Electroencephalography (EEG) dataset during naturalistic music listening comprising different genres with familiarity and enjoyment ratings.

The article provides an open-source Music Listening- Genre (MUSIN-G) EEG dataset which contains 20 participants' continuous Electroencephalography responses to 12 songs of different genres (from Indian folk music to Goth Rock to western electronic), along with their familiarity and enjoyment ratings. The participants include 16 males and 4 females, with an average age of 25.3 (+/-3.38). The EEG data was collected at the Indian Institute of Technology Gandhinagar, India, using 128 channels Hydrocel Geodesic Sensor Net (HCGSN) and the Netstation 5.4 data acquiring software. We provide the raw and partially preprocessed data of each participant while they listened to 12 different songs with closed eyes. The dataset also contains the behavioural familiarity and enjoyment ratings (scale of 1 to 5) of the participants for each of the songs. In this article, we further discuss the preprocessing steps which can be used on the dataset and prepare the data for analysis, as in the paper [1].

Classifying oscillatory brain activity associated with Indian Rasas using network metrics.

Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa-as opposed to a pure emotion-is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exhibited the maximum discriminating features between Rasas, whereas alpha and theta bands showed fewer distinguishable pairs. Out of nine Rasas, Sringaram (love), Bibhatsam (odious), and Bhayanakam (terror) were distinguishable from other Rasas the most across frequency bands. On the scale of most network metrics, Raudram (rage) and Sringaram are on the extremes, which also resulted in their good classification accuracy of 95%. This is reminiscent of the circumplex model where anger and contentment/happiness are on extremes on the pleasant scale. Interestingly, our results are consistent with the previous studies which highlight the significant role of higher frequency oscillations in the classification of emotions, in contrast to the alpha band that has shows non-significant differences across emotions. This research contributes to one of the first attempts to investigate the neural correlates of Rasas. Therefore, the results of this study can potentially guide the explorations into the entrainment of brain oscillations between performers and viewers, which can further lead to better performances and viewer experience.