Open AccessJournal Article
Estimating Granger causality from Fourier and wavelet transforms of time series data
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TL;DR: In this article, the authors extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences, and illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.
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Abstract: Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. Here, we extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences. We illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.
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Citations
Inferring correlations associated to causal interactions in brain signals using autoregressive models.
Víctor J. López-Madrona,Fernanda S. Matias,Claudio R. Mirasso,Santiago Canals,Ernesto Pereda +4 more
TL;DR: In this article, an extension of the well-known Granger causality (GC) was introduced to analyze the correlation associated to the specific influence that a transmitter node has over the receiver.
Effective connectivity and cortical information flow under visual stimulation in migraine with aura
Gabriele Trotta,Sebastiano Stramaglia,Mario Pellicoro,Roberto Bellotti,Daniele Marinazzo,Marina de Tommaso +5 more
- 13 Jun 2013
TL;DR: MWA patients showed increased non linear Granger causality values in beta band under all types of visual stimulation, and increased information flow toward frontal regions, with respect to MWoA and controls, which outline important pathophysiological difference between migraine phenotypes.
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Determination of ECoG information flow activity based on Granger causality and Hilbert transformation
TL;DR: The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.
4
•Posted Content
Fast Estimation of Causal Interactions using Wold Processes
TL;DR: In this article, Granger causality matrices for multivariate point processes are learned in an asymptotically fast manner, with a complexity of O(n(n), log(n) + log(N), n(log(N)), n(n+log(n)).
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•Dissertation
Frequency and time domain analysis of networks of interacting processes: what can be achieved from records of short duration
F.M. Allehiany
- 01 Jan 2012
TL;DR: The structure of the causal influences shows that there are statistically significant reciprocal causal effects between processes representing the brain's region, the frontal lobe, the central area, the parietal lobe and the temporal lobe, and the depth of correlations is unknown for the multivariate autoregressive model of order 2.
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