Time-frequency data reduction for event related potentials: Combining principal component analysis and matching pursuit

Selin Aviyente, Edward M. Bernat, Stephen M. Malone, William G. Iacono

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14 Scopus citations


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 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 experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.

Original languageEnglish (US)
Article number289571
JournalEurasip Journal on Advances in Signal Processing
StatePublished - 2010

Bibliographical note

Funding Information:
This work was in part supported by Grants from the National Science Foundation under CAREER CCF-0746971, National Institutes of Health NIDA13240, NIDA05147, NIDA024417, NIAA09367, and K08MH080239.


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