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The temporal coordination of neural activity within structural networks of the brain has been posited as a basis for cognition. Changes in the frequency and similarity of oscillating electrical potentials emitted by neuronal populations may reflect the means by which networks of the brain carry out functions critical for adaptive behavior. A computation of the phase relationship between signals recorded from separable brain regions is a method for characterizing the temporal interactions of neuronal populations. Recently, different phase estimation methods for quantifying the time-varying and frequency-dependent nature of neural synchronization have been proposed. The most common method for measuring the synchronization of signals through phase computations uses complex wavelet transforms of neural signals to estimate their instantaneous phase difference and locking. In this article, we extend this idea by introducing a new time-varying phase synchrony measure based on Cohen's class of time-frequency distributions. This index offers improvements over existing synchrony measures by characterizing the similarity of signals from separable brain regions with uniformly high resolution across time and frequency. The proposed measure is applied to both synthesized signals and electroencephalography data to test its effectiveness in estimating phase changes and quantifying neural synchrony in the brain.

Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.