A study of problems encountered in Granger causality analysis from a neuroscience perspective
PA Stokes, PL Purdon - … of the national academy of sciences, 2017 - National Acad Sciences
Granger causality methods were developed to analyze the flow of information between time
series. These methods have become more widely applied in neuroscience. Frequency …
series. These methods have become more widely applied in neuroscience. Frequency …
Granger causality between multiple interdependent neurobiological time series: blockwise versus pairwise methods
X Wang, Y Chen, SL Bressler, M Ding - International journal of …, 2007 - World Scientific
Granger causality is becoming an important tool for determining causal relations between
neurobiological time series. For multivariate data, there is often the need to examine causal …
neurobiological time series. For multivariate data, there is often the need to examine causal …
Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data
Y Chen, SL Bressler, M Ding - Journal of neuroscience methods, 2006 - Elsevier
It is often useful in multivariate time series analysis to determine statistical causal relations
between different time series. Granger causality is a fundamental measure for this purpose …
between different time series. Granger causality is a fundamental measure for this purpose …
Multivariate Granger causality and generalized variance
Granger causality analysis is a popular method for inference on directed interactions in
complex systems of many variables. A shortcoming of the standard framework for Granger …
complex systems of many variables. A shortcoming of the standard framework for Granger …
Solved problems for Granger causality in neuroscience: A response to Stokes and Purdon
Granger-Geweke causality (GGC) is a powerful and popular method for identifying directed
functional ('causal') connectivity in neuroscience. In a recent paper, Stokes and Purdon …
functional ('causal') connectivity in neuroscience. In a recent paper, Stokes and Purdon …
Granger causality: basic theory and application to neuroscience
M Ding, Y Chen, SL Bressler - Handbook of time series analysis …, 2006 - Wiley Online Library
Multielectrode neurophysiological recordings produce massive quantities of data.
Multivariate time series analysis provides the basic framework for analyzing the patterns of …
Multivariate time series analysis provides the basic framework for analyzing the patterns of …
Behaviour of Granger causality under filtering: theoretical invariance and practical application
Granger causality (G-causality) is increasingly employed as a method for identifying directed
functional connectivity in neural time series data. However, little attention has been paid to …
functional connectivity in neural time series data. However, little attention has been paid to …
Granger causality analysis in neuroscience and neuroimaging
A key challenge in neuroscience and, in particular, neuroimaging, is to move beyond
identification of regional activations toward the characterization of functional circuits …
identification of regional activations toward the characterization of functional circuits …
Partial Granger causality—Eliminating exogenous inputs and latent variables
Attempts to identify causal interactions in multivariable biological time series (eg, gene data,
protein data, physiological data) can be undermined by the confounding influence of …
protein data, physiological data) can be undermined by the confounding influence of …