EEG analysis on skull conductivity perturbations using realistic head model

Bashar, Md. Rezaul and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 and Wen, Peng ORCID: https://orcid.org/0000-0003-0939-9145 (2009) EEG analysis on skull conductivity perturbations using realistic head model. In: 4th International Conference on Rough Sets and Knowledge Technology (RSKT 2009), 14-16 Jul 2009, Gold Coast, Australia.

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Abstract

Measurement of electroencephalogram (EEG) requires accurate estimation of tissue conductivity. Among the head tissues, skull compartment has less conductivity due to compacta and spongiosa, which impacts on EEG measurement. Therefore, skull conductivity plays a vital role in head modeling, forward computation and source localization. In this study, we have investigated the effects of scalp potentials due to skull conductivity perturbations in realistic head models using different skull to brain and/or scalp conductivity ratio (σ ratio). Several studies used this σ ratio as 1/80, however, other studies found the values of σ ratio between 1/20 and 1/72. Each head model constructed from the values of different σ ratio ranging from 1/20 to 1/72 is compared to the head model constructed from σ ratio =1/80. The obtained results demonstrated that the skull conductivity perturbations have effects on EEG and the head model constructed from less σ ratio generates larger errors due to higher potential differences.

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Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	File reproduced in accordance with the copyright policy of the publisher/author.
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited:	24 Jan 2010 02:42
Last Modified:	22 Jun 2018 01:26
Uncontrolled Keywords:	EEG; head modelling; anisotropic conductivity; MRI and FEM; electroencephalography
Fields of Research (2008):	10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors) 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified 11 Medical and Health Sciences > 1103 Clinical Sciences > 110320 Radiology and Organ Imaging
Fields of Research (2020):	32 BIOMEDICAL AND CLINICAL SCIENCES > 3206 Medical biotechnology > 320602 Medical biotechnology diagnostics (incl. biosensors) 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320222 Radiology and organ imaging
Socio-Economic Objectives (2008):	E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Identification Number or DOI:	https://doi.org/10.1007/978-3-642-02962-2_26
URI:	http://eprints.usq.edu.au/id/eprint/6647

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