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dc.contributor.authorLee, Jaehyung-
dc.contributor.authorCha, Kabmun-
dc.contributor.authorKim, Hyungmin-
dc.contributor.authorChoi, Junhyuk-
dc.contributor.authorKim, Choonghyun-
dc.contributor.authorLee, Songjoo-
dc.date.accessioned2024-01-19T10:37:13Z-
dc.date.available2024-01-19T10:37:13Z-
dc.date.created2022-02-28-
dc.date.issued2019-02-19-
dc.identifier.issn2572-7680-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/114305-
dc.description.abstractThe goal of this study was to compare decoding accuracy of left and right movement intention from electroencephalography (EEG) using three different types of paradigms: Motor Imagery (MI), Selective Attention (SA), and Hybrid task (HY)). Specifically, SA and HY are the Steady-State Somatosensory Evoked potential (SSSEP) paradigms which elicit brain responses to tactile stimulation. One subject participated in two sessions (Screening and Study session). In the screening session, resonance-like frequency of the subject was found at each hand while sitting on a chair. In the study session, the subject was asked to imagine either left of right hand open-close movement (MI task), to give selective attention to the vibrotactile stimulation (SA task), and to perform combined MI and SA task (HY) according to a randomly assigned directional cue. The accuracies of 3 paradigms were MI-left 65.8%, MI-right 69.2% (mean: 67.5%), SA-left 76.6%, SA-right 84.0% (mean: 80.3%) and HY-left 93.8%, HY-right 95.9% (mean: 94.9%). The method and results of the current study could be a basis for controlling the left and right movement direction of an exoskeleton robot using EEG.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleHybrid MI-SSSEP Paradigm for classifying left and right movement toward BCI for exoskeleton control-
dc.typeConference-
dc.identifier.doi10.1109/IWW-BCI.2019.8737319-
dc.description.journalClass1-
dc.identifier.bibliographicCitation7th International Winter Conference on Brain-Computer Interface (BCI), pp.196 - 198-
dc.citation.title7th International Winter Conference on Brain-Computer Interface (BCI)-
dc.citation.startPage196-
dc.citation.endPage198-
dc.citation.conferencePlaceUS-
dc.citation.conferencePlacehigh1 resort, Korea-
dc.citation.conferenceDate2019-02-18-
dc.relation.isPartOf2019 7TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI)-
dc.identifier.wosid000492868700045-
dc.identifier.scopusid2-s2.0-85068341921-
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KIST Conference Paper > 2019
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