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Patient specific conductivity models: characterization of the skull bones.

Abstract : One of the major issues related to electroencephalography (EEG) is to localize where in the brain signals are generated, this is so called inverse problem of source localization. The quality of the source localization depends on the accuracy of the geometry and the electrical conductivity model used to solve the problem. Among the head tissues, the skull conductivity is the one that influences most the accuracy of the source localization,due to its low value. Moreover, the human skull is a bony tissue consisting of compact and spongy bone layers, whose thickness vary across the skull. As the skull tissue composition has strong inter-individual variability both in terms of geometry and of individual conductivity, conductivity estimation techniques are required in order to determine the unknown skull conductivity. The aim of this thesis is to reduce the uncertainty on the skull conductivity both in spherical and realistic head geometries in order to increase the quality of the inverse source localization problem. Therefore, conductivity estimation is first performed on a 3-layered spherical head model. Existence, uniqueness and stability of the conductivity in the intermediate skull layer are discussed, together with a constructive recovery scheme. Then a simulation study is performed comparing two realistic head models, a bulk model where the skull is modelled as a single compartment and a detailed one accounting for the compact and spongy bone layers, in order to determine the importance of the internal skull structure for conductivity estimation in EEG.
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Contributor : Christos Papageorgakis Connect in order to contact the contributor
Submitted on : Thursday, March 15, 2018 - 4:13:00 PM
Last modification on : Saturday, June 25, 2022 - 11:29:46 PM


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  • HAL Id : tel-01662075, version 2



Christos Papageorgakis. Patient specific conductivity models: characterization of the skull bones.. Modeling and Simulation. Université Côte d'Azur, 2017. English. ⟨NNT : ⟩. ⟨tel-01662075v2⟩



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