A0999
Title: Spectral non-linear Granger causality for multivariate time series
Authors: Hernando Ombao - KAUST (Saudi Arabia) [presenting]
Abstract: One of the key goals in analyzing multivariate time series is to characterize and estimate the cross-dependence structure among the components. Traditional approaches (e.g., coherence and correlation) capture only linear dependence. This serious limitation could lead to false conclusions under non-linearity. Keeping this as motivation, we propose a procedure for identifying non-linear and frequency-band-specific Granger causality (Spec NLGC) connections. The advantages of the Spec NLGC approach over traditionally used VAR-based models will be demonstrated using simulations and in the analysis of epileptic seizure EEG data. It was able to uncover non-linear dynamics and yielded novel and insightful findings. The time-evolving Spec NLGC connections give more meaningful insights regarding the frequency-specific connectivity changes at the onset of epileptic seizures as compared to VAR-based PDC connections. These confirm the viability of the proposed algorithm as a good connectivity exploration tool.
