Nondyadic decomposition algorithm with Meyer's wavelet packets: an application to EEG signal

Philippe Carre; Noel Richard; Christine Fernandez-Maloigne; Joel Paquereau

doi:10.1117/12.366834

26 October 1999 Nondyadic decomposition algorithm with Meyer's wavelet packets: an application to EEG signal

Philippe Carre, Noel Richard, Christine Fernandez-Maloigne, Joel Paquereau

Proceedings Volume 3813, Wavelet Applications in Signal and Image Processing VII; (1999) https://doi.org/10.1117/12.366834
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

In this paper, we propose an original decomposition scheme based on Meyer's wavelets. In opposition to a classical technique of wavelet packet analysis, the decomposition is an adaptative segmentation of the frequential axis which does not use a filters bank. This permits a higher flexibility in the band frequency definition. The decomposition computes all possible partitions from a sequential space: it does not only compute those that come from a dyadic decomposition. Our technique is applied on the electroencephalogram signal; here the purpose is to extract a best basis of frequential decomposition. This study is part of a multimodal functional cerebral imagery project.

Citation Download Citation

Philippe Carre, Noel Richard, Christine Fernandez-Maloigne, and Joel Paquereau "Nondyadic decomposition algorithm with Meyer's wavelet packets: an application to EEG signal", Proc. SPIE 3813, Wavelet Applications in Signal and Image Processing VII, (26 October 1999); https://doi.org/10.1117/12.366834

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KEYWORDS

Wavelets

Electroencephalography

Wavelet packet decomposition

Fourier transforms

Linear filtering

Algorithm development

Digital filtering

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