EEG Correlates of Music Familiarity and Enjoyment: Individual-Level Analysis

Abstract

The experience of listening to music is a complex perceptual and cognitive phenomenon influenced by two primary subjective factors: familiarity with the material and the pleasure it generates. Despite the extensive application of electroencephalography (EEG) in the investigation of music cognition, the brain correlates of these subjective evaluations have not been analysed at the individual subject level using open-access datasets. This study offers the inaugural individual-level examination of familiarity and enjoyment ratings derived directly from raw event markers in the Music Listening Genre EEG dataset (MUSIN-G), which includes 20 subjects who listened to 12 songs across several genres. Spectral power characteristics across five canonical frequency bands were retrieved from 240 recording sessions and evaluated using nonparametric statistical tests with false discovery rate adjustment, Spearman rank correlation, and leave-one-subject-out cross-validation employing three machine learning classifiers. Familiarity strongly influenced band power in the delta, theta, alpha, and beta ranges, with the theta band demonstrating the most pronounced effect (Cohen's $\mathrm{d}=-0.368$, Spearman $\rho=0.184$, p_FDR $=0.021$). A song-level null result verified that this effect is influenced by individual exposure history rather than acoustic characteristics. In contrast, no substantial EEG correlates of enjoyment were identified across any frequency band. The machine learning analysis of familiarity did not surpass random chance in cross-subject evaluations, thereby providing a reliable baseline and encouraging future research utilising spatial and temporal EEG characteristics.