Human ECoG analysis during speech perception using matching pursuit: a comparison between stochastic and dyadic dictionaries

摘要

We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (<70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (<98.5%) to the one generated by the stochastic MP. However, the dyadic MP is computationally much faster than the stochastic MP.