Estimating Electroencephalograph Network Parameters Using Mutual Information

Brain Connectivity
Ranjit Arulnayagam Thuraisingham

Abstract

Statistical parameters that measure strength, integration, and segregation of a multichannel electroencephalograph (EEG) network are evaluated using a similarity measure based on mutual information (MI) between the measured channel data. Compared with the unsigned linear correlation coefficient, MI is more robust to volume conduction and is applicable to nonlinear data. The statistical parameters estimated are node strength, average path length, and clustering coefficient. These parameters provide valuable insights into the brain network of the subject. MI is evaluated using a recently developed procedure based on the Gaussian copula. It is a computationally efficient procedure since estimation of MI is carried out analytically. This procedure is illustrated here for a 30-channel random noise and EEG network. The results are compared with those obtained using the linear correlation coefficient. The results show improvements by using MI to estimate the network properties.

Citations

Nov 3, 2001·Physical Review Letters·V Latora, M Marchiori
May 2, 2003·BMC Bioinformatics·Jacques Rougemont, Pascal Hingamp
May 2, 2006·Statistical Applications in Genetics and Molecular Biology·Bin Zhang, Steve Horvath
Oct 7, 2006·NeuroImage·T MontezCornelis J Stam
Mar 20, 2012·Clinical EEG and Neuroscience·Mehran AhmadlouAmir Adeli
Nov 27, 2014·Journal of Neural Transmission·R A Thuraisingham

Related Concepts

Electroencephalography
Log-Linear Models
Copula
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Neural Pathways
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Copula (Body Structure)

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