Timezone: »
Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control. While alpha waves (8--12\,Hz) are known to closely resemble short sinusoids, and thus are revealed by Fourier analysis or wavelet transforms, there is an evolving debate that electromagnetic neural signals are composed of more complex waveforms that cannot be analyzed by linear filters and traditional signal representations. In this paper, we propose to learn dedicated representations of such recordings using a multivariate convolutional sparse coding (CSC) algorithm. Applied to electroencephalography (EEG) or magnetoencephalography (MEG) data, this method is able to learn not only prototypical temporal waveforms, but also associated spatial patterns so their origin can be localized in the brain. Our algorithm is based on alternated minimization and a greedy coordinate descent solver that leads to state-of-the-art running time on long time series. To demonstrate the implications of this method, we apply it to MEG data and show that it is able to recover biological artifacts. More remarkably, our approach also reveals the presence of non-sinusoidal mu-shaped patterns, along with their topographic maps related to the somatosensory cortex.
Author Information
Tom Dupré la Tour (Télécom ParisTech)
Thomas Moreau (Inria)
Mainak Jas (Télécom ParisTech)
Alexandre Gramfort (INRIA, Université Paris-Saclay)
More from the Same Authors
-
2022 Poster: Benchopt: Reproducible, efficient and collaborative optimization benchmarks »
Thomas Moreau · Mathurin Massias · Alexandre Gramfort · Pierre Ablin · Pierre-Antoine Bannier · Benjamin Charlier · Mathieu Dagréou · Tom Dupre la Tour · Ghislain DURIF · Cassio F. Dantas · Quentin Klopfenstein · Johan Larsson · En Lai · Tanguy Lefort · Benoît Malézieux · Badr MOUFAD · Binh T. Nguyen · Alain Rakotomamonjy · Zaccharie Ramzi · Joseph Salmon · Samuel Vaiter -
2022 Poster: Deep invariant networks with differentiable augmentation layers »
Cédric ROMMEL · Thomas Moreau · Alexandre Gramfort -
2022 Poster: A framework for bilevel optimization that enables stochastic and global variance reduction algorithms »
Mathieu Dagréou · Pierre Ablin · Samuel Vaiter · Thomas Moreau -
2020 Poster: Learning to solve TV regularised problems with unrolled algorithms »
Hamza Cherkaoui · Jeremias Sulam · Thomas Moreau -
2020 Poster: Modeling Shared responses in Neuroimaging Studies through MultiView ICA »
Hugo Richard · Luigi Gresele · Aapo Hyvarinen · Bertrand Thirion · Alexandre Gramfort · Pierre Ablin -
2020 Spotlight: Modeling Shared responses in Neuroimaging Studies through MultiView ICA »
Hugo Richard · Luigi Gresele · Aapo Hyvarinen · Bertrand Thirion · Alexandre Gramfort · Pierre Ablin -
2020 Poster: NeuMiss networks: differentiable programming for supervised learning with missing values. »
Marine Le Morvan · Julie Josse · Thomas Moreau · Erwan Scornet · Gael Varoquaux -
2020 Oral: NeuMiss networks: differentiable programming for supervised learning with missing values. »
Marine Le Morvan · Julie Josse · Thomas Moreau · Erwan Scornet · Gael Varoquaux -
2020 Poster: Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso »
Jerome-Alexis Chevalier · Joseph Salmon · Alexandre Gramfort · Bertrand Thirion -
2019 Poster: Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso »
Quentin Bertrand · Mathurin Massias · Alexandre Gramfort · Joseph Salmon -
2019 Poster: Learning step sizes for unfolded sparse coding »
Pierre Ablin · Thomas Moreau · Mathurin Massias · Alexandre Gramfort -
2019 Poster: Manifold-regression to predict from MEG/EEG brain signals without source modeling »
David Sabbagh · Pierre Ablin · Gael Varoquaux · Alexandre Gramfort · Denis A. Engemann -
2017 Poster: Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding »
Mainak Jas · Tom Dupré la Tour · Umut Simsekli · Alexandre Gramfort -
2016 Poster: GAP Safe Screening Rules for Sparse-Group Lasso »
Eugene Ndiaye · Olivier Fercoq · Alexandre Gramfort · Joseph Salmon -
2015 Poster: GAP Safe screening rules for sparse multi-task and multi-class models »
Eugene Ndiaye · Olivier Fercoq · Alexandre Gramfort · Joseph Salmon