Lie Detection Based EEG-P300 Signal Classified by ANFIS Method

Authors

  • Arjon Turnip Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, Indonesia.
  • M. Faizal Amri Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, Indonesia.
  • M. Agung Suhendra Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, Indonesia.
  • Dwi Esti Kusumandari Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, Indonesia.

Keywords:

Lie Detection, ERP, EEG-P300, ANFIS, Feature Extraction, Classification,

Abstract

In this paper, the differences in brain signal activity (EEG-P300 component) which detects whether a person is telling the truth or lying is explored. Brain signal activity is monitored when they are first respond to a given experiment stimulus. In the experiment, twelve subjects whose age are around 19 ± 1 years old were involved. In the signal processing, the recorded brain signals were filtered and extracted using bandpass filter and independent component analysis, respectively. Furthermore, the extracted signals were classified with adaptive neuro-fuzzy inference system method. The results show that a huge spike of the EEG-P300 amplitude on a lying subject correspond to the appeared stimuli is achieved. The findings of these experiments have been promising in testing the validity of using an EEG-P300 as a lie detector.

References

Rosenfeld J.P., Shue E. and Singer E. 2007. Single Versus Multiple Probe Blocks Of P300-Based Concealed Information Tests For Selfreferring Versus Incidentally Obtained Information. Biological Psychology 74:396-404,

Bhutta M. R. and Hong K. 2015. Classifiation Performance Analysis Of Combined Fnrs-Polygraph System Using Different Temporal Windows. 2015 15th International Conference on Control, Automation and Systems no. Iccas.1795–1800

Gao J. and Rao N. 2012. A New Lie Detection Method based on Small-number of P300 Responses.

Abootalebi V., Moradi M. H., and Khalilzadeh M. A. 2009. A new approach for EEG feature extraction in P300-based lie detection, Comput. Methods Programs Biomed. 94(1): 48–57,.

Gao J., Yan X., Sun J., and Zheng C. 2010. Denoised P300 and machine learning-based concealed information test method. Comput. Methods Programs Biomed., 104(3):410–417

Sellers E. W., Krusienski D. J., Mcfarland D. J., Vaughan T. M., and

Wolpaw J. R. 2006. A P300 event-related potential brain – computer

interface ( BCI ): The effects of matrix size and inter stimulus interval on performance. 73: 242–252

Turnip A., Hong K., and Jeong M. 2011. Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis.1–20,.

Turnip A. and Hong K. S. 2012. Classifying mental activities from EEG-P300 signals using adaptive neural network. Int. J. Innov. Comp. Inf. Control . 8(7)

Turnip A., Hutagalung S. S., Pardede J., and, Soetraprawata D. 2013. P300 detection using multilayer neural networks based adaptive feature extraction method. International Journal of Brain and Cognitive Sciences. 2(5):63-75,

Turnip A. and Siahaan M. 2014. Adaptive Principal Component Analysis based Recursive Least Squares for Artifact Removal of EEG Signals. Advanced Science Letters. 20(10-12):2034-2037

Krusienski D. J., Sellers E. W., Bayoudh S., Mcfarland D. J., Vaughan T. M., and Wolpaw J. R. 2006. A comparison of classification techniques for the P300 Speller. 299.

Turnip A. and Siahaan M. 2013. P300 Detection Using Nonlinear Independent Component Analysis. 1:104–109,.

Dehzangi O., Jafari R., and Zou Y. 2013. Simultaneous Classification of Motor Imagery and SSVEP EEG Signals. 6–8,.

Turnip A. and Hutagalung S. S. 2013. P300 Detection based on Extraction and Classification in online BCI. 1.

Turnip A. and Kusumandari D. E. 2014. Improvement of BCI performance through nonlinear independent component analisis extraction. Journal of Computer. 9(3):688-695.

Turnip A., Haryadi, Soetraprawata D., and Kusumandari D. E. 2014. Comparison of Extraction Techniques for the rapid EEG-P300 Signals. Advanced Science Letters. 20(1):80-85

Tahir M., Mitsuhashi W., and James C. J. 2012. Employing spatially constrained ICA and wavelet denoising , for automatic removal of artifacts from multichannel EEG data. Signal Processing, 92(2):401–416.

Liao J., Shih W., Huang K., and Fang W. 2013. An Online Recursive ICA Based Real-time Multi- channel EEG System on Chip Design with Automatic Eye Blink Artifact Rejection. 1–3.

Ayoubian S., Member S., Qazi S., and Member S. 2006. Wavelet Filtering of the P300 Component in Event-Related Potentials. 1719–1722.

Patil S. S. 2012. Quality advancement of EEG by wavelet denoising for biomedical analysis. 1–6.

Abbasimehr H. 2011. A Neuro-Fuzzy Classifier for Customer Churn Prediction. 19(8):35–41.

Ramírez-cortes J. M., Alarcon-aquino V., Rosas-cholula G., Gomez-gil P., and Escamilla-ambrosio J. 2010. P-300 Rhythm Detection Using ANFIS Algorithm and Wavelet Feature Extraction in EEG Signals. I,.

Turnip A., Simbolon A. I., and Amri M. F. 2015. Utilization of EEGSSVEP method and ANFIS Classifier for Controlling Electronic

Wheelchair. 43–146.

Azeez D., Alauddin M., Ali M., Gan K., and Saiboon I. 2013. Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department. 1–10.

Downloads

Published

2017-04-01

How to Cite

Turnip, A., Amri, M. F., Suhendra, M. A., & Kusumandari, D. E. (2017). Lie Detection Based EEG-P300 Signal Classified by ANFIS Method. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-5), 107–110. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1845

Issue

Vol. 9 No. 1-5: Smart City Technology in Communication and Computer Engineering III

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.