Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG

Alexandre Gramfort, Jean-François Cardoso, Jair Montoya-Martínez, 2017, LVA/ICA.

On the Consistency of Ordinal Regression Methods

Francis R. Bach, Alexandre Gramfort, Fabian Pedregosa, 2014, J. Mach. Learn. Res..

Blind Denoising with Random Greedy Pursuits

Gaël Richard, Laurent Daudet, Manuel Moussallam, 2014, IEEE Signal Processing Letters.

New Software and Platforms - Scikit learn

Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion, 2014 .

Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere

Anne Sabourin, Stéphan Clémençon, Albert Thomas, 2017, AISTATS.

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

Yves Grenier, Alexandre Gramfort, Virginie van Wassenhove, 2017 .

Stochastic algorithms with descent guarantees for ICA

Francis Bach, Alexandre Gramfort, Jean-François Cardoso, 2018, AISTATS.

Autoreject: Automated artifact rejection for MEG and EEG data

Alexandre Gramfort, Denis A. Engemann, Mainak Jas, 2016, NeuroImage.

A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2011, MLINI.

Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise pdf

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2019, NeurIPS 2019.

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann, 2019, bioRxiv.

Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Daniel S. Margulies, Julia M. Huntenburg, Satrajit S. Ghosh, 2011, Front. Neuroinform..

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

Gaël Varoquaux, Alexandre Gramfort, Franziskus Liem, 2020, eLife.

Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness

J. R. King, Stanislas Dehaene, Alexandre Gramfort, 2013, NeuroImage.

Decoding perceptual thresholds from MEG/EEG

Philippe Ciuciu, Alexandre Gramfort, Fabian Pedregosa, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

New Software and Platforms - Nilearn

Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion, 2014 .

Uncovering the structure of clinical EEG signals with self-supervised learning

Aapo Hyvärinen, Alexandre Gramfort, Hubert J. Banville, 2020, Journal of neural engineering.

Comparison of beamformer implementations for MEG source localization

Robert Oostenveld, Jan-Mathijs Schoffelen, John C. Mosher, 2020, NeuroImage.

OpenMEEG for M/EEG forward modeling: a comparison study

Maureen Clerc, Alexandre Gramfort, Emmanuel Olivi, 2010 .

Identifying Predictive Regions from fMRI with TV-L1 Prior

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

Beyond Pham's algorithm for joint diagonalization pdf

Alexandre Gramfort, Jean-François Cardoso, Pierre Ablin, 2018, ESANN.

Computer and biological vision

Rachid Deriche, Pierre Kornprobst, Romain Veltz, 2007 .

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals

Alexandre Gramfort, Denis A. Engemann, D. Engemann, 2015, NeuroImage.

Decoding Visual Percepts Induced by Word Reading with fMRI

Gaël Varoquaux, Bertrand Thirion, Alexandre Gramfort, 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

Electro-Metabolic Coupling Investigated with Jitter Invariant Dictionary Learning

Christian Bénar, Théodore Papadopoulo, Maureen Clerc, 2014 .

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2017, AISTATS.

Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction

Danilo Bzdok, Gaël Varoquaux, Bertrand Thirion, 2017, NIPS 2017.

Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals

Alexandre Gramfort, Mainak Jas, Tom Dupré la Tour, 2018, NeurIPS.

Fast Optimal Transport Averaging of Neuroimaging Data

Gabriel Peyré, Marco Cuturi, Alexandre Gramfort, 2015, IPMI.

Forward Field Computation with OpenMEEG

Théodore Papadopoulo, Maureen Clerc, Alexandre Gramfort, 2011, Comput. Intell. Neurosci..

Disentangling syntax and semantics in the brain with deep networks

Alexandre Gramfort, Jean-Remi King, Charlotte Caucheteux, 2021, ICML.

Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements pdf

Demian Wassermann, Alexandre Gramfort, Maeliss Jallais, 2021, ArXiv.

A unified view on beamformers for M/EEG source reconstruction

Robert Oostenveld, John C. Mosher, Vladimir Litvak, 2021, NeuroImage.

DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals pdf

Alexandre Gramfort, Thomas Moreau, C'edric Allain, 2021, ArXiv.

Implicit differentiation for fast hyperparameter selection in non-smooth convex learning pdf

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2021, ArXiv.

Decomposing lexical and compositional syntax and semantics with deep language models pdf

Alexandre Gramfort, Jean-Remi King, Charlotte Caucheteux, 2021, ArXiv.

Adaptive Multi-View ICA: Estimation of noise levels for optimal inference pdf

Alexandre Gramfort, Pierre Ablin, Saclay, 2021, ArXiv.

CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals pdf

Alexandre Gramfort, Thomas Moreau, C'edric Rommel, 2021, ArXiv.

Leveraging Global Parameters for Flow-based Neural Posterior Estimation pdf

Gilles Louppe, Alexandre Gramfort, Thomas Moreau, 2021, ArXiv.

Long-range and hierarchical language predictions in brains and algorithms pdf

Alexandre Gramfort, Jean-Remi King, Charlotte Caucheteux, 2021, ArXiv.

Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects pdf

Alexandre Gramfort, Charlotte Caucheteux, Jean-R'emi King, 2021, EMNLP.

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso pdf

Luigi Nardi, Alexandre Gramfort, Joseph Salmon, 2021, ArXiv.

Shared Independent Component Analysis for Multi-Subject Neuroimaging pdf

Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, 2021, NeurIPS.

Robust learning from corrupted EEG with dynamic spatial filtering pdf

Alexandre Gramfort, Chris Aimone, Hubert J. Banville, 2021, NeuroImage.

Electromagnetic neural source imaging under sparsity constraints with SURE-based hyperparameter tuning

Alexandre Gramfort, Joseph Salmon, Quentin Bertrand, 2021, 2112.12178.

HNPE: Leveraging Global Parameters for Neural Posterior Estimation

Alexandre Gramfort, Thomas Moreau, Pedro L. C. Rodrigues, 2021, NeurIPS.

Cytoarchitecture Measurements in Brain Gray Matter Using Likelihood-Free Inference

Demian Wassermann, Alexandre Gramfort, Pedro L. C. Rodrigues, 2021, IPMI.

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

Clemens Brunner, Romain Quentin, Alexandre Gramfort, 2019, J. Open Source Softw..

Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach.

Mehdi Cherti, Balázs Kégl, Alexandre Gramfort, 2018, Talanta.

Accelerating ISTA with an active set strategy

Matthieu Kowalski, Alexandre Gramfort, Sandrine Anthoine, 2011 .

POT: Python Optimal Transport

Nicolas Courty, Ievgen Redko, Stanislas Chambon, 2021, J. Mach. Learn. Res..

MEG/EEG source reconstruction based on Gabor thresholding in the source space

Jens Haueisen, Matthieu Kowalski, Alexandre Gramfort, 2011, 2011 8th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the 2011 8th International Conference on Bioelectromagnetism.