The study of human brain functions has dramatically increased greatly due to the advent of functional Magnetic Resonance Imaging (fMRI), arguably the best technique for observing human brain activity that is currently available. However, fMRI techniques produce extremely high dimensional, sparse and noisy data which is difficult to visualize, monitor and analyze. In this document, we propose a sonification approach for exploratory fMRI data analysis. The goal of this tool is to allow the auditory identification of cognitive states produced by different stimuli. The system consists of a feature selection component and a sonification engine. We will explore different feature selection methods and sonification strategies. Moreover, we present a computational model which predicts the fMRI neural activation in humans produced by rhythm/no-rhythm auditory stimuli. The model was trained with acoustic features extracted from the auditory signals and the associated observed fMRI images. The obtained model is able to predict fMRI activation with high accuracy. This work represent
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