We propose a novel information theoretic model to interpret the entire ``transmission chain'' comprising stimulus generation, brain processing by the human subject, and the electroencephalograph (EEG) response measurements as a nonlinear, time-varying communication channel with memory. Mutual information (MI) is used as a measure to assess audio quality perception by directly analyzing the brainwave responses of human subjects listening to music sequences whose quality varies with time. We show that the recorded EEG measurements can be modeled as a multidimensional Gaussian mixture model (GMM), and present a novel low-complexity approximation technique for the differential entropy of the multidimensional GMM. Additionally, we propose a new causal bidirectional information (CBI) measure to infer the cortical functional connectivity in response to the varying audio stimulus. CBI can be intuitively interpreted as a causal bidirectional modification of directed information, and inherently calculates the divergence of the observed data from a multiple access channel (MAC) with feedback. The results indicate the proposed information theoretic approach to be successful in quantifying and distinguishing between the perceived audio quality.