Time-domain analysis of EEG during guilty knowledge test: Investigation of epoch extraction criteria

Anna Caterina Merzagora, Meltem Izzetoglu, Scott Bunce, Banu Onaral

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

4 Scopus citations

Abstract

The study of electroencephalography (EEG) for deception detection has long been regarded as an alternative to the standard polygraphy, whose main shortcoming is its unacceptably low level of reliability. Most of the EEG deception research has focused on the amplitude and topography of P300. However, the characteristics of the P300 component are tightly connected to the experimental design and hence countermeasures are easily available for P300-based deception detection. The goal of this study is to evaluate different epoching criteria for the extraction of EEG features that are most suitable for the discrimination between truthful and deceptive responses. In order to reach this aim, a modified version of the Guilty Knowledge Test was used where EEG recordings were obtained from four frontal electrodes and two midline electrodes. In none of the electrodes the P300 component differed between deceptive and truthful responses. Differences have instead been revealed through the extraction of response-locked epochs and analysis of area under the curve.

Original languageEnglish (US)
Title of host publication29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Pages1302-1305
Number of pages4
DOIs
StatePublished - Dec 1 2007
Event29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France
Duration: Aug 23 2007Aug 26 2007

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Other

Other29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Country/TerritoryFrance
CityLyon
Period8/23/078/26/07

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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