Personal identification by EEG using ICA and neural network

Authors:
Preecha Tangkraingkij;Chidchanok Lursinsap;Siripun Sanguansintukul;Tayard Desudchit
Affiliations:
Advanced Virtual and Intelligent Computing (AVIC) Research Center, Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;Advanced Virtual and Intelligent Computing (AVIC) Research Center, Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;Advanced Virtual and Intelligent Computing (AVIC) Research Center, Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;The Chulalongkorn Comprehensive Epilepsy Program (CCEP), Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
Venue:
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Year:
2010

Citing 3
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Abstract

The problem of identifying a person using biometric data is interesting. In this paper, the uniqueness of EEG signals of individuals is used to determine personal identity. EEG signals can be measured from different locations, but too many signals can degrade the recognition speed and accuracy. A practical technique combining Independent Component Analysis (ICA) for signal cleaning and a supervised neural network for classifying signals is proposed. From 16 EEG different signal locations, four truly relevant locations F7, C3, P3, and O1 were selected. This selection can identify a group of 20 persons with high accuracy.