Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

By Yonghong Chen, Steven L. Bressler, Mingzhou Ding. Journal of Neuroscience Methods, 2006

342 citations recorded by the source corpus.

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. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2367d6f7f946c543bb07fe7d74e939037ce40c94

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Citations in the corpus

The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. (2014, methodology): “Thus it becomes essential to compute conditional spectral causalities where, as has been established (Chen et al., 2006a), the traditional dual-regression approach is known to produce spurious results, including negative causal estimates.”
Causal Discovery with Attention-Based Convolutional Neural Networks (2019, background): “Since pairwise methods cannot correctly handle indirect causal relationships, conditional Granger causality takes a third time series into account [25].”
Granger Causality: Basic Theory and Application to Neuroscience (2006, methodology): “In a recent paper we developed a partition matrix technique to overcome this problem [6].”
A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG (2010, positive): “…interdependent (see (Granger , 1969; Kami´nski et al., 1991, 2005; Gourévitch et al., 2006; Korzeniewska et al., 2003; Eichler, 2006; Blinowska et al., 2004; Ancona et al., 2004; Astolfi et al., 2004, 2005; Schelter et al., 2005; Chen et al., 2006) for detailed information about Granger causality).”
CZECH TECHNICAL UNIVERSITY IN PRAGUE (2005, methodology): “(Chen et al., 2006) applied a partition matrix technique to overcome the problem.”
Analyzing information flow in brain networks with nonparametric Granger causality (2008, methodology): “Parametric power, coherence, and Granger causality analysis of these data (Brovelli et al., 2004; Chen et al., 2006; Ding et al., 2006) has reported the following findings: (i) synchronized beta-frequency (15–30 Hz) oscillations linked together diverse sensorimotor areas to form a large-scale…”
Measuring and Testing Granger Causality over the Spectrum: An Application to European Production Expectation Surveys (2008, neutral): “´22;1ifi 2(n i 1) ; ( 12 ) with ´22;1ifi the 1ifi quantile of the chi-squared distribution with 2 degrees of freedom.”
A study of problems encountered in Granger causality analysis from a neuroscience perspective (2017, background): “(28) suggested using a reduced model featuring a colored noise term formed from the components of the estimated full model.”
Distinguishing time-delayed causal interactions using convergent cross mapping (2015, neutral): “3A, y1 causes y2 causes y3 causes y4 (equation (3)).”
Wiener-Granger Causality: A well established methodology (2011, neutral): “A conditional form of spectral WGC has been described by Chen et al. (2006).”
Detecting Causality in Complex Ecosystems (2012, background): “Although correlation is neither necessary nor sufficient to establish causation, it remains deeply ingrained in our heuristic thinking (8, 13, 16, 17).”