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Proceedings Paper

Regularized two-step brain activity reconstruction from spatiotemporal EEG data
Author(s): Teodor I. Alecu; Sviatoslav Voloshynovskiy; Thierry Pun
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Paper Abstract

We are aiming at using EEG source localization in the framework of a Brain Computer Interface project. We propose here a new reconstruction procedure, targeting source (or equivalently mental task) differentiation. EEG data can be thought of as a collection of time continuous streams from sparse locations. The measured electric potential on one electrode is the result of the superposition of synchronized synaptic activity from sources in all the brain volume. Consequently, the EEG inverse problem is a highly underdetermined (and ill-posed) problem. Moreover, each source contribution is linear with respect to its amplitude but non-linear with respect to its localization and orientation. In order to overcome these drawbacks we propose a novel two-step inversion procedure. The solution is based on a double scale division of the solution space. The first step uses a coarse discretization and has the sole purpose of globally identifying the active regions, via a sparse approximation algorithm. The second step is applied only on the retained regions and makes use of a fine discretization of the space, aiming at detailing the brain activity. The local configuration of sources is recovered using an iterative stochastic estimator with adaptive joint minimum energy and directional consistency constraints.

Paper Details

Date Published: 22 October 2004
PDF: 12 pages
Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); doi: 10.1117/12.556293
Show Author Affiliations
Teodor I. Alecu, Computer Vision Lab./Univ. de Geneve (Switzerland)
Sviatoslav Voloshynovskiy, Computer Vision Lab./Univ. de Geneve (Switzerland)
Thierry Pun, Computer Vision Lab./Univ. de Geneve (Switzerland)


Published in SPIE Proceedings Vol. 5562:
Image Reconstruction from Incomplete Data III
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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