Numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between EEG signals. This interdependency parameter is often used to characterize the functional coupling between different brain structures or regions during either normal or pathological processes. In this paper we focus on the time-frequency characterization of interdependencies between nonstationary signals. Particularly, we propose a novel estimator based on the cross correlation of narrow band filtered signals. In a simulation framework, results show that this estimator may exhibit higher statistical performances (bias and variance) compared to a more classical estimator based on the coherence function. On real data (intracerebral EEG signals), they show that this estimator enhances the readability of the time-frequency representation of the relationship and can thus improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).