Linked Life Data

19224726

Source:http://linkedlifedata.com/resource/pubmed/id/19224726

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-2-19
pubmed:abstractText
Electroencephalographic (EEG) source localization is an important tool for noninvasive study of brain dynamics, due to its ability to probe neural activity more directly, with better temporal resolution than other imaging modalities. One promising technique for solving the EEG inverse problem is Kalman filtering, because it provides a natural framework for incorporating dynamic EEG generation models in source localization. Here, a recently developed inverse solution is introduced, which uses spatiotemporal Kalman filtering tuned through likelihood maximization. Standard diagnostic tests for objectively evaluating Kalman filter performance are then described and applied to inverse solutions for simulated and clinical EEG data. These tests, employed for the first time in Kalman-filter-based source localization, check the statistical properties of the innovation and validate the use of likelihood maximization for filter tuning. However, this analysis also reveals that the filter's existing space- and time-invariant process model, which contains a single fixed-frequency resonance, is unable to completely model the complex spatiotemporal dynamics of EEG data. This finding indicates that the algorithm could be improved by allowing the process model parameters to vary in space.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1558-2531
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
56
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
122-36
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Evaluating the performance of Kalman-filter-based EEG source localization.
pubmed:affiliation
School of Physics, University of Sydney, Sydney, N.S.W. 2006, Australia. m.barton@physics.usyd.edu.au
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't