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On the interpretability and computational reliability of frequency-domain Granger causality

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Abstract
This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.
Keywords
granger causality, signal processing

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Citation

Please use this url to cite or link to this publication:

Chicago
Faes, Luca, Sebastiano Stramaglia, and Daniele Marinazzo. 2017. “On the Interpretability and Computational Reliability of Frequency-domain Granger Causality.” F1000RESEARCH 6.
APA
Faes, L., Stramaglia, S., & Marinazzo, D. (2017). On the interpretability and computational reliability of frequency-domain Granger causality. F1000RESEARCH, 6.
Vancouver
1.
Faes L, Stramaglia S, Marinazzo D. On the interpretability and computational reliability of frequency-domain Granger causality. F1000RESEARCH. F1000 Research, Ltd.; 2017;6.
MLA
Faes, Luca, Sebastiano Stramaglia, and Daniele Marinazzo. “On the Interpretability and Computational Reliability of Frequency-domain Granger Causality.” F1000RESEARCH 6 (2017): n. pag. Print.
@article{8538890,
  abstract     = {This Correspondence article is a comment which directly relates to the paper {\textquotedblleft}A study of problems encountered in Granger causality analysis from a neuroscience perspective{\textquotedblright} (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name {\textquotedblleft}causality{\textquotedblright}, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.},
  articleno    = {1710},
  author       = {Faes, Luca and Stramaglia, Sebastiano and Marinazzo, Daniele},
  issn         = {2046-1402},
  journal      = {F1000RESEARCH},
  language     = {eng},
  publisher    = {F1000 Research, Ltd.},
  title        = {On the interpretability and computational reliability of frequency-domain Granger causality},
  url          = {http://dx.doi.org/10.12688/f1000research.12694.1},
  volume       = {6},
  year         = {2017},
}

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