  This paper presents a general framework for modeling dependence in
multivariate time series. Its fundamental approach relies on decomposing each
signal in a system into various frequency components and then studying the
dependence properties through these oscillatory activities.The unifying theme
across the paper is to explore the strength of dependence and possible lead-lag
dynamics through filtering. The proposed framework is capable of representing
both linear and non-linear dependencies that could occur instantaneously or
after some delay(lagged dependence). Examples for studying dependence between
oscillations are illustrated through multichannel electroencephalograms. These
examples emphasized that some of the most prominent frequency domain measures
such as coherence, partial coherence,and dual-frequency coherence can be
derived as special cases under this general framework.This paper also
introduces related approaches for modeling dependence through phase-amplitude
coupling and causality of (one-sided) filtered signals.
