The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. Granger causality constitutes a major tool to reveal effective connectivity, and it is widely used to analyze EEG/MEG data as well as fMRI signals in its linear version. In order to capture nonlinear interactions between even short and noisy time series, a kernel version of Granger causality has been recently proposed. We review kernel Granger causality and show the application of this approach on EEG signals.
Nonlinear Granger causality for brain connectivity
STRAMAGLIA, Sebastiano;ANGELINI, Leonardo;
2011-01-01
Abstract
The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. Granger causality constitutes a major tool to reveal effective connectivity, and it is widely used to analyze EEG/MEG data as well as fMRI signals in its linear version. In order to capture nonlinear interactions between even short and noisy time series, a kernel version of Granger causality has been recently proposed. We review kernel Granger causality and show the application of this approach on EEG signals.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
