A nonlinear generalization of spectral granger causality IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214 :+91-8144199666 Email:ieeeprojectchennai@gmail.com http://www.projectsieee.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explaintion Teamviewer Suppport

1. A Nonlinear Generalization of Spectral
Granger Causality
Spectral measures of linear Granger causality have been widely applied to study the causal
connectivity between time series data in neuroscience, biology, and economics. Traditional
Granger causality measures are based on linear autoregressive with exogenous (ARX)
inputs models of time series data, which cannot truly reveal nonlinear effects in the data
especially in the frequency domain. In this study, it is shown that the classical Geweke's
spectral causality measure can be explicitly linked with the output spectra of corresponding
restricted and unrestricted time-domain models. The latter representation is then
generalized to nonlinear bivariate signals and for the first time nonlinear causality analysis
in the frequency domain. This is achieved by using the nonlinear ARX (NARX) modeling of
signals, and decomposition of the recently defined output frequency response function
which is related to the NARX model.