Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Evaluation of a Compact Hybrid Brain-Computer Interface System

Full metadata record
DC Field Value Language
dc.contributor.authorShin, Jaeyoung-
dc.contributor.authorMueller, Klaus-Robert-
dc.contributor.authorSchmitz, Christoph H.-
dc.contributor.authorKim, Do-Won-
dc.contributor.authorHwang, Han-Jeong-
dc.date.accessioned2021-12-21T19:40:47Z-
dc.date.available2021-12-21T19:40:47Z-
dc.date.created2021-08-30-
dc.date.issued2017-
dc.identifier.issn2314-6133-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/132442-
dc.description.abstractWe realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherHINDAWI LTD-
dc.subjectMOTOR IMAGERY-
dc.subjectEEG-
dc.subjectNIRS-
dc.subjectBCI-
dc.subjectREHABILITATION-
dc.subjectCLASSIFICATION-
dc.subjectSTATE-
dc.subjectPERFORMANCE-
dc.subjectARTIFACTS-
dc.subjectFNIRS-
dc.titleEvaluation of a Compact Hybrid Brain-Computer Interface System-
dc.typeArticle-
dc.contributor.affiliatedAuthorMueller, Klaus-Robert-
dc.contributor.affiliatedAuthorHwang, Han-Jeong-
dc.identifier.doi10.1155/2017/6820482-
dc.identifier.scopusid2-s2.0-85015871799-
dc.identifier.wosid000398688600001-
dc.identifier.bibliographicCitationBIOMED RESEARCH INTERNATIONAL, v.2017-
dc.relation.isPartOfBIOMED RESEARCH INTERNATIONAL-
dc.citation.titleBIOMED RESEARCH INTERNATIONAL-
dc.citation.volume2017-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaResearch & Experimental Medicine-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryMedicine, Research & Experimental-
dc.subject.keywordPlusMOTOR IMAGERY-
dc.subject.keywordPlusEEG-
dc.subject.keywordPlusNIRS-
dc.subject.keywordPlusBCI-
dc.subject.keywordPlusREHABILITATION-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusSTATE-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusARTIFACTS-
dc.subject.keywordPlusFNIRS-
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles
Graduate School > Department of Electronics and Information Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE