A new approach for concealed information identification based on ERP assessment

Journal of Medical Systems

Volumn 36, Issue 4, 2012, Pages 2401-2409

  Zhao, Min ^a,b   Zheng, Chongxun ^a   Zhao, Chunlin ^a,b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b Engineering College of Armed Police Force   (China)

Author keywords

CIT; ERP; KPCA; SVM; WCs

Indexed keywords

ACCURACY; ADULT; ALPHA RHYTHM; ARTICLE; BETA RHYTHM; CLASSIFICATION ALGORITHM; CONCEALED INFORMATION IDENTIFICATION; DELTA RHYTHM; ELECTROENCEPHALOGRAM; FEMALE; HUMAN; HUMAN EXPERIMENT; INFORMATION SYSTEM; LEARNING ALGORITHM; MALE; RESPONSE TIME; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; THETA RHYTHM; ALGORITHM; DECEPTION; EVOKED RESPONSE; FORENSIC MEDICINE; WAVELET ANALYSIS;

ALGORITHMS; DECEPTION; EVOKED POTENTIALS; FEMALE; HUMANS; LIE DETECTION; MALE; WAVELET ANALYSIS; YOUNG ADULT;

EID: 84873033543     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9707-0     Document Type: Article

References (38)

1. Meijer, E. H., Verschuere, B., and Harald Merchelbach, H., Detecting criminal intent with the concealed information test. Open Criminol. J. 3:44-47, 2010.
2. The National Academies' National Research Council, The polygraph and lie detection. National Academies Press, Washington, DC, pp. 11-28, 2003.
3. Lykken, D. T., The GSR in the deteciton of guilt. J. Appl. Psychol. 43:385-388, 1959.
4. Ben-Shakhar, G., and Elaad, E., The validity of psychophysiological detection of information with the guilty knowledge test: a meta-Analytic interview. J. Appl. Psychol. 88:131-151, 2003.
5. Rosenfeld, P. J., Alternative views of Bashore and Rapp's (1993) alternatives to traditional polygraph: a critique. Psychol. Bull. 117:159-166, 1995.
6. Rosenfeld, J. P., Shue, E., and Singer, E., Single versus multiple probe blocks of P300-based concealed information tests for self-referring versus incidentally obtained information. Biol. Psychol. 74:396-404, 2007.
7. Nose, I., Murai, J., and Taira, M., Disclosing concealed information on the basis of cortical activations. Neuroimage 44:1380-1386, 2009.
8. Ganis, G., Kosslyn, S. M., Stose, S., Thompson, W. L., and Yurgelun-Todd, D. A., Neural correlates of different types of deception: an fMRI investigation. Cereb. Cortes. 13:830-836, 2003.
9. Meixner, J. B., and Rosenfeld, J. P., Countermeasure mechanisms in a P300-based concealed information test. Psychophysiology 47:57-65, 2010.
10. Mertens, R., and Allen, J. J. B., The role of psychophysiology in forensic assessments: deception detection, ERPs, and virtual reality mock crime scenarios. Psychophysiology 45:286-298, 2008.
11. Fehér, O., and Schnell, R., Visual event related potentials the origin of wave P300 a computer model. Acta Biol. Hung. 53(4):445-464, 2002.
12. Abootalebi, V., Moradi, M. H., and Khalizadeh, M. A., Comparison of methods for ERP assessment in a P300-based GKT. Int. J. Psychophysiol. 62:309-320, 2006.
13. Farwell, L. A., and Donchin, E., The truth will out: interrogative polygraphy ("lie detecion") with event-related brain potentials. Psychophysiology 28:531-547, 1991.
14. Rosenfeld, J. P., Soskins, M., Bosh, G., and Ryan, A., Simple, effective countermeasures to P300-based tests of detection of concealed information. Psychophysiology 41:205-219, 2004.
15. Demiralp, T., Ademoglu, A., Schurmann, M., Basar-Eroglu, C., and Basar, E., Detection of P300 waves in Single Trails by the Wavelet Transform (WT). Brain Lang. 66:108-128, 1999.
16. Merazgora, A. C., Bunce, S., Izzetoglu, M., and Onaral, B., Wavelet analysis for EEG feature extraction in deception detection. Proceeding of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug. 2434-2437, 2006.
