INTRODUCTION: Recent studies indicate that coupling between low- and high-frequency brain rhythms provides valuable information on cognitive processing in humans. One of the approaches to study these couplings is based on analysis of time-frequency representations of the ECoG or EEG signals aligned to a given phase in the low-frequency band. The method proposed by Canolty (2006) is based on time‑frequency representations obtained by bandpass filtering of the signal. Each band is normalized by means of the z-transform. AIM(S): We propose a development of Canolty’s method. The purpose of this study was to test the properties and efficacy of the enhanced method, and to compare the results to those obtained with the original Canolty’s method. METHOD(S): The proposed method relies on time--frequency representation of signal’s energy density derived from continuous wavelet transform, and normalization of each frequency relative to its average value in the baseline period (analogously to Event Related Synchronization/Desynchronization analysis). Moreover, we proposed the use of cluster‑based statistic to identify statistically significant effects. Both methods were tested on simulated signal. The simulations consist of a low-frequency sine (in the range of theta rhythm frequencies) with superimposed spindles of high-frequency (from the gamma band range) and white noise. For each method we determined the signal-to-noise ratio range where methods give reliable results, for selected ratios of gamma to theta amplitude. RESULTS: The proposed method turned out to be more sensitive and more specific. It identifies coupling only in the correct theta frequency, whereas original method localizes coupling also in neighboring frequency bands. The modified method is also more robust to higher noise levels. CONCLUSIONS: The findings suggest that the enhanced method have sufficient sensitivity to measure the theta-gamma coupling as measured by high quality EEG or ECoG.
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