Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 장원두 | - |
dc.date.accessioned | 2017-11-28T02:19:31Z | - |
dc.date.available | 2017-11-28T02:19:31Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.citation | PHYSIOLOGICAL MEASUREMENT, v. 37, NO 3, Page. 401-417 | en_US |
dc.identifier.issn | 0967-3334 | - |
dc.identifier.issn | 1361-6579 | - |
dc.identifier.uri | http://iopscience.iop.org/article/10.1088/0967-3334/37/3/401/meta | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/31902 | - |
dc.description.abstract | Electroencephalogram (EEG) is easily contaminated by unwanted physiological artifacts, among which electrooculogram (EOG) artifacts due to eye blinking are known to be most dominant. The eye blink artifacts are reported to affect theta and alpha rhythms of frontal EEG signals, and hard to be accurately detected in an unsupervised way due to large individual variability. In this study, we propose a new method for detecting eye blink artifacts automatically in real time without using any labeled training data. The proposed method combined our previous method for detecting eye blink artifacts based on digital filters with an automatic thresholding algorithm. The proposed method was evaluated using EEG data acquired from 24 participants. Two conventional algorithms were implemented and their performances were compared with that of the proposed method. The main contributions of this study are (1) confirming that individual thresholding is necessary for artifact detection, (2) proposing a novel algorithm structure to detect blink artifacts in a real-time environment without any a priori knowledge, and (3) demonstrating that the length of training data can be minimized through the use of a real-time adaption procedure. | en_US |
dc.description.sponsorship | This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2014R1A2A1A11051796) and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2014R1A1A2A16052334). The authors declare that there is no conflict of interests regarding the publication of this paper. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IOP PUBLISHING LTD | en_US |
dc.subject | electroencephalogram (EEG) | en_US |
dc.subject | electrooculogram (EOG) | en_US |
dc.subject | ocular artifact | en_US |
dc.subject | eye blink | en_US |
dc.title | An Unsupervised Eye Blink Artifact Detection Method for Real-time Electroencephalogram Processing | en_US |
dc.type | Article | en_US |
dc.relation.no | 3 | - |
dc.relation.volume | 37 | - |
dc.identifier.doi | 10.1088/0967-3334/37/3/401 | - |
dc.relation.page | 401-417 | - |
dc.relation.journal | PHYSIOLOGICAL MEASUREMENT | - |
dc.contributor.googleauthor | Chang, Won-Du | - |
dc.contributor.googleauthor | Lim, Jeong-Hwan | - |
dc.contributor.googleauthor | Im, Chang-Hwan | - |
dc.relation.code | 2016002518 | - |
dc.sector.campus | S | - |
dc.sector.daehak | RESEARCH INSTITUTE[S] | - |
dc.sector.department | INSTITUTE OF BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | cross1279 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.