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Signal processing and Learning Applied to Brain data

Total Cost €


EC-Contrib. €






 SLAB project word cloud

Explore the words cloud of the SLAB project. It provides you a very rough idea of what is the project "SLAB" about.

modern    21st    magnetoencephalography    electroencephalography    mathematical    sensors    autism    century    datasets    boost    signals    quality    reduce    levels    accurate    astronomy    1929    healthy    heterogeneous    sleep    generation    cognitive    clinical    lack    questions    brain    keeps    heterogeneity    birth    functional    interactions    revolutionized    modeling    biology    machine    twenty    foundations    neural    localization    time    tools    head    mesoscale    limited    physics    mining    pathological    tremor    ensembles    stationary    works    favor    first    learning    mri    disorders    eeg    computational    spectral    meg    fusion    last    perspectives    statistical    size    image    complexity    noise    epilepsy    experts    strengthen    source    resolution    emergence    slab    representation    answers    power    engineering    imaging    fast    primary    econometrics    models    temporal    full    data    algorithms    coupling    ways    understand    acquisition    pioneering    neuroscience    electrophysiology    understanding    millisecond    software    signal    stationarity   

Project "SLAB" data sheet

The following table provides information about the project.


Organization address
postcode: 78153

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 1˙492˙252 €
 EC max contribution 1˙492˙252 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-STG
 Funding Scheme ERC-STG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
2    INSTITUT MINES-TELECOM FR (PALAISEAU) participant 112˙110.00


 Project objective

Understanding how the brain works in healthy and pathological conditions is considered as one of the challenges for the 21st century. After the first electroencephalography (EEG) measurements in 1929, the 90’s was the birth of modern functional brain imaging with the first functional MRI and full head magnetoencephalography (MEG) system. In the last twenty years, imaging has revolutionized clinical and cognitive neuroscience. After pioneering works in physics and engineering, the field of neuroscience has to face two major challenges. The size of the datasets keeps growing. The answers to neuroscience questions are limited by the complexity of the signals observed: non-stationarity, high noise levels, heterogeneity of sensors, lack of accurate models. SLAB will provide the next generation of models and algorithms for mining electrophysiology signals which offer unique ways to image the brain at a millisecond time scale. SLAB will develop dedicated machine learning and signal processing methods and favor the emergence of new challenges for these fields. SLAB focuses on five objectives: 1) source localization with M/EEG for brain imaging at high temporal resolution 2) representation learning to boost statistical power and reduce acquisition costs 3) fusion of heterogeneous sensors 4) modeling of non-stationary spectral interactions to identify functional coupling between neural ensembles 5) development of fast algorithms easy to use by non-experts. SLAB aims to strengthen mathematical and computational foundations of brain data analysis. The methods developed will have applications across fields (computational biology, astronomy, econometrics). Yet, the primary impact of SLAB will be on neuroscience. The tools and high quality open software produced in SLAB will facilitate the analysis of electrophysiology data, offering new perspectives to understand how the brain works at a mesoscale, and for clinical applications (epilepsy, autism, tremor, sleep disorders).


year authors and title journal last update
List of publications.
2018 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
MEG-BIDS, the brain imaging data structure extended to magnetoencephalography
published pages: 180110, ISSN: 2052-4463, DOI: 10.1038/sdata.2018.110
Scientific Data 5 2019-07-08
2017 Tom Dupré la Tour, Lucille Tallot, Laetitia Grabot, Valérie Doyère, Virginie van Wassenhove, Yves Grenier, Alexandre Gramfort
Non-linear auto-regressive models for cross-frequency coupling in neural time series
published pages: e1005893, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1005893
PLOS Computational Biology 13/12 2019-07-08
2017 Badeau, Roland; Bekhti, Yousra; Gramfort, Alexandre
Hyperparameter Estimation in Maximum a Posteriori Regression Using Group Sparsity with an Application to Brain Imaging
published pages: , ISSN: 2076-1465, DOI: 10.5281/zenodo.1159734
25th European Signal Processing Conference (EUSIPCO) 5 2019-07-08
2017 Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Salmon, Joseph
Gap Safe screening rules for sparsity enforcing penalties
published pages: , ISSN: 1532-4435, DOI:
Journal of Machine Learning Research 5 2019-07-08
2018 Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Celer: a Fast Solver for the Lasso with Dual Extrapolation
published pages: 3315--3324, ISSN: , DOI:
Proceedings of the 35th International Conference on Machine Learning 2019-07-08
2018 Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
published pages: 998--1007, ISSN: , DOI:
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics 2019-07-08
2017 Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
published pages: 1099--1108, ISSN: , DOI:
Advances in Neural Information Processing Systems 30 2019-07-08
2017 Mainak Jas, Denis A. Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort
Autoreject: Automated artifact rejection for MEG and EEG data
published pages: 417-429, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2017.06.030
NeuroImage 159 2019-07-08
2017 Jair Montoya-Martínez, Jean-François Cardoso, Alexandre Gramfort
Caveats with stochastic gradient and maximum likelihood based ICA for EEG
published pages: 279-289, ISSN: , DOI: 10.1007/978-3-319-53547-0_27
2016 Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
GAP Safe Screening Rules for Sparse-Group Lasso
published pages: 388--396, ISSN: , DOI:
Advances in Neural Information Processing Systems 29 2019-07-08
2018 Pierre Ablin, Jean-Francois Cardoso, Alexandre Gramfort
Faster Independent Component Analysis by Preconditioning With Hessian Approximations
published pages: 4040-4049, ISSN: 1053-587X, DOI: 10.1109/TSP.2018.2844203
IEEE Transactions on Signal Processing 66/15 2019-07-08
2018 Yousra Bekhti, Felix Lucka, Joseph Salmon, Alexandre Gramfort
A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
published pages: 85010, ISSN: 0266-5611, DOI: 10.1088/1361-6420/aac9b3
Inverse Problems 34/8 2019-07-08
2018 La Tour , Tom Dupré; Moreau , Thomas; Jas , Mainak; Gramfort , Alexandre
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems (NeurIPS), Dec 2018, Montréal, Canada 3 2019-05-27
2018 Mainak Jas, Eric Larson, Denis A. Engemann, Jaakko Leppäkangas, Samu Taulu, Matti Hämäläinen, Alexandre Gramfort
A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices
published pages: , ISSN: 1662-453X, DOI: 10.3389/fnins.2018.00530
Frontiers in Neuroscience 12 2019-04-18
2018 Niso, G.; Gorgolewski, K. J.; Bock, E.; Brooks, T. L.; Flandin, G.; Gramfort, A.; Henson, R. N.; Jas, M.; Litvak, V.; T Moreau, J.; Oostenveld, R.; Schoffelen, J-M; Tadel, F.; Wexler, J.; Baillet, S.
MEG-BIDS, the brain imaging data structure extended to magnetoencephalography
published pages: , ISSN: 2052-4463, DOI: 10.17863/CAM.30375
Scientific Data , 5 , Article 180110. (2018) 1 2019-04-18
2017 Mathurin Massias, Alexandre Gramfort, Joseph Salmon
From safe screening rules to working sets for faster Lasso-type solvers
published pages: , ISSN: , DOI:
Workshop NIPS OPTML 2019-04-18
2018 Massias , Mathurin; Fercoq , Olivier; Gramfort , Alexandre; Salmon , Joseph
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
published pages: , ISSN: , DOI:
21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Apr 2018, Lanzarote, Spain 5 2019-04-18

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