BIOMETRY BASED ON EEG SIGNALS USING NEURAL NETWORK AND SUPPORT VECTOR MACHINE

Hamid Bagherzadeh Rafsanjani, Mozafar Iqbal, Morteza Zabihi, Hideaki Touyama

Abstract

The use of EEG as a unique character to identify individuals has been considered in recent years. Biometric systems are generally operated into Identification mode and Verification mode. In this paper the feasibility of the personal recognition in verification mode were investigated, by using EEG signals based on P300, and also, the people’s identifying quality, in identification mode and especially in single trial, was improved with Neural Network (NN) and Support Vector Machine (SVM) as classifier. Nine different pictures have been shown to five participants randomly; before the test was examined, each subject had already chosen one or some pictures in order to P300 occurrence took place in examination. Results in the single trial were increased from 56.2\% in the previous study, to 75\% and 81.4\% by using SVM and NN, respectively. Meanwhile in a maximum state, 100% correctly classified was performed by only 5 times averaging of EEG. Also it was observed that using support vector machine has more sustainable results as a classifier for EEG signals that contain P300 occurrence.

References

  1. Berger, H., 1938. Das Elektrenkephalogramm des Menschen, Nova Acta Leopoldina 6. pp 173-309.
  2. Bertillon, A., 1896. Signaletic Instructions including the theory and practice of Anthropometrical Identification. The Werner Company.
  3. Burghardt, B., 2002. Inside iris recognition, Master's thesis. University of Bristol.
  4. Im, S., Park, H., Kim, Y., Han, S., Kim, S., Kang, C., 2001. An Biometric Identification System by extracting Hand Vein Patterns, Journal of the Korean Physical Society 38, pp. 268-272.
  5. Lindsay, I. & Smith, (2002). A tutorial on principal components analysis. <http://kybele.psych.cornell.edu /edelman/Psych-465Spring-2003/PCA-tutorial>.
  6. Palaniappan, R., 2004. Method of Identifying Individuals Using VEP Signals and Neural Network. In: IEEE Sci. Meas. Technol. pp. 16-20
  7. Palaniappan, R., Mandic, D.P., 2005. Energy of Brain Potentials Evoked During Visual Stimulus: A New Biometric. In: Int'l Conf. Artificial Neural Network. pp. 735-740.
  8. Poulos, M., Rangoussi, M., Chrissikopoulos, V., Evangelou, A., 1999 Person Identification Based on Parametric Processing of the EEG. In: IEEE Int'l Conf. on Electronics Circuits and Systems. Vol. 1, pp. 283-286.
  9. Poulos, M., Rangoussi, M., Chrissikopoulos, V., Evangelou, A., 1999. Parametric Person Identification from the EEG Using Computational Geometry. In IEEE Int'l Conf. on Electronics Circuits and Systems. Vol. 2, pp. 1005-1008.
  10. Pierre, M., Nicolas, O., 2010. A Precursor in the History of Fingermark Detection and Their Potential Use for Identifying Their Source (1863), Journal of forensic identification 60, pp. 129-134.
  11. Tangkraingkij, P., Lursinsap, C., Sanguansintukul, S., Desudchit, T., 2010. Personal Identification by EEG Using ICA and Neural Network. Lecture Notes in Computer Science 6018. Springer Berlin / Heidelberg. pp. 419-430, 2010.
  12. Touyama, H., Hirose, M. 2008. The Use of Photo Retrieval for EEG-Based Personal Identification. Lecture Notes in Computer Science 5068. SpringerVerlag Berlin /Heidelberg. pp. 276-283
  13. Vogol, F., 1970. The genetic basis of the normal human EEG, Humangenetik 10. pp.91-114.
  14. Woodward, J. D., Nicholas, Jr., Orlans, M., Higgins, P. T., 2003, Biometrics. McGraw Hill Osborne. New York.
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Paper Citation


in Harvard Style

Bagherzadeh Rafsanjani H., Iqbal M., Zabihi M. and Touyama H. (2012). BIOMETRY BASED ON EEG SIGNALS USING NEURAL NETWORK AND SUPPORT VECTOR MACHINE . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 374-380. DOI: 10.5220/0003769903740380


in Bibtex Style

@conference{biosignals12,
author={Hamid Bagherzadeh Rafsanjani and Mozafar Iqbal and Morteza Zabihi and Hideaki Touyama},
title={BIOMETRY BASED ON EEG SIGNALS USING NEURAL NETWORK AND SUPPORT VECTOR MACHINE},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={374-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003769903740380},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - BIOMETRY BASED ON EEG SIGNALS USING NEURAL NETWORK AND SUPPORT VECTOR MACHINE
SN - 978-989-8425-89-8
AU - Bagherzadeh Rafsanjani H.
AU - Iqbal M.
AU - Zabihi M.
AU - Touyama H.
PY - 2012
SP - 374
EP - 380
DO - 10.5220/0003769903740380