Classifying Songs with EEG

TL;DR

This study explores how EEG responses to music can be classified using machine learning, aiming to understand the neural basis of aesthetic enjoyment and resonance in musical perception.

Contribution

It introduces a new EEG dataset and applies machine learning models to classify neural responses to different songs, linking EEG entrainment to aesthetic experience.

Findings

EEG responses can be classified to distinguish different songs.

Resonance in EEG correlates with aesthetic enjoyment.

Machine learning models effectively predict musical engagement.

Abstract

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.