Revealing Preference in Popular Music Through Familiarity and Brain Response

TL;DR

This study investigates how familiarity, response times, and EEG brain responses relate to music preference, using machine learning to classify preferences and highlighting potential applications in music therapy and healthcare.

Contribution

It introduces a combined behavioral and neural approach to classify music preference using machine learning on EEG, familiarity, and response data, revealing hemispheric differences.

Findings

EEG-based classification aligns with behavioral data.

Right hemisphere EEG outperforms left in preference classification.

Familiarity and response rates are significant indicators of music preference.

Abstract

Music preference was reported as a factor, which could elicit innermost music emotion, entailing accurate ground-truth data and music therapy efficiency. This study executes statistical analysis to investigate the distinction of music preference through familiarity scores, response times (response rates), and brain response (EEG). Twenty participants did self-assessment after listening to two types of popular music's chorus section: music without lyrics (Melody) and music with lyrics (Song). \textcolor{red}{We then conduct a music preference classification using a support vector machine, random forest, and k-nearest neighbors with the familiarity scores, the response rates, and EEG as the feature vectors. The statistical analysis and F1-score of EEG are congruent, which is the brain's right side outperformed its left side in classification performance.} Finally, these behavioral and brain studies support that preference, familiarity, and response rates can contribute to the music emotion experiment's design to understand music, emotion, and listener. Not only to the music industry, the biomedical and healthcare industry can also exploit this experiment to collect data from patients to improve the efficiency of healing by music.