ISSN : 1226-9654
본 연구는 여러 단어로 구성된 기억 단서에 의해 유발된 뇌파를 이용하여 목격 여부를 우연 수준 이상으로 구별할 수 있는지 알아보기 위해 수행되었다. Ham, Kim과 Jeong(2018)의 연구에서 수집한 사건관련전위 자료(n=69)를 재분석하여 선형 서포트 벡터 머신(support vector machine, SVM) 분류 모델의 정확성을 평가했다. 참가자들마다 절도 범죄를 재연한 동영상에서 목격할 수 있었던 물건과 목격하지 않은 물건을 정확하게 분류한 적중 시행과 정확 기각 시행의 뇌파가 분석에 이용되었다. 선형 SVM 커널을 이용하여 뇌파를 분류한 결과, 기억 단서 제시 후 800∼850ms 구간에서 분류 정확률 평균이 57.43%로 가장 높았다. 이런 결과는 기억 단서가 제시되었을 때 이 단서와 관련된 세부적인 정보 인출이 이뤄지는 것으로 여겨지는 구간의 뇌파를 분석하여 목격 여부를 구별할 수 있음을 시사한다. 마지막으로 수사 현장에서 뇌파를 활용할 때 고려해야 할 점에 대해 논의하였다.
The focus of this study was discriminating the eyewitness memory using single-trial electroencephalogram (EEG) evoked by memory cues composed of multiple words. We reanalyzed the event-related potentials (ERP) data (n=69) from Ham, Kim, & Jeong (2018) studies, and two-class classification was conducted to distinguish correctly witnessed objects vs unwitnessed objects trials using linear support vector machine (SVM) algorithm. The single-trial classification analysis showed the post-stimulus EEG activity between 800 to 850 ms was the most accurate interval for the memory classification (57.43%). This result suggests that the EEG during memory retrieval can be used to determine whether or not it is witnessed. Finally, we discussed some points to consider when using EEG for eyewitness memory classification.
Abe, N., Okuda, J., Suzuki, M., Sasaki, H., Matsuda, T., Mori, E., ..., & Fujii, T. (2008). Neural correlates of true memory, false memory, and deception. Cerebral Cortex, 18, 2811- 2819.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289-300.
Bode, S., Feuerriegel, D., Bennett, D., & Alday, P. M. (2018). The Decision Decoding ToolBOX (DDTBOX) - A multivariate pattern analysis toolbox for event-related potentials. Neuroinformatics, 1-16.
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, 1-27.
Fisher, R. P., Milne, R., & Bull, R. (2011). Interviewing cooperative witnesses. Current Directions in Psychological Science, 20, 16-19.
Grootswagers, T., Wardle, S. G., & Carlson, T. A. (2017). Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time series neuroimaging data. Journal of Cognitive Neuroscience, 29, 677-697.
Hagoort, P., Brown, C., & Groothusen, J. (1993). The syntactic positive shift (SPS) as an ERP measure of syntactic processing. Language and Cognitive Processes, 8, 439-483.
Ham, K. S., & Jeong, H. J. (2017). The effect of horizontal saccadic eye movement on eyewitness memory recall. The Korean Journal of Forensic Psychology, 8, 195-211.
Ham, K. S., Kim, K. P., & Jeong, H. J. (2018). Estimating eyewitness memory accuracy using event-related potentials (ERPs) Focusing on FN400 and LPC. Korea Journal of Investigative Psychology, 4, 1-12.
Ham, K. S., Kim, K. P., Jeong, H. J., & Yoo, S. H. (2018). The assessment of eyewitness memory using electroencephalogram: application of machine learning algorithm. Korean Journal of Legal Medicine, 42, 62-70.
Ham, K. S., Pyo, C. Y., Jang, T. I., & Yoo, S. H. (2015). Estimation of eyewitness identification Accuracy by event-related potentials. Korean Journal of Legal Medicine, 39, 115-119.
Kim, M. Y., & Kim, S. U. (2016). SAI (Self-Administered Interview) effect on the accuracy of recalling the event. The Korean Journal of Social and Personality Psychology, 30, 63-75.
Lefebvre, C. D., Marchand, Y., Smith, S. M., & Connolly, J. F. (2007). Determining eyewitness identification accuracy using event-related brain potentials (ERPs). Psychophysiology, 44, 894-904.
Müller-Putz, G., Scherer, R., Brunner, C., Leeb, R., & Pfurtscheller, G. (2008). Better than random: a closer look on BCI results. International Journal of Bioelectromagnetism, 10, 52-55.
Nemrodov, D., Niemeier, M., Mok, J. N., & Nestor, A. (2016). The time course of individual face recognition: A pattern analysis of ERP signals. Neuroimage, 132, 469-476.
Noh, E., Herzmann, G., Curran, T., & de Sa, V. R. (2014). Using single-trial EEG to predict and analyze subsequent memory. Neuroimage, 84, 712-723.
Rugg, M. D., & Curran, T. (2007). Event-related potentials and recognition memory. Trends in Cognitive Science, 11, 251- 257.
Tanner, D., Morgan-Short, K., & Luck, S. J. (2015). How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition. Psychophysiology, 52, 997-1009.
Wallstrom, G. L., Kass, R. E., Miller, A., Cohn, J. F., & Fox, N. A. (2004). Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component- based methods. International Journal of Psychophysiology, 53, 105-119.
Weber, N., Brewer, N., Wells, G. L., Semmler, C., & Keast, A. (2004). Eyewitness identification accuracy and response latency: the unruly 10-12-second rule. Journal of Expeprimental Psychoogyl: Appiedl, 10, 139-147.
Wells, G. L., Memon, A., & Penrod, S. D. (2006). Eyewitness Evidence: Improving Its Probative Value. Psychological Science in the Public Interest, 7, 45-75.