fMRI Sonification & Brain Activity Prediction

TitlefMRI Sonification & Brain Activity Prediction
Publication TypeMaster Thesis
Year of Publication2011
AuthorsGómez Rubio, I.
Abstract

 

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 dicult to visualize, monitor and analyze. In this document, we propose a soni cation approach for exploratory fMRI data analysis. The goal of this tool is to allow the auditory identi cation of cognitive states produced by di erent stimuli. The system consists of a feature selection component and a soni cation engine. We will explore di erent feature selection methods and soni cation 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 represents a natural progression from building catalogues of patterns of fMRI activity associated with particular auditory stimuli to constructing computational models which predict the fMRI activity for auditory stimuli for which fMRI data are not available yet.
Final publicationhttps://doi.org/10.5281/zenodo.1161175
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