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Data-driven user feedback: An improved neurofeedback strategy considering individual variability of EEG features

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dc.contributor.authorHan, Chang-Hee-
dc.contributor.authorIm, Chang-Hwan-
dc.date.accessioned2021-08-03T09:51:21Z-
dc.date.available2021-08-03T09:51:21Z-
dc.date.created2021-07-09-
dc.date.issued2014-06-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/44066-
dc.description.abstractThe aim of the present study was to develop a new neurofeedback strategy named the data-driven user feedback that considers individual variability of electroencephalography (EEG) features in order to make the users of the neurofeedback system experience wider range of feedbacks. Twenty healthy subjects performed a hidden catch paradigm, during which EEG signals were acquired from two prefrontal channels. From our experimental results, 72% increment in the number of valid (feedback) bins could be attained using the proposed strategy. ? 2014 IEEE.-
dc.language영어-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleData-driven user feedback: An improved neurofeedback strategy considering individual variability of EEG features-
dc.typeConference-
dc.contributor.affiliatedAuthorIm, Chang-Hwan-
dc.identifier.scopusid2-s2.0-84907420117-
dc.identifier.bibliographicCitation18th IEEE International Symposium on Consumer Electronics, ISCE 2014, pp.1 - 2-
dc.relation.isPartOf18th IEEE International Symposium on Consumer Electronics, ISCE 2014-
dc.relation.isPartOfProceedings of the International Symposium on Consumer Electronics, ISCE-
dc.citation.title18th IEEE International Symposium on Consumer Electronics, ISCE 2014-
dc.citation.startPage1-
dc.citation.endPage2-
dc.citation.conferencePlaceKO-
dc.citation.conferencePlaceJeju, Korea (South)-
dc.citation.conferenceDate2014-06-22-
dc.type.rimsCONF-
dc.description.journalClass1-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/6884526-
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