GRAMFORT , Alexandre

The overall aim of this thesis is the development of novel electroencephalography (EEG) and magnetoencephalography (MEG) analysis methods to provide new insights to the functioning of the human brain. MEG and EEG are non-invasive techniques that measure outside of the head the electric potentials and the magnetic fields induced by the neuronal...

On s'intéresse aux problèmes inverses sous déterminés, et plus particulièrement à la localisation de sources en magnéto et électro- encéphalographie (M/EEG). Dans ces problèmes, bien que l'on ait à disposition un modèle physique de la diffusion (ou du "mélange") des sources, le caractère très sous-déterminé des problèmes rend l'inversion très...

The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient $\ell_2$ norm. However such methods are known to smear the estimated distribution of cortical currents. In order to provide sparser solutions, other norms than $\ell_2$ have...

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...

Extracting information from multitrial magnetoencephalography or electroencephalography (EEG) recordings is challenging because of the very low SNR, and because of the inherent variability of brain responses. The problem of low SNR is commonly tackled by averaging multiple repetitions of the recordings, also called trials, but the variability...

OpenMEEG implements boundary element solutions for simulating electromagnetic fields in the quasistaticregime. Originally designed for the forward EEG and MEG problems (MEEG collectively), it has also beenapplied to compute forward solutions for ECoG, for implanted EEG, for cochlear implant stimulation, for tDCSand other electrostimulation...

International audience

Functional brain imaging with M/EEG (Magneto/Electro-EncephaloGraphy) requires first to compute the forward problem whose solution is called lead-field matrix. The lead-field matrix is obtained by concatenating the forward fields computed for thousands of sources characterized by their positions, orientations and strengths. A line of this...

In EEG or MEG, a lead field is the linear operator which associates unitary dipolar sources to the resulting set of sensor measurements. In practise, the source space often includes over 10 000 dipoles, which sometimes causes memory problems. The adjoint approach considers the forward problem from the viewpoint of sensors instead of sources:...

This work proposes to use magnetoencephalography (MEG) and electroencephalography (EEG) source imaging to provide cinematic representations of the temporal dynamics of cortical activations. Cortical activations maps, seen as images of the active brain, are scalar maps de_ned at the vertices of a triangulated cortical surface. They can be...

Electrophysiological modeling of Magneto- and Electro-encephalography (MEG and EEG) rely on accurate forward solvers that relate source activities to sensor measurements. In comparing a Boundary Element (BEM) and a Finite Element Method (FEM) for forward electroencephalography, in our early numerical experiments, we found the FEM to have a...

Neuroimaging with magneto and electroencephalography (M/EEG) requires to compute the forward problem. It consists in predicting what is measured by MEG or EEG sensors due to a configuration of current generators within the head. When considering realistic head models, the equations derived from Maxwell equations can only be solved numerically....

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...

Neuroimaging with magneto and electroencephalography (M/EEG) requires to compute the forward problem. It consists in predicting what is measured by MEG or EEG sensors due to a configuration of current generators within the head. When considering realistic head models, the equations derived from Maxwell equations can only be solved numerically....

OpenMEEG implements boundary element solutions for simulating electromagnetic fields in the quasistatic regime. Originally designed for the forward EEG and MEG problems (MEEG collectively), it has also been applied to compute forward solutions for ECoG, for implanted EEG, for cochlear implant stimulation, for tDCS and other electrostimulation...

To recover the sources giving rise to electro- and magnetoencephalography in individual measurements, realistic physiological modeling is required, and accurate numerical solutions must be computed. We present OpenMEEG, which solves the electromagnetic forward problem in the quasistatic regime, for head models with piecewise constant...

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This work aims at sketching over time the activity of the brain, providing dynamic maps of cortical activations triggered by a stimulus. Raw activations are computed from MEG or EEG using a distributed source model with equivalent current dipoles (ECD) lying on the folded cortical surface. Exploiting the natural graph structure of the cortical...

For composite nonsmooth optimization problems, which are "regular enough", proximal gradient descent achieves model identification after a finite number of iterations. For instance, for the Lasso, this implies that the iterates of proximal gradient descent identify the non-zeros coefficients after a finite number of steps. The identification...

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...

Although the spatial organization of visual areas can be revealed by functional Magnetic Resonance Imaging (fMRI), the synoptic, non-invasive access to the temporal characteristics of the information flow amongst distributed visual processes remains a technical and methodological challenge. Using frequency-encoded steady-state visual...

The simultaneous analysis of multiple recordings of neuronal electromagnetic activity is an important task requiring sophisticated and extremely noise robust techniques. A general goal is to find a representation of the similarities (e.g. repeating waveforms) as well as the differences (e.g. varying shape, latency, phase, or amplitude of...

This work aims at establishing a relationship between neurophysiological and hemodynamic activity in an animal model of epilepsy. For the analysis, we propose a novel algorithm that is suited to learn meaningful representations of the multimodal datasets. As a result, we are able to learn a hemodynamic response and discover spike...