There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.conferencePlace | KO | - |
dc.citation.conferencePlace | Jeju Island | - |
dc.citation.endPage | 1119 | - |
dc.citation.startPage | 1115 | - |
dc.citation.title | 17th International Conference on Control, Automation and Systems, ICCAS 2017 | - |
dc.contributor.author | Lee, Chungho | - |
dc.contributor.author | Kang, Jae-Hwan | - |
dc.contributor.author | Kim, Sung-Phil | - |
dc.date.accessioned | 2023-12-19T18:08:24Z | - |
dc.date.available | 2023-12-19T18:08:24Z | - |
dc.date.created | 2017-12-11 | - |
dc.date.issued | 2017-10-19 | - |
dc.description.abstract | Biometric technology based on electroencephalography (EEG) identifies individuals by using personal characteristics in human brainwaves. This study aims to evaluate EEG features and channels for biometrics and to propose a methodology that selects the optimal features to discriminate individuals. Thirty healthy subjects participated in the study. While recording EEG signals from fourteen channels, subjects were asked to relax and keep eyes closed for two minutes. To evaluate intra-individual variability, we recorded EEG ten times for each subject across different days to reduce any within-day effect. From each channel, eight EEG features were calculated including alpha/beta power ratio, alpha/theta power ratio, beta/theta power ratio, median frequency, PSD entropy, permutation entropy, sample entropy, and maximum Lyapunov exponents. These features were evaluated by three feature selection algorithms based on Fisher score, reliefF, and information gain, respectively. A linear discriminant analysis (LDA) classifier along with a leave-one-out cross validation method discriminated self against others using the selected features. The best feature set was found to be composed of 23 features highly ranked on Fisher scores, which yielded a 18.56% half total error rate. In addition, EEG channels located on occipital and right temporal areas appeared to most contribute to authenticate individuals. | - |
dc.identifier.bibliographicCitation | 17th International Conference on Control, Automation and Systems, ICCAS 2017, pp.1115 - 1119 | - |
dc.identifier.doi | 10.23919/ICCAS.2017.8204382 | - |
dc.identifier.issn | 1598-7833 | - |
dc.identifier.scopusid | 2-s2.0-85044452461 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/35252 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8204382 | - |
dc.language | 영어 | - |
dc.publisher | 17th International Conference on Control, Automation and Systems, ICCAS 2017 | - |
dc.title | Methods of Selecting Electroencephalographic Features for Personal Authentication | - |
dc.type | Conference Paper | - |
dc.date.conferenceDate | 2017-10-18 | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.