The Effect of Graph Frequencies on Dynamic Structures in Graph Signal Processing

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Multivariate signals are signals consisting of multiple signals measured simultaneously over time and are most commonly acquired by sensor networks. The emerging field of graph signal processing (GSP) promises to analyse dynamic characteristics of multivariate signals, while at the same time taking the network, or spatial structure between the signals into account. To do so, GSP decomposes the multivariate signals into graph frequency signals, which are ordered by their magnitude. However, the meaning of the graph frequencies in terms of this ordering remains poorly understood. Here, we investigate the role the ordering plays in preserving valuable dynamic structures in the signals, with neuroimaging applications in mind. In order to overcome the limitations in sample size common to neurophysiological data sets, we introduce a minimalist simulation framework to generate arbitrary amounts of data. Using this artificial data, we find that lower graph frequency signals are less suitable for classifying neurophysiological data than higher graph frequency signals. We further introduce a baseline testing framework for GSP. Using this framework, we conclude that dynamic, or spectral structures are poorly preserved in GSP, high-lighting current limitations of GSP for neuroimaging.

Original languageEnglish
Title of host publication2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665470292
ISBN (Print)9781665470308
Publication statusE-pub ahead of print - 19 Jan 2023
Event2022 IEEE Signal Processing in Medicine and Biology Symposium - Philadelphia, United States
Duration: 3 Dec 20223 Dec 2022

Publication series

Name2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)


Conference2022 IEEE Signal Processing in Medicine and Biology Symposium
Abbreviated titleSPMB 2022
Country/TerritoryUnited States
Internet address

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  • graph Fourier transform
  • graph signal processing
  • multivariate signals
  • neurophysiological signals

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Medicine (miscellaneous)


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