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Proceedings Paper

Iterative Bayesian maximum entropy method for the EEG inverse problem
Author(s): Deepak Khosla; Manuel Don; Manbir Singh
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Paper Abstract

Electroencephalographic imaging is the estimation of 3D neuronal current sources on the cortical surface from the measured electroencephalogram (EEG). It is a highly under- determined inverse problem as there are many 'feasible' images which are consistent with the scalp potentials. Previous approaches to this problem have primarily concentrated on the weighted minimum norm inverse methods. While these methods ensure a unique solution, they often produce overly smoothed solutions and are sensitive to noise in the data. Our group previously proposed a maximum entropy approach to obtain better solutions to this problem. We incorporated a noise rejection term into the maximum entropy method, thereby making it analogous to a Bayesian maximum a posteriori formulation. Additional information from other modalities, like functional magnetic resonance imaging, could be incorporated into this method in the form of a prior bias function to improve solutions. While this approach gave better results than the minimum norm methods, the solutions were still somewhat smooth and blurry. In this work, we developed and tested an iterative version of the maximum entropy method to obtain more localized solutions. This method starts with a distributed estimate computed by the maximum entropy method. It then recursively performs maximum entropy estimations producing a progressively more focal current distribution. We present the method and test its validity through computer simulations for both noiseless and noisy data. The results suggest that the proposed method is a powerful algorithm with good utility for EEG imaging.

Paper Details

Date Published: 9 May 1997
PDF: 12 pages
Proc. SPIE 3033, Medical Imaging 1997: Physiology and Function from Multidimensional Images, (9 May 1997); doi: 10.1117/12.274040
Show Author Affiliations
Deepak Khosla, House Ear Institute and Univ. of Southern California (United States)
Manuel Don, House Ear Institute (United States)
Manbir Singh, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 3033:
Medical Imaging 1997: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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