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Authors
Miyake, Takahiro Graduate School of Sustainability Sciences Tottori University
Kinjo, Nozomu Graduate School of Sustainability Sciences Tottori University
Nakanishi, Isao Faculty of Engineering, Tottori University Researchers DB KAKEN
Keywords
Biometric authentication
Invisible stimulation Machine Learning
Electroencephalogram (EEG)
Wavelet Transform
Abstract
In this study, we propose the authentication of individuals using electroencephalograms (EEGs) evoked by the application of invisible visual stimuli. In our previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and applied it to extract features, including time information, to enable more accurate discrimination between individuals. An equal error rate (EER) of 9.4 % was achieved using Euclidean distance matching. In this paper, we introduce a machine learning-based approach in order to further improve the verification performance. An EER of 8.1 % is achieved by the proposed method after training the constituent neural networks using ensemble learning with 30 networks.
Content Type
Conference Paper
Journal Title
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
Current Journal Title
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
Published Date
2020-11
Text Version
Author
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Citation
T. Miyake, N. Kinjo, I.Nakanishi. Wavelet Transform and Machine Learning-Based Biometric Authentication Using EEG Evoked by Invisible Visual Stimuli. Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020). 2020.
Department
Faculty of Engineering/Graduate School of Engineering
Language
English