Linked Life Data

20172787

Source:http://linkedlifedata.com/resource/pubmed/id/20172787

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-7-27
pubmed:abstractText
This paper proposes a novel spatial filter for biomagnetic source imaging. The proposed spatial filter is derived based on a modified version of the minimum-norm spatial filter and is designed to have a performance close to that of the adaptive minimum-variance spatial filter through the use of an estimated covariance matrix. In this method, the theoretical form of the measurement covariance matrix is estimated as an updated gram matrix in a recursive procedure. Since the proposed method does not use the sample covariance matrix, it is free of the well-known weaknesses of the minimum-variance spatial filter, namely, the proposed spatial filter does not require a large number of time samples, and it can even be applied to single-time-sample data. It is also robust to source correlation. We have validated the method's effectiveness by our computer simulations as well as through experiments using auditory-evoked magnetoencephalographic data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1558-2531
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1358-65
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Array-gain constraint minimum-norm spatial filter with recursively updated gram matrix for biomagnetic source imaging.
pubmed:affiliation
Department of Systems Design and Engineering, Tokyo Metropolitan University, Tokyo 191-0065, Japan. kumihashi136_ac@yahoo.co.jp
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't