Abstract We introduce Ordinal Synchronization (OS) as a new measure to quantify
synchronization between dynamical systems. OS is calculated from the extraction of the …

We present a methodology to characterize synchronization in time series based on symbolic
representations. Each time series is mapped onto a sequence of p-dimensional delay …

Synchronization or phase-locking between oscillating neuronal groups is considered to be
important for coordination of information among cortical networks. Spectral coherence is a …

The study of complex systems consisting of many interacting subsystems requires the use of
analytical tools which can detect statistical dependencies between time series recorded from …

Determining synchronization, causality, and dynamical similarity in highly complex nonlinear
systems like brains is challenging. Although distinct, these measures are related by the …

Synchronization measures have become an important tool for exploring the relationships
between time series. We review three recently proposed nonlinear synchronization …

Indirect quantification of the synchronization between two dynamical systems from
measured experimental data has gained much attention in recent years, especially in the …

Phase synchrony analysis is a relatively new concept that is being increasingly used on
neurophysiological data obtained through different methodologies. It is currently believed …

Detecting nonlinear correlations between time series presents a hard problem for data
analysis. We present a generative statistical modeling method for detecting nonlinear …

Synchronization cluster analysis is an approach to the detection of underlying structures in
data sets of multivariate time series, starting from a matrix R of bivariate synchronization …