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EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model

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Rough Sets and Knowledge Technology (RSKT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

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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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References

  1. Wen, P., Li, Y.: EEG human head modelling based on heterogeneous tissue conductivity. Australias. Phy. & Eng. S. 29, 235–240 (2006)

    Article  Google Scholar 

  2. Baysal, U., Haueisen, J.: Use of priori information in estimation tissue resistivities-application to human data in vivo. Physiol. Meas. 25, 737–748 (2004)

    Article  Google Scholar 

  3. Bashar, B., Li, Y., Wen, P.: Influence of white matter inhomogeneous anisotropy on EEG forward computing. Austras. Phy. & Eng. S. 31, 122–130 (2008)

    Article  Google Scholar 

  4. Bashar, B., Li, Y., Wen, P.: Tissue Conductivity Anisotropy Inhomogeneity Study in EEG Head Modelling. In: International Conference on Bioinformatics and Computational Biology (BIOCOMP 2008), USA (2008)

    Google Scholar 

  5. Hallez, H., Vanrumste, B., Hese, P.V., D’Asseler, Y., Lemahieu, I., de Walle, R.V.: A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization. Phy. Med. Bio. 50, 3787–3806 (2005)

    Article  Google Scholar 

  6. Wolters, C.H.: Influence of Tissue Conductivity Inhomogeneity and Anisotropy on EEG/MEG based Source Localization in the Human Brain. PhD dissertation, University of Leipzig, France (2003)

    Google Scholar 

  7. Chen, F., Hallez, H., van Hese, P., Assler, Y.D., Lemahieu, I.: Dipole Estimation Errors Due to Skull Conductivity Perturbations: Simulation Study in Spherical Head Models. In: IEEE Proc. of Noninvasive Func. Source Imaging of the Brain and Heart and the Int. Conf. Func. Biomed. Imaging (NFSI & ICFBI), pp. 86–89 (2007).

    Google Scholar 

  8. Sadleir, R.J., Argibay, A.: Modeling Skull Electric Properties. Annals of Biomed. Eng. 35, 1699–1712 (2007)

    Article  Google Scholar 

  9. Rush, S., Driscoll, D.: Current distribution in the brain from surface electrodes. Anesth. Analg. 47, 717–723 (1968)

    Article  Google Scholar 

  10. Oostendorp, T.F., Delbeke, J., Stegeman, D.F.: The Conductivity of the Human Skull: results of In Vivo and In Vitro Measurements. IEEE Trans. on Biomed. Eng. 47, 1487–1492 (2000)

    Article  Google Scholar 

  11. Lai, Y., Drongelen, W.V., Ding, L., Hecox, K.E., Towle, V.L., Frim, D.M., He, B.: Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra-and intra-cranial electrical potential recordings. Clinical Neurophysiology 116, 456–465 (2005)

    Article  Google Scholar 

  12. Goncalves, S., de Munck, J.C., Verbunt, J.P.A., Bijma, F., Heethar, R.M., Lopes de Silva, F.: In vivo measurement of the brain and skull resistivities using an EIT-based methods and realistic models of the head. IEEE Trans. on Biomed. Eng. 50, 754–767 (2003)

    Article  Google Scholar 

  13. Goncalves, S., de Munck, J.C., Verbunt, J.P.A., Bijma, F., Heethar, R.M., de Silva, L.F.: In vivo measurement of the brain and skull resistivities using an EIT-based methods and the combined analysis of SEF/SEP data. IEEE Trans. on Biomed. Engr. 50, 1124–1128 (2003)

    Article  Google Scholar 

  14. Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic Resonance Image Tissue Classification Using a Partial Volume Model. NeuroImage 13, 856–876 (2001)

    Article  Google Scholar 

  15. Buchner, H., Knoll, G., Fuchs, M., Reinacker, A., Beckmann, R., Wagner, M., Silny, J., Pesch, J.: Inverse localization of electric dipole current sources in finite elements models of the human head. Electroencephalography and clinical Neurophysiology 102, 267–278 (1997)

    Article  Google Scholar 

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Bashar, M.R., Li, Y., Wen, P. (2009). EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_26

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  • DOI: https://doi.org/10.1007/978-3-642-02962-2_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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