Effect of Familiarity on Recognition of Pleasant and Unpleasant Emotional States Induced by Hindi Music Videos

Abstract

Valence is an important dimension representing the hedonic value of emotion labeled as positive or negative. Inducing these emotions and making an understanding from brain responses have huge practical significance. Music is a powerful tool to induce emotions, and the induced emotions are generally getting influenced by factors external to music such as familiarity. This work presents a novel study on the effect of familiarity on recognition of pleasant (positive) and unpleasant (negative) emotional states induced by Hindi music videos. For this, we recorded 32-channel EEG from six healthy subjects while they watched Hindi music videos and self-reported ratings of felt emotions on valence and familiarity scale. We used a machine learning framework for emotion classification from power spectral and functional connectivity features. The framework consists of SVD-QRcp and F-ratio based feature selection and an SVM classifier. The classification was performed under three cases of familiarity, namely, familiar, unfamiliar, and regardless of familiarity of the music videos. We found that for the familiar case, the classification performance was higher than unfamiliar and regardless of familiarity cases for all considered features. The best performing features were from the individual electrodes and these features were from the frontal and left parietal regions which indicate the lateralized processing of valence. In addition to classification, we analyzed the feature and electrode usage for all the cases of familiarity. It was found that the features from theta, alpha, and gamma band covering the frontal and parietal brain regions were dominantly involved.