Linked Life Data

15000382

Source:http://linkedlifedata.com/resource/pubmed/id/15000382

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-3-5
pubmed:abstractText
An adaptive spectrum estimation method for nonstationary electroencephalogram by means of time-varying autoregressive moving average modeling is presented. The time-varying parameter estimation problem is solved by Kalman filtering along with a fixed-interval smoothing procedure. Kalman filter is an optimal filter in the mean square sense and it is a generalization of other adaptive filters such as recursive least squares or least mean square. Furthermore, by using the smoother the unavoidable tracking lag of adaptive filters can be avoided. Due to the properties of Kalman filter and benefits of the smoothing the time-frequency resolution of the presented Kalman smoother spectra is extremely high. The presented approach is applied to estimation of event-related synchronization/desynchronization (ERS/ERD) dynamics of occipital alpha rhythm measured from three healthy subjects. With the Kalman smoother approach detailed spectral information can be extracted from single ERS/ERD samples.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
51
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
516-24
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS).
pubmed:affiliation
Department of Applied Physics, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland. Mika.Tarvainen@uku.fi
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies, Validation Studies