A pipeline integrating high-density EEG analysis and graph theory: A feasibility study on resting state functional connectivity

Riccardo Iandolo, Jessica Samogin, Federico Barban, Stefano Buccelli, Gaia Taberna, Marianna Semprini, Dante Mantini, Michela Chiappalone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in the field of human brain imaging by electrophysiological recordings allow to reliably quantify the connectivity patterns of spontaneous oscillatory activity. This has provided novel tools to investigate the neural basis underlying complex human behavior and to unravel the mechanisms of brain function and reorganization in response to neurological diseases. In this context, we present a pipeline integrating high-density electroencephalography (hd-EEG) analysis and graph theory. To test our pipeline, we conducted a feasibility study on hd-EEG analysis to recordings from healthy individuals, examining the frequency-specific small-world organization of brain connectivity. Here we present the preliminary results on a small sample size. The ultimate goal of our study is to extract graph-theory related metrics, such as small-worldness index, from injured patients and use them as electrophysiological biomarkers of the recovery.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages271-274
Number of pages4
ISBN (Electronic)9781538679210
DOIs
Publication statusPublished - May 16 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: Mar 20 2019Mar 23 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period3/20/193/23/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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