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Development of a New Around-the-Ear Electroencephalography Device for Passive Brain–Computer Interface Applications

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dc.contributor.authorKim, Minsu-
dc.contributor.authorOh, Eunkyu-
dc.contributor.authorKim, Yoosung-
dc.contributor.authorKim, Seonho-
dc.contributor.authorPark, Dasom-
dc.contributor.authorKim, Junghwan-
dc.contributor.authorKim, Suhye-
dc.contributor.authorAhn, Hyunjin-
dc.contributor.authorIm, Chang Hwan-
dc.date.accessioned2025-11-27T07:30:42Z-
dc.date.available2025-11-27T07:30:42Z-
dc.date.issued2025-09-
dc.identifier.issn1530-437X-
dc.identifier.issn1558-1748-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/209396-
dc.description.abstractAs interest in passive brain–computer interface (pBCI) technology for everyday applications increases, the development of practical wearable electroencephalography (EEG) recording devices has become increasingly essential. Among the various form factors to implement wearable EEG systems, ear-EEG is frequently employed owning to its usefulness in everyday scenarios. In this study, a new wearable around-the-ear EEG recording device for pBCI applications was developed. The performance of the developed device was validated through two pBCI experiments. During the ear-EEG device design, an alpha attenuation test was conducted to determine the optimal location of a pair of EEG electrodes. The two practical pBCI applications tested in this study were the prediction of users’ preferences for short video clips and drowsiness detection during online learning. The experimental results showed an accuracy of 85.71% in terms of preference prediction and a success rate of 80% in terms of drowsiness detection, effectively demonstrating the practicality of the newly developed around-the-ear EEG device for daily life scenarios.-
dc.format.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.titleDevelopment of a New Around-the-Ear Electroencephalography Device for Passive Brain–Computer Interface Applications-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/JSEN.2025.3596292-
dc.identifier.scopusid2-s2.0-105013192438-
dc.identifier.wosid001615631600001-
dc.identifier.bibliographicCitationIEEE Sensors Journal, v.25, no.18, pp 34226 - 34235-
dc.citation.titleIEEE Sensors Journal-
dc.citation.volume25-
dc.citation.number18-
dc.citation.startPage34226-
dc.citation.endPage34235-
dc.type.docTypeaip-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusEVOKED-POTENTIALS-
dc.subject.keywordPlusEEG-
dc.subject.keywordPlusSLEEPINESS-
dc.subject.keywordPlusRESPONSES-
dc.subject.keywordPlusALPHA-
dc.subject.keywordAuthorDrowsiness detection-
dc.subject.keywordAuthorear electroencephalography (ear-EEG)-
dc.subject.keywordAuthorpassive brain–computer interface (pBCI)-
dc.subject.keywordAuthorpreference detection-
dc.subject.keywordAuthorwearable device-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/11123617-
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