Source:http://linkedlifedata.com/resource/pubmed/id/11835609
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rdf:type | |
lifeskim:mentions | |
pubmed:issue |
4
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pubmed:dateCreated |
2002-2-8
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pubmed:abstractText |
We have applied the eigenspace-based beamformer to reconstruct spatio-temporal activities of neural sources from MEG data. The weight vector of the eigenspace-based beamformer is obtained by projecting the weight vector of the minimum-variance beamformer onto the signal subspace of a measurement covariance matrix. This projection removes the residual noise-subspace component that considerably degrades the signal-to-noise ratio (SNR) of the beamformer output when errors in estimating the sensor lead field exist. Therefore, the eigenspace-based beamformer produces a SNR considerably higher than that of the minimum-variance beamformer in practical situations. The effectiveness of the eigenspace-based beamformer was validated in our numerical experiments and experiments using auditory responses. We further extended the eigenspace-based beamformer so that it incorporates the information regarding the noise covariance matrix. Such a prewhitened eigenspace beamformer was experimentally demonstrated to be useful when large background activity exists.
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pubmed:language |
eng
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pubmed:journal | |
pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:month |
Apr
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pubmed:issn |
1065-9471
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pubmed:author | |
pubmed:copyrightInfo |
Copyright 2002 Wiley-Liss, Inc.
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pubmed:issnType |
Print
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pubmed:volume |
15
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
199-215
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pubmed:dateRevised |
2006-11-15
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pubmed:meshHeading | |
pubmed:year |
2002
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pubmed:articleTitle |
Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources.
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pubmed:affiliation |
Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Asahigaoka 6-6, Hino, Tokyo 191-0065, Japan. ksekiha@cc.tmit.ac.jp
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
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