From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

Studer, Daniel; Hoffmann, Ulrich; Koenig, Thomas (2006). From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition. Journal of neuroscience methods, 153(2), pp. 261-75. Amsterdam: Elsevier 10.1016/j.jneumeth.2005.11.006

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy > Psychiatric Neurophysiology (discontinued)

UniBE Contributor:

König, Thomas

ISSN:

0165-0270

ISBN:

16414121

Publisher:

Elsevier

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:51

Last Modified:

06 Dec 2013 13:44

Publisher DOI:

10.1016/j.jneumeth.2005.11.006

PubMed ID:

16414121

Web of Science ID:

000238101700012

URI:

https://boris.unibe.ch/id/eprint/21763 (FactScience: 13614)

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