The functional integration between the different parts of the brain is usually quantified through a measure of coherence. Most of the existing measures define coherence based on the spectral energy distribution of the signals rather than the phase, and therefore cannot be reliably used as measures of neural synchrony. Moreover, the most common methods for quantifying coherence are formulated in the frequency domain and thus, do not take into account the time-varying nature of brain activity. Recently, coherence measures have been extended to account for the energy and the phase relationships between the given signals and the time-varying nature of the signals using the wavelet transform. In this paper, we extend this idea by introducing a new time-varying phase coherence measure based on Cohen's class of time-frequency distributions. This new measure is applied to both synthesized signals and electroencephalogram (EEG) data to show the effectiveness of the proposed measure in estimating phase changes and in quantifying the neural synchrony in the brain.