Explore the words cloud of the SLAB project. It provides you a very rough idea of what is the project "SLAB" about.
The following table provides information about the project.
Coordinator |
INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Organization address contact info |
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.
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1 | INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE | FR (LE CHESNAY CEDEX) | coordinator | 1˙380˙141.00 |
2 | INSTITUT MINES-TELECOM | FR (PALAISEAU) | participant | 112˙110.00 |
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 |
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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-MartÃnez, 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 |
2019-07-08 | |
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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