dc.citation.conferencePlace |
KO |
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dc.citation.conferencePlace |
Gwangju |
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dc.citation.endPage |
1629 |
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dc.citation.startPage |
1628 |
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dc.citation.title |
International Conference on Control, Automation and Systems |
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dc.contributor.author |
Yoo, Jaejun |
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dc.contributor.author |
Kim, Jongmin |
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dc.contributor.author |
Im, Chang-Hwan |
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dc.contributor.author |
Ye, Jong Chul |
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dc.date.accessioned |
2023-12-20T00:37:50Z |
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dc.date.available |
2023-12-20T00:37:50Z |
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dc.date.created |
2021-08-19 |
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dc.date.issued |
2013-10-20 |
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dc.description.abstract |
Brain signal source localization from E/MEG has been an active research area. Currently, there exists var- ious approaches such as MUSIC and M-SBL. However, when the unknown sources are highly correlated, conventional algorithms often exhibit spurious reconstructions. To address the problem, we propose a new algorithm that generalizes M-SBL by exploiting the fundamental subspace geometry in the multiple measurement problem (MMV). Results show that the proposed method outperforms the existing methods even with a highly correlated source. |
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dc.identifier.bibliographicCitation |
International Conference on Control, Automation and Systems, pp.1628 - 1629 |
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dc.identifier.doi |
10.1109/ICCAS.2013.6704191 |
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dc.identifier.issn |
1598-7833 |
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dc.identifier.scopusid |
2-s2.0-84893549661 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/53627 |
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dc.language |
영어 |
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dc.publisher |
제어로봇시스템학회 |
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dc.title |
Neuroelectromagnetic imaging of correlated sources using a novel subspace penalized sparse learning |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2013-10-20 |
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