Abstract
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 |
Fingerprint
Keywords
- EEG Engineering Medicine
Cite this
Multivariate Analysis of Dynamical Processes with applications in the Neurosciences. / Schelter, Bjoern; Sommerlade, Linda; Platt, Bettina; Plano, Andrea; Thiel, Marco; Timmer, Jens.
2011. Paper presented at IEEE Engineering in Medicine and Biology Society 2011, Boston, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Multivariate Analysis of Dynamical Processes with applications in the Neurosciences
AU - Schelter, Bjoern
AU - Sommerlade, Linda
AU - Platt, Bettina
AU - Plano, Andrea
AU - Thiel, Marco
AU - Timmer, Jens
PY - 2011
Y1 - 2011
N2 - 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
AB - 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
KW - EEG Engineering Medicine
M3 - Paper
ER -