Neuroelectromagnetic imaging of correlated sources using a novel subspace penalized sparse learning = 부분공간학습을 이용한 동기화 된 신호원의 뇌전자기 영상기법

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Localization of brain signal sources from EEG/MEG has been an active area of research. Currently, there exists a variety of approaches such as MUSIC, M-SBL, and etc. These algorithms have been applied for various clinical examples and demonstrated excellent performances. However, when the unknown sources are highly correlated, the conventional algorithms often exhibit spurious reconstructions. To address the problem, this paper proposes a new algorithm that generalizes M-SBL by exploiting the fundamental subspace geometry in the multiple measurement problem (MMV). Experimental results using simulation and real epilepsy data show that the proposed algorithm outperforms the existing methods even under a highly correlated source condition.
Advisors
Ye, Jong-Chulresearcher예종철Jeong, Yong정용
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2013
Identifier
514954/325007  / 020113381
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2013.2, [ v, 43 p. ]

Keywords

joint sparse recovery; source localization; MEG; EEG; M-SBL; MUSIC; inverse problem; MMV; 뇌전도; 뇌자도; 신호원 국소화; 부분공간학습; 역문제; correlated signal

URI
http://hdl.handle.net/10203/179731
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=514954&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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