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EEG-based lie detection using ERP P300 in response to known and unknown faces: An overview

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dc.title EEG-based lie detection using ERP P300 in response to known and unknown faces: An overview en
dc.contributor.author Žabčíková, Martina
dc.contributor.author Koudelková, Zuzana
dc.contributor.author Jašek, Roman
dc.relation.ispartof Proceedings - 26th International Conference on Circuits, Systems, Communications and Computers, CSCC 2022
dc.identifier.isbn 978-1-6654-8186-1
dc.date.issued 2022
dc.citation.spage 11
dc.citation.epage 15
dc.event.title 26th International Conference on Circuits, Systems, Communications and Computers, CSCC 2022
dc.event.location Chania, Crete Island
utb.event.state-en Greece
utb.event.state-cs Řecko
dc.event.sdate 2022-07-19
dc.event.edate 2022-07-22
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/CSCC55931.2022.00011
dc.relation.uri https://ieeexplore.ieee.org/document/10017818
dc.relation.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10017818
dc.subject EEG en
dc.subject electroencephalography en
dc.subject ERP en
dc.subject known faces en
dc.subject lie detection en
dc.subject P300 en
dc.subject unknown faces en
dc.subject visual stimuli en
dc.description.abstract Concealed information detection is nowadays an essential part of security. Conventional lie detectors are expensive, time-consuming, and their accuracy depends on the subject. Many researchers have focused on investigating concealed information for lie detection using electroencephalography (EEG) to recognize a lie better. This work aimed to provide an overview of scientific studies on EEG-based lie detection in the context of ERP P300 during the presentation of known and unknown faces published in the last five years (2017-2022). To the best of our knowledge, there is no recent available review of the most used methods for EEG data analysis in this field. For that reason, this article was created containing the current most used methods for feature extraction and classification, protocols, and accuracy of individual approaches. © 2022 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011404
utb.identifier.obdid 43884172
utb.identifier.scopus 2-s2.0-85147731923
utb.source d-scopus
dc.date.accessioned 2023-02-25T13:54:25Z
dc.date.available 2023-02-25T13:54:25Z
dc.description.sponsorship Tomas Bata University in Zlin, TBU: IGA/CebiaTech/2022/006
utb.ou Department of Informatics and Artificial Intelligence
utb.contributor.internalauthor Žabčíková, Martina
utb.contributor.internalauthor Koudelková, Zuzana
utb.contributor.internalauthor Jašek, Roman
utb.fulltext.sponsorship This work was supported by IGA (Internal Grant Agency) of Tomas Bata University in Zlin under the project No. IGA/CebiaTech/2022/006.
utb.scopus.affiliation Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2022/006
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