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   -> Volume 6, Issue 3

Preprint: A Wavelet Packet Model of Evoked Potentials
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Jonathan Raz (

PostPosted: Tue Mar 04, 1997 6:03 pm    
Subject: Preprint: A Wavelet Packet Model of Evoked Potentials
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#9 Preprint: A Wavelet Packet Model of Evoked Potentials

The following paper is available from:

or by contacting:

Jonathan Raz Telephone: (313) 936-1009
Department of Biostatistics FAX: (313) 763-2215
University of Michigan Email:
Ann Arbor, MI 48109-2029

Title: A Wavelet Packet Model of Evoked Potentials

Authors: Jonathan Raz, Linda Dickerson, and Bruce Turetsky

Abstract: The standard methods for decomposition and analysis of
evoked potentials are bandpass filtering, identification of peak
amplitudes and latencies, and principal component analysis (PCA). We
discuss the limitations of these and other approaches, and introduce
wavelet packet analysis. Then we propose the ``single channel wavelet
packet model," a new approach in which a unique decomposition is
achieved using prior time/frequency information and differences in the
responses of the components to changes in experimental
conditions. Orthogonal sets of wavelet packets allow a parsimonious
time/frequency representation of the components. The method allows
energy in some wavelet packets to be shared among two or more
components, so the components are not necessarily orthogonal. The
single channel wavelet packet model and PCA both require constraints
to achieve a unique decomposition. In PCA, however, the constraints
are defined by mathematical convenience and may be unrealistic. In the
single channel wavelet packet model, the constraints are based on
prior scientific knowledge. We give an application of the method to
auditory evoked potentials recorded from cats. The good frequency
resolution of wavelet packets allows us to separate superimposed
components in these data. Our present approach yields estimates of
component waveforms and the effects of experiment conditions on the
amplitude of the components. We discuss future extensions that will
provide confidence intervals and p-values, allow for latency changes,
and represent multichannel data.
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