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http://dx.doi.org/10.9718/JBER.2018.39.1.43

Development of a Biometric Authentication System Based on Electroencephalography  

Choi, Ga-Young (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Kim, Eun-Ji (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Kang, Ye-Na (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Park, Su-Bin (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Park, Su-Jin (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Choi, Soo-In (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Hwang, Han-Jeong (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of Biomedical Engineering Research / v.39, no.1, 2018 , pp. 43-47 More about this Journal
Abstract
Traditional electroencephalography (EEG)-based authentication systems generally use external stimuli that require user attention and relatively long time for authentication. The aim of this study is to investigate the feasibility of biometric authentication based on EEG without using any external stimuli. Seventeen subjects took part in the experiment and their EEGs were measured while repetitively closing and opening their eyes. For identifying each subject, we calculated inter- and intra-subject cross-correlation using changes in alpha activity (8-13 Hz) during eyes closed as compared to eyes open. In order to optimize the number of recording electrodes, we calculated authentication accuracy by progressively reducing the number of electrodes used in the analysis. Significant increase in alpha activity was observed for all subjects during eyes closed, focusing on occipital areas, and spatial patterns of changed alpha activity were considerably different between the subjects. A mean authentication accuracy of 92.45% was obtained, which was retained over 75% when using only 8 electrodes placed around occipital areas. Our results could demonstrate the feasibility of the proposed novel authentication method based on resting state EEGs.
Keywords
Electroencephalography(EEG); Biometric authentication; Resting state; Alpha band;
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