A unified time-frequency parametrization of EEGs

Seventy years since the first recording of the human electroencephalogram (EEG), 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. Seven years since the introduction of the matching pursuit (MP) algorithm, the authors have collected evidence suggesting that adaptive time-frequency approximation is a good candidate for a universal high-resolution parameterization of EEG data, compatible with the visual and spectral analysis, and applicable to a large class of problems. Here, the authors briefly discuss the need for a generally applicable method for a mathematical description (parameterization) of the signal, which would be directly related to the heritage of the traditional EEG analysis. In this context the authors discuss application of the MP algorithm. They present recent advances in analysis of sleep EEGs and discuss earlier works on event-related potentials and epileptic recordings.