A new EEG-based causal information measure for identifying brain connectivity in response to perceived audio quality

Ketan Mehta, Jorg Kliewer

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

1 Scopus citations

Abstract

In this paper, electroencephalography (EEG) measurements are used to assess cortical functional connectivity in response to perceived audio quality. Specifically, in the conducted experiment the brainwave response patterns of human subjects are directly recorded using a high resolution EEG while they listen to audio whose quality varies with time. A new causal bi-directional information (CBI) measure is proposed which quantifies the information flow between EEG electrodes by appropriately grouping them into specific regions of interest (ROIs) over the cortex. It is shown that CBI can be intuitively interpreted as a causal bi-directional modification of directed information applied to a generalized cortical network setting, and inherently calculates the divergence of the observed data from a multiple access channel with feedback. The proposed measure is used to analyze and compare the information flow between ROI pairs for the case when the subject listens to high quality audio compared to when the subject listens to low quality audio. The results indicate that CBI is a more robust measure for inferring connectivity when compared to using standard directed information measures.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
EditorsMerouane Debbah, David Gesbert, Abdelhamid Mellouk
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389990
DOIs
StatePublished - Jul 28 2017
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: May 21 2017May 25 2017

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Other

Other2017 IEEE International Conference on Communications, ICC 2017
CountryFrance
CityParis
Period5/21/175/25/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Electroencephalography (EEG)
  • audio quality
  • causal conditioning
  • directed information
  • functional connectivity

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