FREQUENCY-SPECIFIC NON-LINEAR GRANGER CAUSALITY IN A NETWORK OF BRAIN SIGNALS

Archishman Biswas, Hernando Ombao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We propose a novel algorithm to extract frequency-band specific and non-linear Granger causality (Spectral NLGC) connections between components of a multivariate time series. The advantage of our model over traditionally used VAR based models, as demonstrated in simulations, is the ability to capture complex dependence structures in a network. In addition to the simulations, the proposed method uncovered non-linear dynamics in an epileptic seizure EEG data. Spectral NLGC gives new meaningful insights into frequency specific connectivity changes at the onset of epileptic seizure. Results of both simulated and brain signals confirm the viability of the proposed algorithm as a good tool for exploration of directed connectivity in a network.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1401-1405
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: May 23 2022May 27 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period05/23/2205/27/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE

Keywords

  • Directed connectivity
  • Electroencephalograms
  • Multi-layer perceptrons
  • Networks
  • Spectral dependence

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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