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Section: New Results
Forward and Inverse Problems in MEEG
FindSource3D - Source Localization Using Rational Approximation on Plane Sections
Participants : Todor Jordanov [BESA GmbH, Germany] , Jean-Paul Marmorat [École des Mines ParisTech, Sophia Antipolis] , Maureen Clerc, Juliette Leblond, Andre Waelkens [BESA GmbH, Germany] , Théodore Papadopoulo.
A new method for EEG source localization based on rational approximation techniques in the complex plane was suggested. The method is used in the context of a nested sphere head model, in combination with a cortical mapping procedure [51] . This method was shown to perform perfectly for numerical simulations without noise but its performance with respect to different signal-to-noise ratios (SNRs), to different number of sources and to real EEG data was not investigated until now. The method, formally called FindSource3D (FS3D), is evaluated with data simulations and a real EEG data set.
This work has been published in [40] .
Diffusion Magnetic Resonance information as a regularization term for MEG/EEG inverse problem
Participants : Brahim Belaoucha, Anne-Charlotte Philippe, Maureen Clerc, Théodore Papadopoulo.
Several regularization terms are used to constrain the Magnetoencephalography (MEG) and the Electroencephalography (EEG) inverse problem. It has been shown that the brain can be divided into several regions with functional homogeneity inside each one of them. To locate these regions, we use the structural information coming from the diffusion Magnetic Resonance (dMRI) and more specifically, the anatomical connectivity of the distributed sources computed from dMRI. To investigate the importance of the dMRI in the source reconstruction, this work compares the solutions based on dMRI-based parcellation to random parcellation.
This work has been published in [37] .
Dictionary learning for multitrial datasets
Participants : Maureen Clerc, Sebastian Hitziger, Théodore Papadopoulo.
Following the path opened with the Consensus matching Pursuit method (CMP) [48] , we continue our endeavour to avoid signal averaging using directly the raw signal with the assumption that events of interest are those that repeat in each trial. Towards such a goal, and to improve the simple dictionary used in CMP, we have adapted dictionary learning methods to multitrial bio-electric signals, by explicitly implementing jitter invariance [62] . This allows for a much more detailed data-driven description of events. For example, using local field potential signals of chemically induced spikes (in a rat model), we have been able to distinguish several spike shapes which show some coherence in time. The method has been recently extended to detect spike events in continuous signals (i.e. not organized in epochs). While it requires a good signal to noise ratio, the method is very general and has also been used for various other signal types (see section 6.7 ).
This work has been published in [39] .