Inference of Granger causal time-dependent influences in noisy multivariate time series.
J Neurosci Methods
; 203(1): 173-85, 2012 Jan 15.
Article
in En
| MEDLINE
| ID: mdl-21944999
Inferring Granger-causal interactions between processes promises deeper insights into mechanisms underlying network phenomena, e.g. in the neurosciences where the level of connectivity in neural networks is of particular interest. Renormalized partial directed coherence has been introduced as a means to investigate Granger causality in such multivariate systems. A major challenge in estimating respective coherences is a reliable parameter estimation of vector autoregressive processes. We discuss two shortcomings typical in relevant applications, i.e. non-stationarity of the processes generating the time series and contamination with observational noise. To overcome both, we present a new approach by combining renormalized partial directed coherence with state space modeling. A numerical efficient way to perform both the estimation as well as the statistical inference will be presented.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Models, Neurological
/
Models, Theoretical
/
Nerve Net
Limits:
Animals
Language:
En
Journal:
J Neurosci Methods
Year:
2012
Document type:
Article
Affiliation country:
Germany
Country of publication:
Netherlands