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dc.contributor.author 이진희 -
dc.contributor.author 최민국 -
dc.contributor.author 정희철 -
dc.contributor.author 권순 -
dc.contributor.author 정우영 -
dc.date.accessioned 2023-12-26T20:43:43Z -
dc.date.available 2023-12-26T20:43:43Z -
dc.date.created 2017-11-28 -
dc.date.issued 2017-11-11 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/47071 -
dc.description.abstract Recently, BCI-based research has become active along with advances in machine learning technology, and various BCI-based applications and EEG analysis methods have been developed. In this paper, we propose a framework for analyzing EEG signals based on Deep Neural Networks (DNN) and generating images depicting objects similar to those observed by human. This is a framework for automatically visual classifying using DNNs based on human complicated EEG sequences and generating images similar to human intentions. -
dc.language Korean -
dc.publisher 대한임베디드공학회 -
dc.title DeepImage BCI 기반의 Deep Neural Networks를 이용한 자동 시각 분류 및 영상 생성 프레임워크 -
dc.title.alternative Deeplmage: A Framework for Automated Visual Classification And Image Generation using BCl-Based Deep Neural Networks -
dc.type Conference Paper -
dc.identifier.bibliographicCitation 2017 대한임베디드공학회 추계학술대회, pp.416 - 418 -
dc.citation.conferencePlace KO -
dc.citation.conferencePlace 제주 -
dc.citation.endPage 418 -
dc.citation.startPage 416 -
dc.citation.title 2017 대한임베디드공학회 추계학술대회 -
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Division of Automotive Technology 2. Conference Papers
Convergence Research Center for Future Automotive Technology 2. Conference Papers

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