바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

ISSN : 1226-9654

Article Contents

Prev Next
e-Submission

Vol.30 No.4
Citation Share

Support Vector Machine(SVM)-based classification of eyewitness memory using single-trial EEG

The Korean Journal of Cognitive and Biological Psychology / The Korean Journal of Cognitive and Biological Psychology, (P)1226-9654; (E)2733-466X
2018, v.30 no.4, pp.413-419
https://doi.org/10.22172/cogbio.2018.30.4.007



& (2018). Support Vector Machine(SVM)-based classification of eyewitness memory using single-trial EEG. The Korean Journal of Cognitive and Biological Psychology, 30(4), 413-419, https://doi.org/10.22172/cogbio.2018.30.4.007
  • Downloaded
  • Viewed
PDF Download

Abstract

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.

keywords
목격자 기억, 재인, 뇌파, 서포트 벡터 머신, eyewitness memory, recognition, EEG, support vector machine

Reference

1.

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.

2.

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.

3.

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.

4.

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.

5.

Fisher, R. P., Milne, R., & Bull, R. (2011). Interviewing cooperative witnesses. Current Directions in Psychological Science, 20, 16-19.

6.

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.

7.

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.

8.

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.

9.

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.

10.

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.

11.

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.

12.

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.

13.

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.

14.

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.

15.

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.

16.

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.

17.

Rugg, M. D., & Curran, T. (2007). Event-related potentials and recognition memory. Trends in Cognitive Science, 11, 251- 257.

18.

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.

19.

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.

20.

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.

21.

Wells, G. L., Memon, A., & Penrod, S. D. (2006). Eyewitness Evidence: Improving Its Probative Value. Psychological Science in the Public Interest, 7, 45-75.

The Korean Journal of Cognitive and Biological Psychology

e-Submission OA Policy