INSTITUTE OF EXPERIMENTAL PHYSICS
DEPARTMENT OF PHYSICS
WARSAW UNIVERSITY

Time-frequency analyses of EEG

Advisor
Prof. dr hab. Katarzyna J. Blinowska

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHYSICS

AUGUST 1996

Abstract

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 now.

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