17. Demiralp, T., Ademoglu, A., Istefanopulos, Y., Basar-Eroglu, C., and Basar, E., Wavelet analysis of oddball P300. Int. J. Psychophysiol. 39:221-227, 2001.
18. Teixeira, A. R., Tome, A. M., Stadlthanner, K., and Lang, E. W., KPCA denoising and the pre-image problem revisited. Digit. Signal Process. Rev. Journal. 18(4):568-580, 2008.
19. Kocsor, A., and Toth, L., Kernel-based feature extraction with a speech technology application. IEEE Trans. Signal Process. 52(8):2250-2263, 2004.
20. Xu, Y., Zhang, D., Song, F. X., Yang, J. Y., Jing, Z., and Li, M., A method for speeding up feature extraction based on KPCA. Neurocomputing. 70:1056-1061, 2007.
21. Ubeyli, E. D., Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents. Digit Signal Process. 38(4):646-656, 2008.
22. Daubechies, I., Ten lectures on wavelets. CBMS-NSF regional series in applied mathematics. SIAM, Philadelphia, PA, 1992.
23. Vapnik, V. N., The nature of statistical learning theory. Springer, New York, 1995.
24. Scholkopf, B., Smola, A., and Muller, K. R., Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5):1299-1319, 1998.
25. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf, B., An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2):181-201, 2001.
26. Rosenfeld, J. P., Ellwanger, J. W., Nolan, K., Wu, S., Bermann, R. G., and Sweet, J., P300 Scalp amplitude distribution as an index of deception in a simulated cognitive deficit model. Int. J. Psychophysiol. 33:3-19, 1999.
27. Rosenfeld, J. P., Biroschak, J. R., and Furedy, J. J., P300-based detection of concealed autobiographical versus incidentally acquired information in target and non-target paradigms. Int. J. Psychophysiol. 60:251-259, 2006.
28. Lefebvre, C. D., Marchand, Y., Smith, S. M., and Connolly, J. F., Use of event-related brain potentials (ERPs) to assess eyewitness accuracy and deception. Int. J. Psychophysiol. 73:218-255, 2009.
29. Karakas, S., Erzengin, O. U., and Basar, E., The genesis of human event-related responses explained through the theory of oscillatory neural assemblies. Neurosci. Lett. 285:45-48, 2000.
30. Klimesch, W., Doppelmayr, M., Schwaiger, J., Winkler, T., and Gruber, W., Theta oscillations and the ERP old/new effect: independent phenomena? Clin. Neurophysiol. 111:781-793, 2000.
31. Pang, C. C. C., Upton, A. R. M., Shine, G., and Kamath, M. V., A comparison of algorithms for detection of spikes in the electroencephalogram. IEEE Trans. Biomed. Eng. 50(4):521-526, 2003.
32. Ambach, W., Bursch, S., Stark, R., and Vaitl, D., A Concealed Information Test with multimodal measurement. Int. J. Psychophysiol. 75:258-267, 2010.
33. Verschuere, B., Crombez, G., De Clercq, A., and Koster, E. H., Autonomic and behavioral responding to concealed information: differentiating orienting and defensive response. Psychophysiology 41:461-466, 2004.
34. Gronau, N., Ben-Shakhar, G., and Cohen, A., Behavioral and physiological measures in the detection of concealed information. J. Appl. Psychol. 90:147-158, 2005.
35. Johnson, R. J., Barnhardt, J., and Zhu, J., The deceptive response: effects of response conflict and strategic monitoring on the late positive component and episodic memory-related brain activity. Biol. Psychol. 64:217-253, 2003.
36. Demiralp, T., and Ademoglu, A., Decomposition of event-related brain potentials into multiple functional components using wavelet transform. Clin. Electroencephalogr. 32:122-138, 2001.
37. Muller, K., Anderson, C. W., and Birch, G. E., Linear and nonlinear methods for brain computer inter faces. IEEE Trans. Neural Syst. Rehabil. Eng. 11:165-169, 2003.
38. Begg, R. K., and Kamruzzaman, J., A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J. Biomech. 38:401-408, 2005.