Classifying Songs with EEG
This work addresses the problem of understanding neural correlates of music perception for researchers in neuroscience and machine learning, but it appears incremental as it applies existing methods to a new dataset without claiming major breakthroughs.
The study tackled the problem of characterizing EEG responses to music by investigating the correlation between EEG resonance and individual aesthetic enjoyment, resulting in the creation of an EEG dataset from 20 subjects listening to 12 songs and training multiple machine learning models for classification.
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.