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.
|Number of pages||9|
|Journal||IEEE Transactions on Biomedical Engineering|
|Publication status||Published - 27 Jan 2014|
- Electroencephalogram (EEG)
- frequency response function (FRF)
- Granger causality
- nonlinear systems
- spectral analysis
ASJC Scopus subject areas
- Biomedical Engineering
FingerprintDive into the research topics of 'A nonlinear generalization of spectral granger causality'. Together they form a unique fingerprint.
- Research Centre for Computational Science and Mathematical Modelling - Associate Professor (Research)