CANDARW2022_388.pdf 353 KB
Nakanishi, Isao Faculty of Engineering Tottori University Researchers DB KAKEN
Ishikawa, Yuta School of Engineering Tottori University
Mukai, Kotaro Graduate School of Sustainability Sciences Tottori University
evoked brain wave
reduction of classifiers
statistical values of brain waves
support vector machine
In user verification using electroencephalograms (EEGs) evoked by ultrasound, an error rate of 0% was achieved. However, to achieve this, the classifiers for the number of features multiplied by the number of electrodes must be learned. Therefore, reducing the number of classifiers is crucial and must be achieved. This study confirmed that the random selection of features and electrodes facilitates further reduction in the number of classifiers. Random selection is equivalent to evenly selecting electrodes for each feature and electrode position. Consequently, the effectiveness of even selection was statistically confirmed. Furthermore, even selection resulted in the fusion of uncorrelated features. Thus, four statistical values of an EEG were introduced, and the effectiveness of fusing uncorrelated (independent) features was confirmed.
Proceedings of 2022 tenth International Symposium on Computing and Networking Workshops(CANDARW)
© 2022 IEEE.
I Nakanishi, Y Ishikawa, K Mukai. Correlation Analysis of Features for Fusing in User Verification Using EEG Evoked by Ultrasound. Proceedings of 2022 tenth International Symposium on Computing and Networking Workshops(CANDARW). 2022, 388-391.
Faculty of Engineering/Graduate School of Engineering