Measuring the phase synchrony of brain signals using time-frequency distributions

Quantifying the phase synchrony of brain signals is important for the study of large-scale interactions in the brain. Current methods for computing phase synchrony are limited to amplitude-dependent correlation methods, frequency-domain coherence methods, and wavelet transform methods. However, many of these methods fail to take the time-varying nature of real life signals into account. To address this issue, we propose two measures of phase synchrony based on Cohen's class of time-frequency distributions. The first proposed method measures the phase synchrony between a pair of signals using a time-varying estimate of the phase difference between the two signals. The phase difference estimate is computed using a reduced interference complex time-frequency distribution. The second proposed method measures the phase synchrony among a group of signals by quantifying the frequency locking among the signals using a time-frequency based estimation of the instantaneous frequency. The instantaneous frequency maps of individual signals are combined to obtain the instantaneous frequency histogram as an estimate of the amount of frequency locking across the signals. This analysis is then extended to the estimation of frequency locking across multiple electrodes and multiple trials. Results are shown for both methods using synthetic signal models and electroencephalogram (EEG) data collected from control and schizophrenic subjects.