Medical & Biological Engineering & Computing

, Volume 46, Issue 2, pp 101–108

New method for analysing sensitivity distributions of electroencephalography measurements

Authors

    • Ragnar Granit InstituteTampere University of Technology
  • Outi Väisänen
    • Ragnar Granit InstituteTampere University of Technology
  • Jaakko Malmivuo
    • Ragnar Granit InstituteTampere University of Technology
  • Jari Hyttinen
    • Ragnar Granit InstituteTampere University of Technology
Original Article

DOI: 10.1007/s11517-007-0303-x

Cite this article as:
Väisänen, J., Väisänen, O., Malmivuo, J. et al. Med Bio Eng Comput (2008) 46: 101. doi:10.1007/s11517-007-0303-x

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

ElectroencephalographyModellingRegion of interest sensitivity ratioSensitivity distributionSignal-to-noise ratio

Copyright information

© International Federation for Medical and Biological Engineering 2007