TAILIEUCHUNG - Báo cáo sinh học: " Research Article Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 289571 13 pages doi 2010 289571 Research Article Time-Frequency Data Reduction for Event Related Potentials Combining Principal Component Analysis and Matching Pursuit Selin Aviyente 1 Edward M. Bernat 2 Stephen M. Malone 3 and William G. Iacono3 1 Department of Electrical and Computer Engineering Michigan State University East Lansing MI 48824 USA 2 Department of Psychology Florida State University Tallahassee FL 32306 USA 3 Department of Psychology University of Minnesota Minneapolis MN 55455 USA Correspondence should be addressed to Selin Aviyente aviyente@ Received 2 February 2010 Revised 30 March 2010 Accepted 5 May 2010 Academic Editor Syed Ismail Shah Copyright 2010 Selin Aviyente et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 .

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