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Analysis of parametric estimation of head tissue conductivities using Electrical Impedance Tomography

Fernández-Corazza, Mariano, Beltrachini, Leandro, von Ellenrieder, Nicolas and Muravchik, Carlos H. 2013. Analysis of parametric estimation of head tissue conductivities using Electrical Impedance Tomography. Biomedical Signal Processing and Control 8 (6) , pp. 830-837. 10.1016/j.bspc.2013.08.003

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Abstract

We study the theoretical performance of using Electrical Impedance Tomography (EIT) to measure the conductivity of the main tissues of the head. The governing equations are solved using the Finite Element Method for realistically shaped head models with isotropic and anisotropic electrical conductivities. We focus on the Electroencephalography (EEG) signal frequency range since EEG source localization is the assumed application. We obtain the Cramér-Rao Lower Bound (CRLB) to find the minimum conductivity estimation error expected with EIT measurements. The more convenient electrode pairs selected for current injection from a typical EEG array are determined from the CRLB. Moreover, using simulated data, the Maximum Likelihood Estimator of the conductivity parameters is shown to be close to the CRLB for a relatively low number of measurements. The results support the idea of using EIT as a low-cost and practical tool for individually measure the conductivity of the head tissues, and to use them when solving the EEG source localization. Even when the conductivity of the soft tissues of the head is available from Diffusion Tensor Imaging, EIT can complement the electrical model with the estimation of the skull and scalp conductivities.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Cardiff University Brain Research Imaging Centre (CUBRIC)
Psychology
Subjects: Q Science > QC Physics
Uncontrolled Keywords: Electrical Impedance Tomography; Cramér-Rao Bound; Maximum Likelihood Estimator; Electrical conductivity estimation; Skull anisotropy; Finite Element Method
Publisher: Elsevier
ISSN: 1746-8094
Date of Acceptance: 5 August 2013
Last Modified: 08 May 2019 13:48
URI: http://orca.cf.ac.uk/id/eprint/101078

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