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논문 기본 정보

자료유형
학술저널
저자정보
HyeonBin Lee (Kwangwoon University) Gwangho Kim (Kwangwoon University) JuHyeong Kim (Kwangwoon University) YoungShin Kang (Kwangwoon University) Cheolsoo Park (Kwangwoon University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.10 No.3
발행연도
2021.6
수록면
199 - 203 (5page)
DOI
10.5573/IEIESPC.2021.10.3.199

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초록· 키워드

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An electroencephalogram (EEG) is an electrical recording from the scalp when neurons in the brain are active. EEG signals have been studied for authentication because they are difficult to falsify and can distinguish individuals. On the other hand, EEG is nonstationary, and its patterns vary slightly. The authentication model was trained day-to-day to overcome the nonstationarity of EEG. EEG signals were measured on two-channel frontal electrodes for five days from 10 subjects in their resting states. Convolutional neural networks were designed for an EEG-based authentication system, and the model was optimized using a Bayesian optimization method. The proposed neural network model was trained with the EEG data from the first to the fourth day and tested using the fifth-day data, which yielded a mean accuracy of 93.23%, precision of 71.31%, and recall of 57.65%. The incremental learning of the EEG signals day-to-day improves the authentication performance, including various EEG patterns in the model.

목차

Abstract
1. Introduction
2. Background
3. Materials and Methods
4. Results
5. Conclusion
References

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