Concealed information detection using EEG for lie recognition by ERP P300 in response to visual stimuli: A review

Žabčíková, Martina; Koudelková, Zuzana; Jašek, Roman

dc.title	Concealed information detection using EEG for lie recognition by ERP P300 in response to visual stimuli: A review	en
dc.contributor.author	Žabčíková, Martina
dc.contributor.author	Koudelková, Zuzana
dc.contributor.author	Jašek, Roman
dc.relation.ispartof	WSEAS Transactions on Information Science and Applications
dc.identifier.issn	1790-0832 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2022
utb.relation.volume	19
dc.citation.spage	171
dc.citation.epage	179
dc.type	article
dc.language.iso	en
dc.publisher	World Scientific and Engineering Academy and Society
dc.identifier.doi	10.37394/23209.2022.19.17
dc.relation.uri	https://wseas.com/journals/articles.php?id=7201
dc.relation.uri	https://wseas.com/journals/isa/2022/a345109-011(2022).pdf
dc.subject	concealed information detection	en
dc.subject	EEG	en
dc.subject	EEG-based lie detection	en
dc.subject	electroencephalography	en
dc.subject	ERP P300	en
dc.subject	known	en
dc.subject	lie detection	en
dc.subject	unknown faces	en
dc.subject	visual stimuli	en
dc.description.abstract	Nowadays, lie detection based on electroencephalography (EEG) is a popular area of research. Current lie detectors can be controlled voluntarily and have several disadvantages. EEG-based lie detectors have become popular over polygraphs because human intentions cannot control them, are not based on subjective interpretation, and can therefore detect lies better. This paper's main objective was to give an overview of the scientific works on the recognition of concealed information using EEG for lie detection in response to visual stimuli of faces, as there is no existing review in this area. These were selected publications from the Web of Science (WoS) database published over the last five years. It was found that the Event-Related Potential (ERP) P300 is the most often used method for this purpose. The article contains a detailed overview of the methods used in scientific research in EEG-based lie detection using the ERP P300 component in response to known and unknown faces. © 2022 World Scientific and Engineering Academy and Society. All right reserved.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1011270
utb.identifier.obdid	43883630
utb.identifier.scopus	2-s2.0-85142059257
utb.source	j-scopus
dc.date.accessioned	2023-01-06T08:04:01Z
dc.date.available	2023-01-06T08:04:01Z
dc.description.sponsorship	Tomas Bata University in Zlin, TBU: IGA/CebiaTech/2022/006
dc.rights	Attribution 4.0 International
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.rights.access	openAccess
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.affiliation	MARTINA ZABCIKOVA, ZUZANA KOUDELKOVA, ROMAN JASEK Department of Informatics and Artificial Intelligence Tomas Bata University in Zlin, Faculty of Applied Informatics Nad Stranemi 4511, 760 05 Zlin CZECH REPUBLIC
utb.fulltext.dates	Received: April 19, 2021 Revised: July 11, 2022 Accepted: August 9, 2022 Published: September 9, 2022
utb.fulltext.references	[1] X. Lin, L. Sai, and Z. Yuan, Detecting Concealed Information with Fused Electroencephalography and Functional Near-infrared Spectroscopy, Neuroscience, Vol. 386, 2018, pp. 284–294. [2] L. A. Farwell, and S. S. Smith, Using brain MERMER Testing to Detect Knowledge Despite Efforts to Conceal, Journal of Forensic Sciences, Vol. 46, 2001, pp. 135–143. [3] S. Anwar, T. Batool, and M. Majid, Event Related Potential (ERP) based Lie Detection using a Wearable EEG headset, Proc. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2019, pp. 543–547. [4] A. Bablani, D. R. Edla, D. Tripathi, and V. Kuppili, An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification, Machine Vision and Applications, Vol. 30, 2019, pp. 813–832. [5] N. Saini, S. Bhardwaj, and R. Agarwal, Classification of EEG signals using hybrid combination of features for lie detection, Neural Computing and Applications, Vol. 32, 2020, pp. 3777–3787. [6] A. Bablani, D. R. Edla, D. Tripathi, S. Dodia, and S. Chintala, A Synergistic Concealed Information Test With Novel Approach for EEG Channel Selection and SVM Parameter Optimization, IEEE Transactions on Information Forensics and Security, Vol. 14, 2019, pp. 3057–3068. [7] A. H. Mehrnam, A. M. Nasrabadi, M. Ghodousi, A. Mohammadian, and S. Torabi, A new approach to analyze data from EEG-based concealed face recognition system, International journal of psychophysiology, Vol. 116, 2017, pp. 1–8. [8] A. Alsufyani, O. Hajilou, A. Zoumpoulaki, M. Filetti, H. Alsufyani, Ch. J. Solomon, S. J. Gibson, R. Alroobaea, and H. Bowman, Breakthrough