A Distributed Spatio-Temporal EEG/MEG Inverse Solver

Speaker:	Ms. Wanmei OU
		Computer Science and Artificial Intelligent Laboratory
		Massachusetts Institute of Technology
		USA

Title:		"A Distributed Spatio-Temporal EEG/MEG Inverse Solver"

Date:		Friday, 13 June 2008

Time:		2:00pm - 3:00pm

Venue:		Room 3416 (via lifts 17/18)
		HKUST

Abstract:

Localizing activated regions from electroencephalography (EEG) or
magnetoencephalography (MEG) data involves solving an ill-posed
electromagnetic inverse problem. We propose a novel $\ell_1$$\ell_2$-norm
inverse solver for estimating the sources of EEG/MEG signals. Developed
based on the standard $\ell_1$-norm inverse solvers, this sparse
distributed inverse solver integrates the $\ell_1$-norm spatial model with
a temporal model of the source signals in order to avoid unstable
activation patterns and "spiky" reconstructed signals often produced by
the currently used sparse solvers. The joint spatio-temporal model leads
to a cost function with an $\ell_1$$\ell_2$-norm regularizer whose
minimization can be reduced to a convex second-order cone programming
(SOCP) problem and efficiently solved using the interior-point method. The
efficient computation of the SOCP problem allows us to implement
permutation tests for estimating statistical significance of the inverse
solution. Validation with simulated and real MEG data shows that the
proposed solver yields source time course estimates qualitatively similar
to those obtained through dipole fitting, but without the need to specify
the number of dipole sources in advance. Furthermore, the
$\ell_1$$\ell_2$-norm solver achieves fewer false positives and a better
representation of the source locations than the conventional $\ell_2$
minimum-norm estimates.


*******************
Biography:

Details are available at http://people.csail.mit.edu/wanmei/