Work place: Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
E-mail: reham_2006@mans.edu.eg
Website:
Research Interests: Computer systems and computational processes, Information Systems, Data Structures and Algorithms, Information Theory, Algorithmic Information Theory
Biography
Reham R. Mostafa was born in Abu Dhabi, UAE in 1983. She received the B.S., M.S., Ph.D degrees in information systems from Mansoura University, Egypt in 2005, 2009 and 2014, respectively. Currently she is an associate professor at Information Systems Department, Faculty of Computers and Information, Mansoura University,Egypt.
By Shaimaa Hagras Reham R. Mostafa Ahmed Abou elfetouh
DOI: https://doi.org/10.5815/ijisa.2020.05.03, Pub. Date: 8 Oct. 2020
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.
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