Untitled Document
   
You are from : ( )  
     
Untitled Document
Untitled Document
 

International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

The brain wave activity while a person is either telling the truth or being deceptive is investigated. A subject brain wave activities based EEG-P300 component is monitored while they first respond truthfully and then falsely to questions in regards to a mock theft scenario. Twelve subjects whose age are around 20 ± 1 years old were involved in the experiment. For extraction and classificaton, an independent component analysis and adaptive neuro-fuzzy inference systems were applied. The gathered data were then divided into training and test data to produce several models. The results show that a larger spike in the P300 component when the subject was instructed to conceal which watch they had chosen. The findings of these experiments have been promising in testing the validity of using an EEG in deception detection.

Keywords :

: ERP, EEG-P300, ANFIS method, FeatureExtraction .

References :

  1. J. T. Cacioppo, L. G. Tassinary, and G. Berntson, Handbook of Psychophysiology, 3rd ed. Cambiage University Press, New York, 2007.
  2. I. G. A. Gunadi and A. Harjoko, "Telaah Metode-metode Pendeteksi Kebohongan," Indonesian Journal of Computing and Cybernetics Systems, vol. 6, pp. 35-46, July 2012.
  3. V. Abootalebi, M. H. Moradi, and M. A. Khalilzadeh, "A new approach for EEG feature extraction in P300-based lie detection," Computer Methods and Programs in Biomedicine, vol. 94, pp. 48-57, 4// 2009.
  4. A. Subasi and M. Ismail Gursoy, "EEG signal classification using PCA, ICA, LDA and support vector machines," Expert Systems with Applications, vol. 37, pp. 8659-8666, 2010.
  5. A. Turnip, K.-S. Hong, and M.-Y. Jeong, "Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis," BioMedical Engineering OnLine, 2011.
  6. B. Luzheng, F. Xin-an, and L. Yili, "EEG-Based Brain-Controlled Mobile Robots: A Survey," Human-Machine Systems, IEEE Transactions on, vol. 43, pp. 161-176, 2013.
  7. A. Turnip and K. S. Hong, “Classifying mental activities from EEGP300 signals using adaptive neural network,” Int. J. Innov. Comp. Inf. Control, vol. 8(7), 2012.
  8. A. Turnip, K. S. Hong, and M. Y. Jeong, “Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis,” BioMedical Engineering OnLine,” vol. 10(83), 2011.
  9. A. Turnip, S. S. Hutagalung, J. Pardede, and, D. Soetraprawata, "P300 detection using multilayer neural networks based adaptive feature extraction method", International Journal of Brain and Cognitive Sciences, vol. 2, no. 5, pp. 63-75, 2013.
  10. A. Turnip and M. Siahaan, “Adaptive Principal Component Analysis based Recursive Least Squares for Artifact Removal of EEG Signals,” Advanced Science Letters, vol. 20, no.10-12, pp. 20342037(4), October 2014.
  11. A. Turnip and D. E. Kusumandari, “Improvement of BCI performance through nonlinear independent component analisis extraction,” Journal of Computer, vol. 9, no. 3, pp. 688-695, March 2014.
  12. A. Turnip, Haryadi, D. Soetraprawata, and D. E. Kusumandari, A “Comparison of Extraction Techniques for the rapid EEG-P300 Signals,” Advanced Science Letters, vol. 20, no. 1, pp. 80- 85(6), January, 2014.
  13. J. Gao, X. Yan, J. Sun, and C. Zheng, "Denoised P300 and machine learning-based concealed information test method," Computer Methods and Programs in Biomedicine, vol. 104, pp. 410-417, 12// 2011.
  14. M. T. Akhtar, W. Mitsuhashi, and C. J. James, "Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data," Signal Processing, vol. 92, pp. 401-416, 2// 2012.
  15. S. Kim, Y. Kim, K. Sim, and H. Jeon, “On Developing an Adaptive Neural-Fuzzy Control System,” Proceedings of IEEE/RSJ Conference on Intelligent Robots and Systems, Yokohama, Japan, pp. 950-957, 1993.
  16. P. Sajda, A. Gerson, R. Müller, B. Blankertz, and L. Parra, “A Data Analysis Competition to Evaluate Machine Learning Algorithms for Use in Brain-Computer Interfaces,” Computer Journal of IEEE Trans Neural Systehms, vol. 11, no. 2, pp. 184-185, 2003.
  17. B. Dixon. “Applicability of neuro-fyzzy techniques in predicting ground-wateer vunerability: a GIS-based sensitivity analysis”. Journal of Hydrology. 309. 2005. pp.17-38.

Untitled Document
     
Untitled Document
   
  Copyright © 2014  IJITCS.  All rights reserved. IISRC® is a registered trademark of IJITCS Properties.