Classifying oscillatory brain activity associated with Indian Rasas using network metrics

Neural oscillations are the rich source to understand cognition, perception, and emotions. Decades of research on brain oscillations have primarily discussed neural signatures for the western classification of emotions. Despite this, the Indian ancient treatise on emotions popularly known as Rasas has remained unexplored. In this study, we collected Electroencephalography (EEG) encodings while participants watched nine emotional movie clips corresponding to nine Rasas. The key objective of this study is to identify the brain waves that could
distinguish between Rasas. Therefore, we decompose the EEG signals into five primary frequency bands comprising delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). We construct the functional networks from EEG time-series data and subsequently utilize the fourteen graph-theoretical measures to compute the features. Random Forest models are trained on the extracted 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, Bibhatsam, and Bhayanakam displayed the most distinguishing characteristics from other Rasas. Interestingly, our results are consistent with the previous studies, which highlight the significant role of higher frequency oscillations for the classification of emotions. Our finding on the alpha band is consistent with the previous study, which reports the maximum similarity in brain networks across emotions in the alpha band. This research contributes to the pioneering work on Indian Rasas utilizing brain responses.