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In this paper, we propose a novel information theoretic model to interpretthe entire "transmission chain" comprising stimulus generation, brainprocessing by the human subject, and the electroencephalograph (EEG) responsemeasurements as a nonlinear, time-varying communication channel with memory. Weuse mutual information (MI) as a measure to assess audio quality perception bydirectly measuring the brainwave responses of the human subjects using a highresolution EEG. Our focus here is on audio where the quality is impaired bytime varying distortions. In particular, we conduct experiments where subjectsare presented with audio whose quality varies with time between differentpossible quality levels. The recorded EEG measurements can be modeled as amultidimensional Gaussian mixture model (GMM). In order to make the computationof the MI feasible, we present a novel low-complexity approximation techniquefor the differential entropy of the multidimensional GMM. We find the proposedinformation theoretic approach to be successful in quantifying subjective audioquality perception, with the results being consistent across different musicsequences and distortion types.

An Information Theoretic Approach Towards Assessing Perceptual Audio Quality using EEG