Abstract
Nowadays, data are recorded with increasing spatial
and temporal resolution. Commonly these data are analyzed
using univariate or bivariate approaches. Most of the analysis
techniques assume stationarity of the underlying dynamical
processes. Here, we present an approach that is capable of
analyzing multivariate data, the so-called renormalized partial
directed coherence. It utilizes the concept of Granger causality
and is applicable to non-stationary data. We discuss its abilities
and limitations and demonstrate its usefulness in an application
to sleep transitions in an animal model
and temporal resolution. Commonly these data are analyzed
using univariate or bivariate approaches. Most of the analysis
techniques assume stationarity of the underlying dynamical
processes. Here, we present an approach that is capable of
analyzing multivariate data, the so-called renormalized partial
directed coherence. It utilizes the concept of Granger causality
and is applicable to non-stationary data. We discuss its abilities
and limitations and demonstrate its usefulness in an application
to sleep transitions in an animal model
| Original language | English |
|---|---|
| Publication status | Published - 2011 |
| Event | IEEE Engineering in Medicine and Biology Society 2011 - Boston, United States Duration: 30 Aug 2011 → 3 Sep 2011 |
Conference
| Conference | IEEE Engineering in Medicine and Biology Society 2011 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 30/08/11 → 3/09/11 |
Keywords
- EEG Engineering Medicine