Introduction

70 years since the first recording of human electroencephalogram (EEG, [1]), visual analysis of raw EEG traces is still the major clinical tool and point of reference for other methods, in spite of its inherent limitations: low repeatability and high cost. 7 years since the introduction of the matching pursuit (MP, [9]), we collected evidence suggesting that adaptive time-frequency approximation is a good candidate for a universal high-resolution parametrization of EEG, compatible with the visual and spectral analysis, and applicable to a large class of problems. In the following we briefly discuss the need for a generally applicable method for a mathematical description (parametrization) of the signal, which would be directly related to the heritage of the traditional EEG analysis. The main section discusses in this context application of the Matching Pursuit algorithm. We present recent advances in analysis of sleep EEG and discuss earlier works on event-related potentials and epileptic recordings.

Figure 1: 70 years of progress in clinical electroencephalography: from visual analysis of EEG traces on paper (background picture) to visual analysis of EEG traces displayed on CRT (front).