Title :
Quantifying EEG synchrony using copulas
Author :
Iyengar, Satish G. ; Dauwels, Justin ; Varshney, Pramod K. ; Cichocki, Andrzej
Author_Institution :
Dept. of EECS, Syracuse Univ., Syracuse, NY, USA
Abstract :
In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimer´s disease. Results on real data are presented.
Keywords :
diseases; electroencephalography; medical signal processing; neurophysiology; Alzheimer disease; EEG synchrony; copulas; electroencephalographic signals; nonGaussian statistics; nonlinear dependencies; Alzheimer´s disease; Brain modeling; Distribution functions; Electroencephalography; Epilepsy; Medical diagnostic imaging; Mutual information; Phase detection; Scalp; Statistics; Copula theory; EEG; Kullback-Leibler divergence; Statistical dependence;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495664
