Advancing security measures: A brainwave-based biometric system for user identification and authentication
In contemporary organizational contexts, the imperative for robust user identification and authentication systems to safeguard assets is paramount. Conventional methods like passwords, secret codes, and personal identification numbers are prone to compromise and human error. This study explores the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UMP
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41828/1/Advancing%20Security%20Measures_%20%20A%20Brainwave%20Based%20Biometric.pdf http://umpir.ump.edu.my/id/eprint/41828/ https://doi.org/10.15282/mekatronika.v6i1.10185 https://doi.org/10.15282/mekatronika.v6i1.10185 |
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| Summary: | In contemporary organizational contexts, the imperative for robust user identification and authentication systems to safeguard assets is paramount. Conventional methods like passwords, secret codes, and personal identification numbers are prone to compromise and human error. This study explores the feasibility of utilizing human brainwaves, specifically Electroencephalogram (EEG) signals, as a biometric authentication system. Employing the Unicorn Hybrid Black EEG device for measurement and LabVIEW software for analysis, the research focuses on discerning EEG features pertinent to authentication. Through controlled activities encompassing imaginative (imagining singing a favorite song, imagining opening a locked door) and physical tasks (engaging in a mobile game, solving a Rubik's cube), the study elucidates the dominance of the EEG Theta band across varied cognitive and motor processes. Further analysis underscores the heightened power of the EEG Alpha band during relaxation phases and the prevalence of the EEG Beta band during heightened cognitive engagement. The classification of selected EEG features highlights the efficacy of utilizing Standard Deviation as a discriminative factor, achieving a commendable accuracy of 93.35% with a training-testing ratio of 80:20. This research underscores the potential of EEG-based authentication systems in fortifying organizational security protocols. |
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