  This research study aims to use machine learning methods to characterize the
EEG response to music. Specifically, we investigate how resonance in the EEG
response correlates with individual aesthetic enjoyment. Inspired by the notion
of musical processing as resonance, we hypothesize that the intensity of an
aesthetic experience is based on the degree to which a participants EEG
entrains to the perceptual input. To test this and other hypotheses, we have
built an EEG dataset from 20 subjects listening to 12 two minute-long songs in
random order. After preprocessing and feature construction, we used this
dataset to train and test multiple machine learning models.
