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Interlaced resolution scheme for the simultaneous analysis of brain electric activity and conductivity with combined EEG/MEG diagnostics

Interlaced resolution scheme for the simultaneous analysis of brain electric activity and... Purpose – The purpose of this paper is to evaluate the performances of a resolution scheme able to follow the dynamics of brain tissue properties in combined ElectroEncefaloGraphic (EEG) – MagnetoEncefaloGraphic (MEG) techniques for the brain analysis, minimizing the computation burden. Design/methodology/approach – The estimation process in combined EEG‐MEG is performed by a Moore‐Penrose pseudo‐inverse computation. This is affected by the uncertain knowledge of the living tissues' electric properties. In principle, it is possible to estimate those properties from the EEG‐MEG signals. The estimation process becomes in this case non‐linear. A resolution scheme is proposed, based on the exploitation of the different dynamics characterizing sources and tissues properties. Findings – The proposed resolution scheme provides a reasonable estimate of the sources for a computationally affordable frequency of non‐liner estimations. Research limitations/implications – The proposed approach has not been tested yet on experimental data, and as such, its sensitivity to environmental uncertainty is not known yet. Practical implications – The proposed strategy can be easily implemented to perform realistic measurement processing. Originality/value – The paper presents a novel strategy to estimate tissues properties and EEG‐MEG signal sources based on the exploitation of their different dynamics, possibly taking advantages from an impedance tomography preliminary analysis for the tissue properties dynamics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

Interlaced resolution scheme for the simultaneous analysis of brain electric activity and conductivity with combined EEG/MEG diagnostics

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Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
0332-1649
DOI
10.1108/03321641011078607
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to evaluate the performances of a resolution scheme able to follow the dynamics of brain tissue properties in combined ElectroEncefaloGraphic (EEG) – MagnetoEncefaloGraphic (MEG) techniques for the brain analysis, minimizing the computation burden. Design/methodology/approach – The estimation process in combined EEG‐MEG is performed by a Moore‐Penrose pseudo‐inverse computation. This is affected by the uncertain knowledge of the living tissues' electric properties. In principle, it is possible to estimate those properties from the EEG‐MEG signals. The estimation process becomes in this case non‐linear. A resolution scheme is proposed, based on the exploitation of the different dynamics characterizing sources and tissues properties. Findings – The proposed resolution scheme provides a reasonable estimate of the sources for a computationally affordable frequency of non‐liner estimations. Research limitations/implications – The proposed approach has not been tested yet on experimental data, and as such, its sensitivity to environmental uncertainty is not known yet. Practical implications – The proposed strategy can be easily implemented to perform realistic measurement processing. Originality/value – The paper presents a novel strategy to estimate tissues properties and EEG‐MEG signal sources based on the exploitation of their different dynamics, possibly taking advantages from an impedance tomography preliminary analysis for the tissue properties dynamics.

Journal

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic EngineeringEmerald Publishing

Published: Nov 16, 2010

Keywords: Brain; Electrical measurement; Magnetic measurement; Inverse problems

References

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