Linked Life Data

10473216

Source:http://linkedlifedata.com/resource/pubmed/id/10473216

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
1999-10-13
pubmed:abstractText
Magnetoencephalography (MEG) is a method which allows the non-invasive measurement of the minute magnetic field which is generated by ion currents in the brain. Due to the complex sensitivity profile of the sensors, the measured data are a non-trivial representation of the currents where information specific to local generators is distributed across many channels and each channel contains a mixture of contributions from many such generators. We propose a framework which generates a new representation of the data through a linear transformation which is designed so that some desired property is optimized in one or more new virtual channel(s). First figures of merit are suggested to describe the relation between the measured data and the underlying currents. Within this context the new framework is established by first showing how the transformation matrix itself is designed and then by its application to real and simulated data. The results demonstrate that the proposed linear transformations of data space provide a computationally efficient tool for analysis and a very much needed dimensional reduction of the data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0031-9155
pubmed:author
pubmed:issnType
Print
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2081-97
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Linear transformations of data space in MEG.
pubmed:affiliation
Institute of Medicine, Research Center Jülich GmbH, Germany.
pubmed:publicationType
Journal Article, Comparative Study