SSCI2022.pdf 643 KB
Nakanishi, Isao Faculty of Engineering Tottori Univerisity Researchers DB KAKEN
Mukai, Kotaro Faculty of Sustainable Sciences Tottori University
evoked brain wave
mutual feature between electrodes
reduction of SVM models
In user management, to realize continuous user authentication, we study the use of an electroencephalogram (EEG) evoked by ultrasound as biometrics. In previous studies, using a spectrum and four nonlinear quantities in EEG as individual features and a support vector machine (SVM) as a verification method achieved an equal error rate (EER) of 0 %. However, it required a large number of SVM models, wherein considerable amount of computation regarding learning was consumed. In this study, we introduce a mutual feature between electrodes and confirm its effectiveness in achieving EER = 0 % with a smaller number of SVM models.
Proceedings of 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022)
(C) 2022 IEEE
I. Nakanishi and K. Mukai, "Introduction of a Mutual Feature between Electrodes into Support Vector Machine Based Person Verification Using Evoked Electroencephalogram by Ultrasound," 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 171-177, doi: 10.1109/SSCI51031.2022.10022272.
Faculty of Engineering/Graduate School of Engineering