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Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
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A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands.

Jonathan D Drover1, Nicholas D Schiff2

  • 1Weill Cornell Medical College, New York, NY, 10065, USA. jod2017@med.cornell.edu.

Journal of Computational Neuroscience
|August 1, 2018
PubMed
Summary

We introduce Frequency Extracted Hierarchical Decomposition (FEHD) to analyze complex time series data. This method reveals causal hierarchies within data across specific frequency bands, enhancing stability and reliability.

Keywords:
AutoregressionElectroencephalogramGranger causalityHierarchical decomposition

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Area of Science:

  • Biomedical Engineering
  • Time Series Analysis
  • Neuroscience

Background:

  • Multivariate time series analysis is crucial for understanding complex systems.
  • Existing hierarchical decomposition methods have limitations in frequency band specificity.
  • Causal relationships in biological and neurological data require robust analytical tools.

Purpose of the Study:

  • To introduce a novel method, Frequency Extracted Hierarchical Decomposition (FEHD), for analyzing multivariate time series.
  • To identify and order components within a causal hierarchy across specified frequency bands.
  • To improve the stability, reliability, and sensitivity of causal structure identification.

Main Methods:

  • FEHD identifies linear combinations of time series components exhibiting a causal hierarchy.
  • The method allows for causal hierarchy determination within arbitrarily specified frequency bands.
  • A novel minimization strategy enhances decomposition stability and reliability.

Main Results:

  • FEHD successfully identified causal hierarchies in artificial time series with known causal graphs.
  • Application to human EEG data demonstrated the method's utility in clinical populations.
  • The enhanced minimization strategy improved model sensitivity and parameter reliability.

Conclusions:

  • FEHD offers a powerful new approach for studying causal structures in multivariate time series, particularly within specific frequency bands.
  • The method provides a stable and reliable tool for analyzing complex biological signals like EEG.
  • FEHD has significant potential for advancing research in neuroscience and biomedical engineering.