Journal List > Korean J Leg Med > v.42(2) > 1095164

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

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.

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

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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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