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

We propose Granger causality mapping (GCM) as an approach to explore directed
influences between neuronal populations (effective connectivity) in fMRI data. The method …

Nowadays, several types of brain imaging device are available to provide images of the
functional activity of the cerebral cortex based on hemodynamic, metabolic, or …

The analysis of multichannel recordings such as electroencephalography (EEG) and
magnetoencephalography (MEG) is important both for basic brain research and for medical …

There is much current interest in identifying the anatomical and functional circuits that are
the basis of the brain's computations, with hope that functional neuroimaging techniques will …

To show how causal interactions in neural dynamics are modulated by behavior, it is
valuable to analyze these interactions without perturbing or lesioning the neural mechanism …

Given two time series X and Y, their mutual information, I (X, Y)= I (Y, X), is the average
number of bits of X that can be predicted by measuring Y and vice versa. In the analysis of …

Over the last decades several techniques have been developed to analyze interactions in
multivariate dynamic systems. These analysis techniques have been applied to empirical …

The identification of effective connectivity from time-series data such as
electroencephalogram (EEG) or time-resolved function magnetic resonance imaging (fMRI) …

OBJECTIVE: To test a technique called Directed Transfer Function (DTF) for the estimation
of human cortical connectivity, by means of simulation study and human study, using high …