loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hamid Bagherzadeh Rafsanjani 1 ; Mozafar Iqbal 1 ; Morteza Zabihi 2 and Hideaki Touyama 3

Affiliations: 1 Islamic Azad University, Iran, Islamic Republic of ; 2 Tampere University of Technology, Finland ; 3 Toyama Prefectural University, Japan

ISBN: 978-989-8425-89-8

ISSN: 2184-4305

Keyword(s): EEG, Biometry, P300, Neural Network, Support Vector Machine.

Related Ontology Subjects/Areas/Topics: Applications ; Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Telecommunications ; Theory and Methods

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 m achine has more sustainable results as a classifier for EEG signals that contain P300 occurrence. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.231.21.83

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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, ISSN 2184-4305, pages 374-380. DOI: 10.5220/0003769903740380

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.