Stochastic Time-Frequency Dictionaries for Matching Pursuit
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Analyzing large amounts of sleep electroencephalogram (EEG) data by means of the matching pursuit (MP) algorithm, we encountered a statistical bias of the decomposition, resulting from the structure of the applied dictionary. As a solution we propose stochastic dictionaries, where the parameters of the dictionary's waveforms are randomized before each decomposition. The MP algorithm was modified for this purpose and tuned for maximum time-frequency resolution. Examples of applications of the new method include parametrization of EEG structures and time-frequency representation of signals with changing frequency. Keywords: time-frequency, adaptive approximations, signal analysis, EEG, matching pursuit
Stochastic time-frequency dictionaries for Matching Pursuit, P.J. Durka, D. Ircha and K.J. Blinowska. IEEE Transactions on Signal Processing, vol. 49, No. 3, pp. 507-510, March 2001.
Sep 17, 2003 01:30 PM
