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   -> Volume 6, Issue 4

Thesis: Time-Frequency Analyses of EEG
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Author Message (Piotr Durka)

PostPosted: Tue Mar 25, 1997 12:54 pm    
Subject: Thesis: Time-Frequency Analyses of EEG
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#10 Thesis: Time-Frequency Analyses of EEG

PhD thesis: "Time-Freqency Analyses of EEG"
by Piotr J. Durka, Warsaw University.


Proper description of the electroencephalogram (EEG) often requires
simultaneous localization of signal's structures in time and
frequency. We discuss several time-frequency methods: windowed
Fourier transform, wavelet transform (WT), wavelet packets, wavelet
networks and Matching Pursuit (MP). Properties of orthogonal WT are
discussed in detail. Advantages of wavelet parameterization, including
fast calculation of band-limited products, are demonstrated on an
example of input preprocessing for feedforward neural network learning
detection of EEG artifacts.
MP algorithm finds sub-optimal solution to the problem of optimal linear
expansion of function over large and redundant dictionary of
waveforms. We construct a method for automatic detection and analysis
of sleep spindles in overnight EEG recordings, based upon MP with real
dictionary of Gabor functions. Each spindle is described in terms of
natural parameters. In the same way the slow wave activity (SWA) is
parametrized. In this framework several of reported in literature
hypotheses, regarding spatial, temporal and frequency distribution of
sleep spindles, and their relations to the SWA, are confirmed. We
present also an application to automatic detection and spatial
analysis of superimposed spindles. Finally, owing to its high
sensitivity, proposed approach allows the first insight into the issue
of low amplitude spindles, undetectable by the methods applied up to

Contains 97 pages, 36 figures. PostScript file comes in four flavors:
prepared for double- or single-sided printing and compressed as Unix *.Z
or DOS *.zip. Available via my Web page,
All times are GMT + 1 Hour
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