Authentication System by Human Brainwaves Using Machine Learning and Artificial Intelligence

Hu, Zhengbing and Buriachok, Volodymyr and TajDini, Mahyar and Sokolov, V. Y. (2021) Authentication System by Human Brainwaves Using Machine Learning and Artificial Intelligence Part of the Lecture Notes on Data Engineering and Communications Technologies, 83. pp. 374-388. ISSN 2194-5365

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Abstract

Authentication and authorization are an indispensable piece of security in computer-based frameworks. As an option for biometrics, electroencephalography (EEG) authentication (authorization) presents focal points contrasted with other biological qualities. Brainwaves are difficult to reproduce, and diverse mental undertakings produce various brainwaves. This examination researches the parts of execution and time-invariance of the EEG-based confirmation. Two arrangements of trials are done to record EEG of various people. We actualize the utilization of artificial intelligence (AI), for example, support vector machine (SVM) and deep neural network (DNN) to characterize EEG of subjects. The correlation between EEG highlights, anodes position, and a mental errand is made. We accomplish more than 90% order exactness utilizing three kinds of highlights from four electrodes. Information from prior meetings is utilized as AI preparing information and information from later meeting are grouped. We discovered that characterization precision diminishes after some time, and inactive undertakings perform in a way that is better than dynamic errands.

Item Type: Article
Additional Information: EID: 2-s2.0-85111924795 DOI: 10.1007/978-3-030-80472-5_31
Uncontrolled Keywords: Human brainwave authentication;Biometric authentication; Machine learning authentication; Electroencephalography; EEG; Deep neural network; DNN; Support vector machine; SVM; Keras Neural Network; KNN
Subjects: Статті у наукометричних базах > Scopus
Divisions: Факультети > Факультет інформаційних технологій та управління > Кафедра інформаційної та кібернетичної безпеки
Depositing User: Volodymyr Sokolov
Date Deposited: 17 Aug 2021 12:04
Last Modified: 17 Aug 2021 12:04
URI: https://elibrary.kubg.edu.ua/id/eprint/36975

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