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

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

2012

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.

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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