期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 203, 期 1, 页码 173-185出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2011.08.042
关键词
Non-stationary causal influences; Time-resolved partial directed coherence; Vector autoregressive processes; State space models; Expectation-Maximization algorithm
资金
- German Science Foundation [Ti315/4-2]
- German Federal Ministry of Education and Research (BMBF) [01GQ0420]
- Excellence Initiative of the German Federal Government
- State Government
- Landesstiftung Baden-Wurttemberg
- Eliteprogramme
- UK Biotechnology and Biological Sciences Research Council under the Systems Approaches to Biological Research (SABR) Initiative [BB/FO0513X/1]
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. (C) 2011 Elsevier B.V. All rights reserved.
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