Using brain connectivity measure of EEG synchrostates for discriminating typical and Autism Spectrum Disorder

Wasifa Jamal, Saptarshi Das, Koushik Maharatna, Doga Kuyucu, Federico Sicca, Lucia Billeci, Fabio Apicella, Filippo Muratori

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

Abstract

In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data.

Original languageEnglish
Title of host publicationInternational IEEE/EMBS Conference on Neural Engineering, NER
Pages1402-1405
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

Keywords

  • Autism
  • Brain connectivity
  • Complex networks
  • EEG phase synchronization
  • Modularity
  • Synchrostate

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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