65 Scopus citations

Abstract

Techniques based on electroencephalography (EEG) measure the electric potentials on the scalp and process them to infer the location, distribution, and intensity of underlying neural activity. Accuracy in estimating these parameters is highly sensitive to uncertainty in the conductivities of the head tissues. Furthermore, dissimilarities among individuals are ignored when standarized values are used. In this paper, we apply the maximum-likelihood and maximum a posteriori (MAP) techniques to simultaneously estimate the layer conductivity ratios and source signal using EEG data. We use the classical 4-sphere model to approximate the head geometry, and assume a known dipole source position. The accuracy of our estimates is evaluated by comparing their standard deviations with the Cramér-Rao bound (CRB). The applicability of these techniques is illustrated with numerical examples on simulated EEG data. Our results show that the estimates have low bias and attain the CRB for sufficiently large number of experiments. We also present numerical examples evaluating the sensitivity to imprecise assumptions on the source position and skull thickness. Finally, we propose extensions to the case of unknown source position and present examples for real data.

Original languageEnglish
Pages (from-to)2113-2122
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume51
Issue number12
DOIs
StatePublished - Dec 2004

Keywords

  • Brain conductivities
  • Cramér-Rao bound
  • Electroencephalography
  • Maximum-likelihood estimation
  • Parameter estimation
  • Sensor array processing

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