文章基本信息

标题：A Biometric System Based on Single-channel EEG Recording in One-second
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作者：Shaimaa Hagras ; Reham R. Mostafa ; Ahmed Abou elfetouh 等
期刊名称：International Journal of Intelligent Systems and Applications
印刷版ISSN：2074-904X
电子版ISSN：2074-9058
出版年度：2020
卷号：12
期号：5
页码：28-40
DOI：10.5815/ijisa.2020.05.03
出版社：MECS Publisher
摘要：In recent years, there are great research interests in using the Electroencephalogram (EEG) signals in biometrics applications. The strength of EEG signals as a biometric comes from its major fraud prevention capability. However, EEG signals are so sensitive, and many factors affect its usage as a biometric; two of these factors are the number of channels, and the required time for acquiring the signal; these factors affect the convenience and practicality. This study proposes a novel approach for EEG-based biometrics that optimizes the channels of acquiring data to only one channel. And the time to only one second. The results are compared against five commonly used classifiers named: KNN, Random Forest (RF), Support Vector Machine (SVM), Decision Tables (DT), and Naïve Bayes (NB). We test the approach on the public Texas data repository. The results prove the constancy of the approach for the eight minutes. The best result of the eyes-closed scenario is Average True Positive Rate (TPR) 99.1% and 98.2% for the eyes-opened. And it reaches 100% for multiple subjects.
关键词：Biometrics;Electroencephalogram;Hjorth parameters;K-Nearest Neighbor;Naïve Bayes;Random Forest;and Support Vector Machine