Abstract
Identifying directed information flow or Granger causality between multivariate time series is important for a wide range of applications in science and engineering. However, traditional data-driven approaches are insufficient to assess Granger causality between multimodal data with distinct temporal resolution. Here we propose a new analysis approach to address this challenge and present quantitative characterizations and statistical assessment on frequency-dependent directed information flow ("generalized spectral causality"). We validate our approach with intensive computer simulations on bivariate and trivariate systems for various conditions.