Medical & Biological Engineering & Computing

, Volume 46, Issue 2, pp 101–108

New method for analysing sensitivity distributions of electroencephalography measurements

  • Juho Väisänen
  • Outi Väisänen
  • Jaakko Malmivuo
  • Jari Hyttinen
Original Article

Abstract

In this paper, we introduce a new modelling related parameter called region of interest sensitivity ratio (ROISR), which describes how well the sensitivity of an electroencephalography (EEG) measurement is concentrated within the region of interest (ROI), i.e. how specific the measurement is to the sources in ROI. We demonstrate the use of the concept by analysing the sensitivity distributions of bipolar EEG measurement. We studied the effects of interelectrode distance of a bipolar EEG lead on the ROISR with cortical and non-cortical ROIs. The sensitivity distributions of EEG leads were calculated analytically by applying a three-layer spherical head model. We suggest that the developed parameter has correlation to the signal-to-noise ratio (SNR) of a measurement, and thus we studied the correlation between ROISR and SNR with 254-channel visual evoked potential (VEP) measurements of two testees. Theoretical simulations indicate that source orientation and location have major impact on the specificity and therefore they should be taken into account when the optimal bipolar electrode configuration is selected. The results also imply that the new ROISR method bears a strong correlation to the SNR of measurement and can thus be applied in the future studies to efficiently evaluate and optimize EEG measurement setups.

Keywords

Electroencephalography Modelling Region of interest sensitivity ratio Sensitivity distribution Signal-to-noise ratio 

References

  1. 1.
    Andrews TJ, Halpern SD, Purves D (1997) Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. J Neurosci 17(8):2859–2868Google Scholar
  2. 2.
    Celesia GG, Bodis-Wollner I, Chatrian GE, et al (1993) Recommended standards for electroretinograms and visual evoked potentials. Report of an IFCN committee. Electroencephalogr Clin Neurophysiol 87(6):421–436CrossRefGoogle Scholar
  3. 3.
    de Munck JC, Vijn PC, Lopes da Silva FH (1992) A random dipole model for spontaneous brain activity. IEEE Trans Biomed Eng 39(8):791–804CrossRefGoogle Scholar
  4. 4.
    Di Russo F, Martinez A, Sereno MI, et al (2002) Cortical sources of the early components of the visual evoked potential. Hum Brain Mapp 15(2):95–111CrossRefGoogle Scholar
  5. 5.
    Di Russo F, Pitzalis S, Aprile T, et al (2006) Spatiotemporal analysis of the cortical sources of the steady-state visual evoked potential. Hum Brain MappGoogle Scholar
  6. 6.
    He B, Yao D, Lian J (2002) High-resolution EEG: on the cortical equivalent dipole layer imaging. Clin Neurophysiol 113(2):227–235CrossRefGoogle Scholar
  7. 7.
    Hoekema R, Wieneke GH, Leijten FS, et al (2003) Measurement of the conductivity of skull, temporarily removed during epilepsy surgery. Brain Topogr 16(1):29–38CrossRefGoogle Scholar
  8. 8.
    Ikeda H, Nishijo H, Miyamoto K, et al (1998) Generators of visual evoked potentials investigated by dipole tracing in the human occipital cortex. Neuroscience 84(3):723–739CrossRefGoogle Scholar
  9. 9.
    Lai Y, van Drongelen W, Ding L, et al (2005) Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings. Clin Neurophysiol 116(2):456–465CrossRefGoogle Scholar
  10. 10.
    Lutkenhoner B (1998) Dipole source localization by means of maximum likelihood estimation I: theory and simulations. Electroencephalogr Clin Neurophysiol 106(4):314–321CrossRefGoogle Scholar
  11. 11.
    Lutkenhoner B (1998) Dipole source localization by means of maximum likelihood estimation: II. experimental evaluation. Electroencephalogr Clin Neurophysiol 106(4):322–329CrossRefGoogle Scholar
  12. 12.
    Malmivuo J, Plonsey R (1995) Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press, New YorkGoogle Scholar
  13. 13.
    Malmivuo J, Suihko V, Eskola H (1997) Sensitivity distributions of EEG and MEG measurements. IEEE Trans Biomed Eng 44(3):196–208CrossRefGoogle Scholar
  14. 14.
    Malmivuo JA, Suihko VE (2004) Effect of skull resistivity on the spatial resolutions of EEG and MEG. IEEE Trans Biomed Eng 51(7):1276–1280CrossRefGoogle Scholar
  15. 15.
    McFee R, Johnston FD (1953) Electrocardiographic leads I: introduction. Circulation 8(4):554–568Google Scholar
  16. 16.
    Neilson LA, Kovalyov M, Koles ZJ (2005) A computationally efficient method for accurately solving the EEG forward problem in a finely discretized head model. Clinical Neurophysiology 116(10):2302–2314CrossRefGoogle Scholar
  17. 17.
    Niedermeyer E, Lopes da Silva F (1993) Electroencephalography: basic principles, clinical applications, and related fields. Williams and Wilkins, BaltimoreGoogle Scholar
  18. 18.
    Nunez P (1981) Electric fields of the brain: the neurophysics of EEG. Oxford University Press, New YorkGoogle Scholar
  19. 19.
    Oostendorp TF, Delbeke J, Stegeman DF (2000) The conductivity of the human skull: results of in vivo and in vitro measurements. IEEE Trans Biomed Eng 47(11):1487–1492CrossRefGoogle Scholar
  20. 20.
    Raz J, Turetsky B, Fein G (1988) Confidence intervals for the signal-to-noise ratio when a signal embedded in noise is observed over repeated trials. IEEE Trans Biomed Eng 35(8):646–649CrossRefGoogle Scholar
  21. 21.
    Rush S, Driscoll DA (1969) EEG electrode sensitivity—an application of reciprocity. IEEE Trans Biomed Eng 16(1):15–22Google Scholar
  22. 22.
    Vanrumste B, Van Hoey G, Van de Walle R, et al (2001) The validation of the finite difference method and reciprocity for solving the inverse problem in EEG dipole source analysis. Brain Topogr 14(2):83–92CrossRefGoogle Scholar
  23. 23.
    Watson JDG (2000) The human visual system. In: Toga AW, Mazziotta JC (eds) Brain mapping: the systems. Academic Press, San Diego, pp 263–289Google Scholar
  24. 24.
    Väisänen J, Hyttinen J, Malmivuo J (2006) Finite difference and lead field methods in designing implantable ECG monitor. Med Biol Eng Comput 44(10):857–864CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  • Juho Väisänen
    • 1
  • Outi Väisänen
    • 1
  • Jaakko Malmivuo
    • 1
  • Jari Hyttinen
    • 1
  1. 1.Ragnar Granit InstituteTampere University of TechnologyTampereFinland

Personalised recommendations