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
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
machine has more sustainable results as a classifier for EEG signals that contain P300 occurrence.
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