Classification of musical preference in generation Z through EEG signal processing and machine learning

This paper proposes a methodology for investigating musical preferences of the age group between 18 and 24. We conducted an electroencephalogram (EEG) experiment to collect individual’s responses to audio stimuli along with a measure of like or dislike for a piece of music. Machine learning (multilayer perceptron and support vector machine) classifiers and signal processing [independent component analysis (ICA)] techniques were applied on the pre-processed dataset of 10 participant’s EEG signals and preference ratings. Our classification model classified song preference with high accuracy. The ICA based EEG signal processing enabled the identification of perceptual patterns via analysis of the spectral peaks which suggest that the recorded brain activities were dependent on the respective song’s rating