The availability of time series of the evolution of the properties of physical systems is
increasing, stimulating the development of many novel methods for the extraction of …

Multivariate time series analysis is extensively used in neurophysiology with the aim of
studying the relationship between simultaneously recorded signals. Recently, advances on …

Background People with epilepsy are burdened with the apparent unpredictability of
seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) …

To derive tests for randomness, nonlinear-independence, and stationarity, we combine
surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and …

The analysis of the interdependence between time series has become an important field of
research in the last years, mainly as a result of advances in the characterization of …

The dynamical systems found in nature are rarely isolated. Instead they interact and
influence each other. The coupling functions that connect them contain detailed information …

Since the operative definition given by CWJ Granger of an idea expressed by N. Wiener, the
Wiener-Granger causality (WGC) has been one of the most relevant concepts exploited by …

The multivariate versus bivariate measures of Granger causality were considered. Granger
causality in the application to multivariate physiological time series has the meaning of the …

Complex systems are challenging to understand, especially when they defy manipulative
experiments for practical or ethical reasons. Several fields have developed parallel …

The investigation of synchronization phenomena on measured experimental data such as
biological time series has recently become an increasing focus of interest. Different …