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    <meta name="description" content="Application Domains - Inverse source problems in EEG"/>
    <meta name="dc.title" content="Application Domains - Inverse source problems in EEG"/>
    <meta name="dc.creator" content="Laurent Baratchart"/>
    <meta name="dc.creator" content="Juliette Leblond"/>
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    <meta name="dc.date" content="(SCHEME=ISO8601) 2014-01"/>
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	    2014</a> | <a href="http://www.inria.fr/en/teams/apics">Presentation of the Project-Team APICS</a> | <a href="http://team.inria.fr/apics/">APICS Web Site
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        <h2>Section: 
      Application Domains</h2>
        <h3 class="titre3">Inverse source problems in EEG</h3>
        <p class="participants"><span class="part">Participants</span> :
	Laurent Baratchart, Juliette Leblond.</p>
        <p>This work is performed in collaboration with Maureen Clerc and Théo Papadopoulo from the Athena Project-Team, and Jean-Paul Marmorat (Centre de mathématiques appliquées - CMA, École des Mines de Paris).</p>
        <p>Solving overdetermined Cauchy problems for the Laplace equation on a
spherical layer (in 3-D) in order to extrapolate
incomplete data (see Section <a title="Range of inverse problems" href="./uid11.html#uid12">
	3.2.1</a> ) is
a necessary
ingredient of the team's approach to inverse source problems, in particular
for applications to EEG. Indeed, the latter involves propagating the
initial conditions through several layers of different conductivities,
from the boundary shell
down to the center of the domain where the
singularities (<i>i.e.</i> the sources) lie.
Once propagated
to the innermost sphere, it turns out that traces of the
boundary data on 2-D cross sections coincide
with analytic functions with branched singularities
in the slicing plane
<a href="./bibliography.html#apics-2014-bid23">[3]</a> . The singularities are
related to the actual location of the sources, namely their moduli
reach in turn a
maximum when the plane contains one of the sources. Hence we are
back to the 2-D framework of Section <a title="Approximation" href="./uid17.html#uid22">
	3.3.3</a> ,
and recovering these singularities
can be performed <i>via</i> best rational approximation.
The goal is to produce a fast and sufficiently accurate
initial guess on the number
and location of the sources in order to run heavier
descent algorithms on the direct problem, which are more precise but
computationally costly and often
fail to converge if not properly initialized.</p>
        <p>Numerical experiments give
very good results on simulated data and we are now engaged in the process
of handling
real experimental data (see
Sections <a title="&#10;        FindSources3D&#10;      " href="./uid52.html">
	5.6</a>  and <a title="Source recovery problems" href="./uid58.html">
	6.1</a> ),
in collaboration with the Athena team at Inria Sophia Antipolis,
neuroscience teams in partner-hospitals (la Timone, Marseille),
and the BESA company (Munich).
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