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제스처와 EEG 신호를 이용한 감정인식 방법

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dc.contributor.author심귀보-
dc.contributor.author양현창-
dc.contributor.author김호덕-
dc.contributor.author정태민-
dc.date.available2019-07-24T05:56:27Z-
dc.date.issued2007-
dc.identifier.issn1976-5622-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/30392-
dc.description.abstractElectroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.-
dc.format.extent6-
dc.publisher제어·로봇·시스템학회-
dc.title제스처와 EEG 신호를 이용한 감정인식 방법-
dc.title.alternativeEmotion Recognition Method using Gestures and EEG Signals-
dc.typeArticle-
dc.identifier.bibliographicCitation제어.로봇.시스템학회 논문지, v.13, no.9, pp 832 - 837-
dc.identifier.kciidART001075159-
dc.description.isOpenAccessN-
dc.citation.endPage837-
dc.citation.number9-
dc.citation.startPage832-
dc.citation.title제어.로봇.시스템학회 논문지-
dc.citation.volume13-
dc.publisher.location대한민국-
dc.subject.keywordAuthorElectroencephalographic(EEG)-
dc.subject.keywordAuthorgesture-
dc.subject.keywordAuthoremotion recognition-
dc.subject.keywordAuthorInteractive Feature Selection(IFS)-
dc.description.journalRegisteredClasskci-
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