Brain Melody Interaction: Understanding Effects of Music on Cerebral Hemodynamic Responses

Abstract

Music elicits strong emotional reactions in people, regardless of their gender, age or cultural background. Understanding the effects of music on brain activity can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research. We explore the effects of three different music genres on people’s cerebral hemodynamic responses. Functional near-infrared spectroscopy (fNIRS) signals were collected from 27 participants while they listened to 12 different pieces of music. The signals were pre-processed to reflect oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentrations in the brain. K-nearest neighbor (KNN), random forest (RF) and a one-dimensional (1D) convolutional neural network (CNN) were used to classify the signals using music genre and subjective responses provided by the participants as labels. Results from this study show that the highest accuracy in distinguishing three music genres was achieved by deep learning models (73.4% accuracy in music genre classification and 80.5% accuracy when predicting participants’ subjective rating of emotional content of music). This study validates a strong motivation for using fNIRS signals to detect people’s emotional state while listening to music. It could also be beneficial in giving personalised music recommendations based on people’s brain activity to improve their emotional well-being.