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The wavelet transform is a relatively new approach to data processing which has been applied in different areas such as signal, speech and image processing. In the last decade, many papers have been published on wavelet theory and its applications. The wavelet transform provides an elegant alternative to the classical Fourier or Gabor transforms unifying numerous signal processing techniques in a common framework. The purpose of the present paper is to provide an overview of the applicability of the wavelet transform to EEG signal analysis. In the first part of the paper the mathematical background is summarized. In the second part, applications to the sleep EEG field are presented and discussed. The results of these illustrations demonstrate the usefulness of the wavelet transform to solve various problems including signal parametrization, pattern recognition and biosignal representation.
Jobert et al. (Thu,) studied this question.