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Ham, Kim, Jeong, and Yoo: The Assessment of Eyewitness Memory Using Electroencephalogram: Application of Machine Learning Algorithm
Original Article

Korean Journal of Legal Medicine 2018; 42(2): 62-70.

Published online: 31 May 2018

DOI: https://doi.org/10.7580/kjlm.2018.42.2.62

The Assessment of Eyewitness Memory Using Electroencephalogram: Application of Machine Learning Algorithm

Keunsoo Ham1, Ki Pyeong Kim1, Hojin Jeong1, Seong Ho Yoo2

1Psychological Forensics Division, National Forensic Service, Wonju, Korea.

2Department of Forensic Medicine, Institute of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea.

Correspondence to Keunsoo Ham. Psychological Forensics Division, National Forensic Service, 10 Ipchun-ro, Wonju 26460, Korea. Tel: +82-33-902-5355, Fax: +82-33-902-5923, ksham@korea.kr

Co-correspondence to Seong Ho Yoo. Department of Forensic Medicine, Institute of Forensic Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. Tel: +82-2-740-8360, Fax: +82-2-902-8340, yoosh@snu.ac.kr

Received 2 February 2018       Revised 23 March 2018       Accepted 19 May 2018

© Copyright 2018 by the Korean Society for Legal Medicine

The Korean Society for Legal Medicine

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study was conducted to investigate whether memory accuracy can be assessed by analyzing electrophysiological responses (i.e., electroencephalography [EEG]) for retrieval cues related to the witnessed scene. Specifically, we examined the different patterns of EEG signals recorded during witnessed (target) and unwitnessed (lure) stimuli using event-related potential (ERP) analysis. Moreover, using multivariate pattern analysis, we also assessed how accurately single-trial EEG signals can classify target and lure stimuli. Participants watched a staged-crime video (theft crime), and the EEG signals evoked by the objects shown in the video were analyzed (n=56). Compared to the target stimulus, the lure stimulus elicited larger negative ERPs in frontal brain regions 300 to 500 milliseconds after the retrieval cue was presented. Furthermore, the EEG signals observed 450 to 500 milliseconds after the retrieval cue was presented showed the best classification performance related to eyewitness memory, with the mean classification accuracy being 56%. These results suggest that the knowledge and techniques of cognitive neuroscience can be used to estimate eyewitness memory accuracy.

Keywords: Memory, Recognition, Electroencephalography, Event-related potentials, Machine learning, Cognitive neuroscience

Figures and Tables

Fig. 1

The procedure and recognition task in this study. MINI, Mini-International Neuropsychiatric Interview; EEG, electroencephalography.

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Fig. 2

The event-related potentials (ERP) of hit and correct rejection condition. (A) The ERP of hit (green) and correct rejection (red) condition at thirty electroencephalography (EEG) channels. The topographic maps of mean EEG variation and channels (red dot) significantly different in hit and correct rejection conditions 300 to 500 milliseconds (B) and 500 to 800 milliseconds (C) after retrieval cue.

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Fig. 3

Support vector machine classification accuracy according to time windows (50 msec; −200 to 1,000 msec). a)False discovery rate-corrected P<0.05.

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Table 1

The results of SVM classification after retrieval cue presentation

kjlm-42-62-i001

SVM, support vector machine.

a)False discovery rate-corrected P<0.05; b)Maximum value of each column.

Acknowledgments

This work was supported by National Forensic Service (2017-Psychology-01), Ministry of the Interior and safety, Republic of Korea.

Notes

Conflicts of Interest No potential conflict of interest relevant to this article was reported.

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