Percepts of Famous Faces, Psychophysiology, Vol. 56, 2019. [9] A. Bablani, D. R. Edla, and S. Dodia, Classification of EEG Data using k-Nearest Neighbor approach for Concealed Information Test, Procedia Computer Science, Vol. 143, 2018, pp. 242–249. [10] A. Bablani, D. R. Edla, V. Kuppili, and D. Ramesh, A Multi Stage EEG data classification using k-Means and Feed Forward Neural Network, Clinical Epidemiology and Global Health, Vol. 8, 2020, pp. 718–724. [11] A. Bablani, D. R. Edla, V. Kuppili, and R. Dharavath, Lie Detection Using Fuzzy Ensemble Approach With Novel Defuzzification Method for Classification of EEG Signals, IEEE Transactions on Instrumentation and Measurement, Vol. 70, 2509413, 2021, pp. 1–13. [12] A. Bablani, D. R. Edla, and V. Kuppili, Deceit Identification Test on EEG Data Using Deep Belief Network, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2018, pp. 1–6. [13] S. Dodia, D. R. Edla, A. Bablani, D. Ramesh, and V. Kuppili, An Efficient EEG based Deceit Identification Test using Wavelet Packet Transform and Linear Discriminant Analysis, Journal of Neuroscience Methods, Vol. 314, 2019, pp. 31–40. [14] S. Dodia, D. R. Edla, A. Bablani, and R. Cheruku, Lie detection using extreme learning machine: A concealed information test based on short-time Fourier transform and binary bat optimization using a novel fitness function, Computational Intelligence, Vol. 36, 2019, pp. 637–658. [15] A. Akhavan, and M. H. Moradi, Detection of Concealed Information Using Multichannel Discriminative Dictionary and Spatial Filter Learning, IEEE Transactions on Information Forensics and Security, Vol. 13, 2018, pp. 2616–2627. [16] J. Gao, J. Song, Y. Yang, S. Yao, J. Guan, H. Si, H. Zhou, S. Ge, and P. Lin, Deception Decreases Brain Complexity, IEEE Journal of Biomedical and Health Informatics, Vol. 23, No. 1, 2019, pp. 164–174. [17] W. Chang, H. Wang, Z. Lu, and Ch. Liu, A Concealed Information Test System Based on Functional Brain Connectivity and Signal Entropy of Audio-Visual ERP, IEEE Transactions on Cognitive and Developmental Systems, Vol. 12, No. 2, 2020, pp. 361–370. [18] G. Lukacs, A. Grzadziel, M. Kempkes, and U. Ansorge, Item Roles Explored in a Modified P300-Based CTP Concealed Information Test, Applied Psychophysiology and Biofeedback, Vol. 44, 2019, pp. 195–209. [19] A. Akhavan, M. H. Moradi, and S. R. Vand. Subject-based discriminative sparse representation model for detection of concealed information, Computer Methods and Programs in Biomedicine, Vol. 143, 2017, pp. 25–33. [20] L. A. Farwell, Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials, Cognitive Neurodynamics, Vol. 6, 2012, pp. 115–154. [21] M. Funicelli, L. White, S. Ungureanu, and J. R. Laurence, An Independent Validation of the EEG-Based Complex Trial Protocol with Autobiographical Data and Corroboration of its Resistance to a Cognitively Charged Countermeasure, Applied Psychophysiology and Biofeedback, Vol. 46, 2021, pp. 287–299. [22] A. Alsufyani, K. Harris, A. Zoumpoulaki, M. Filetti, and H. Bowman, Breakthrough percepts of famous names, Cortex, Vol. 139, 2021, pp. 267–281. [23] P. Liu, H. Shen, and S. Ji, Functional Connectivity Pattern Analysis Underlying Neural Oscillation Synchronization during Deception, Neural Plasticity, Vol. 2019, 2019, p. 10. [24] I. Karton, and T. Bachmann, Disrupting dorsolateral prefrontal cortex by rTMS reduces the P300 based marker of deception. Brain Behavior, Vol. 7, No. 4, 2017. [25] Q. Liu, X.G. Zhao, Z.G. Hou, and H.G. Liu, Deep Belief Networks for EEG-Based Concealed Information Test, Advances in Neural Networks, Vol. 10262, 2017, pp. 498–506. [26] M. D. Kohan, A. M. Nasrabadi, M. B. Shamsollahi, and A. Sharifi, EEG/PPG effective connectivity fusion for analyzing deception in interview, Signal Image and Video Processing, Vol. 14, 2020, pp. 907–914. [27] S. Thakur, R. Dharavath, and D. R. Edla, Spark and Rule-KNN based scalable machine learning framework for EEG deceit identification, Biomedical Signal Processing and Control, Vol. 58, 2020. [28] Y.F. Lai, M.Y. Chen, and H.S. Chiang, Constructing the lie detection system with fuzzy reasoning approach, Granular Computing, Vol. 3, 2018, pp. 169–176. [29] W. Chang, H. Wang, Ch. Hua, Qi. Wang, and Y. Yuan, Comparison of different functional connectives based on EEG during concealed information test, Biomedical Signal Processing and Control, Vol. 49, 2019, pp. 149–159.
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, Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, Zlin, 760 05, Czech Republic
utb.fulltext.projects	IGA/CebiaTech/2022/006
utb.fulltext.faculty	Faculty of Applied Informatics
utb.fulltext.ou	Department of Informatics and Artificial Intelligence