Linked Life Data

16414121

Source:http://linkedlifedata.com/resource/pubmed/id/16414121

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-5-15
pubmed:abstractText
In this work, we present a multichannel EEG decomposition model based on an adaptive topographic time-frequency approximation technique. It is an extension of the Matching Pursuit algorithm and called dependency multichannel matching pursuit (DMMP). It takes the physiologically explainable and statistically observable topographic dependencies between the channels into account, namely the spatial smoothness of neighboring electrodes that is implied by the electric leadfield. DMMP decomposes a multichannel signal as a weighted sum of atoms from a given dictionary where the single channels are represented from exactly the same subset of a complete dictionary. The decomposition is illustrated on topographical EEG data during different physiological conditions using a complete Gabor dictionary. Further the extension of the single-channel time-frequency distribution to a multichannel time-frequency distribution is given. This can be used for the visualization of the decomposition structure of multichannel EEG. A clustering procedure applied to the topographies, the vectors of the corresponding contribution of an atom to the signal in each channel produced by DMMP, leads to an extremely sparse topographic decomposition of the EEG.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0165-0270
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
153
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
261-75
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition.
pubmed:affiliation
Department of Psychiatric Neurophysiology, University Hospital of Clinical Psychiatry, Bolligenstrasse 111, CH-3000 Berne, Switzerland. daniel.studer@puk.unibe.ch
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't