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.

Autore Pugliese

• Angelini Leonardo , Stramaglia Sebastiano

Tutti gli autori

• ANGELINI L.;STRAMAGLIA S.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2011

ISSN

Non Disponibile

ISBN

978-1-4244-9337-1

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

1

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

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Codici ASJC

Non Disponibile