NDLI: Analysis of Event Related Potentials using PCA and Matching Pursuit on the Time-Frequency Plane

Content Provider	IEEE Xplore Digital Library
Author	Aviyente, S. Bernat, E.M. Malone, S.M. Iacono, W.G.
Copyright Year	2006
Description	Author affiliation: Dept. of Electric. & Comput. Eng., Michigan Univ., East Lansing, MI (Aviyente, S.)
Abstract	Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This rich representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to physiologically meaningful components are essential. The method presented in this paper extends principal component analysis to the time-frequency plane to reduce a large set of ERPs to a small number of significant components. These components are then characterized using a Gabor dictionary to offer a succinct parametrization of the ERP data. The results show that the principal component analysis is successful at extracting components that can be described as the superposition of a small number of Gabor logons, and that the resulting set of logons succinctly represent physiologically meaningful ERP events
Sponsorship	IEEE EMB
Starting Page	2454
Ending Page	2457
File Size	136608
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.259590
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Matching pursuit algorithms Time frequency analysis Enterprise resource planning Data mining USA Councils Energy resolution Signal resolution Pursuit algorithms Cities and towns
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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