title: A time domain frequency-selective multivariate Granger causality approach.
abstract: The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.
authors.forename: Lutz
authors.initials: L
authors.lastname: Leistritz
authors.forename: Herbert
authors.initials: H
authors.lastname: Witte
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000166:cytology / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D012815:Signal Processing, Computer-Assisted