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

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...

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Bibliographic Details
Published in2022 26th International Conference on Circuits, Systems, Communications and Computers (CSCC) pp. 11 - 15
Main Authors Zabcikova, Martina, Koudelkova, Zuzana, Jasek, Roman
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2022
Subjects
EEG
Electroencephalography
ERP
Feature extraction
known faces
lie detection
Mobile handsets
P300
Protocols
Support vector machines
Technological innovation
unknown faces
visual stimuli
Visualization
Online AccessGet full text
DOI10.1109/CSCC55931.2022.00011

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Summary: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.
DOI:10.1109/CSCC55931.2022.00011