Full metadata record
DC Field | Value | Language |
---|---|---|
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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