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Authors
Nakanishi, Isao Faculty of Engineering, Tottori University Researchers DB KAKEN
Maruoka, Takehiro Graduate School of Sustainability Sciences, Tottori University
Keywords
biometrics
brain wave
EEG
ultrasound
evoked potential
multidimensional nonlinear feature
Abstract
Biometrics such as fingerprints and iris scans has been used in authentication. However, conventional biometrics is vulnerable to identity theft, especially in user-management systems. As a new biometrics without this vulnerability, brain waves have been a focus. In this paper, brain waves (electroencephalograms (EEGs)) were measured from ten experiment subjects. Individual features were extracted from the log power spectra of the EEGs using principal component analysis, and verification was achieved using a support vector machine. It was found that, for the proposed authentication method, the equal error rate (EER) for a single electrode was about 22–32%, and that, for a multiple electrodes, was 4.4% by using the majority decision rule. Furthermore, nonlinear features based on chaos analysis were introduced for feature extraction and then extended to multidimensional ones. By fusing the results of all electrodes when using the proposed multidimensional nonlinear features and the spectral feature, an EER of 0% was achieved. As a result, it was confirmed that individuals can be authenticated using induced brain waves when they are subjected to ultrasounds.
Publisher
MDPI
Content Type
Journal Article
ISSN
20799292
Journal Title
Electronics
Volume
9
Issue
1
Published Date
2020
Publisher-DOI
Text Version
Author
Rights
©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Citation
Isao Nakanishi, Takehiro Maruoka. Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features. Electronics. 2020, 9, 24.
Department
Faculty of Engineering/Graduate School of Engineering
Language
English