EEG Source Localization Sensitivity Due to Brain Lesions Modeling Errors

Abstract

For accurate electroencephalogram-based (EEG) localization of neural sources correct modeling of brain lesions geometry and tissue conductivity is required. Lesion properties are derived from anatomical images like CT or MRI. According to imaging modality, lesion can appear of different size and shape. Conductivity parameters are taken from standard references, despite the large variability in the available data. The uncertainties in lesion conductivity assignment (LCA) and in determining exact lesion geometry affect source localization accuracy. The aim of this paper is to quantify the combined effect of these uncertainties on EEG dipole source localization accuracy. The study was conducted using an eccentric-spheres model of the head in which a modifiable eccentric bubble approximated various brain lesions. In 32 simulated pathological conditions the inverse dipole fitting procedures was carried out assuming an incorrect (under/overestimate) lesion dimension and conductivity. Errors in lesion modeling led to markedly wrong source reconstruction even for small differences between the actual lesion and its model. Localization errors up to 15.4 mm demonstrate the requirement of an accurate parametric setting of the model to achieve localization accuracy within few millimeters.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412046

Entities

People

  • F. Vatta
  • P. Bruno
  • P. Inchingolo

Organizations

  • University of Trieste

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Conductivity
  • Data Analysis
  • Electrical Conductivity
  • Electrical Properties
  • Electrodes
  • Electroencephalography
  • Errors
  • Imaging Techniques
  • Inverse Problems
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Sensitivity
  • Soft Tissues
  • Tissues
  • Uncertainty
  • X-Ray Computed Tomography

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computational Modeling and Simulation
  • Medical Imaging.