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

Area of Science:

• Neuroscience
• Psychology
• Performing Arts

Background:

• Western emotion research often uses distinct categories, while ancient Indian arts describe emotions as complex states called Rasas.
• Previous research has not explored the neural correlates of these nine Rasas defined in the Natyashastra.
• Understanding Rasa-specific neural activity could bridge traditional arts and modern neuroscience.

Purpose of the Study:

• To investigate the neural oscillations and functional connectivity patterns associated with experiencing the nine Rasas.
• To identify which brainwave frequencies and network metrics best differentiate between distinct Rasa states.
• To explore the potential of machine learning models in classifying Rasas based on EEG data.

Main Methods:

• Electroencephalography (EEG) was used to record neural oscillations in participants experiencing Rasa-related emotional states.
• Network-based functional connectivity metrics were applied across five frequency bands (delta, theta, alpha, beta, gamma).
• Random Forest models were trained on extracted network features to classify the different Rasas.

Main Results:

• Slow (delta) and fast (beta, gamma) brain waves showed the most significant differences between Rasas.
• Sringaram (love), Bibhatsam (odious), and Bhayanakam (terror) were most distinguishable.
• Raudram (rage) and Sringaram (love) exhibited extreme network metric values, achieving 95% classification accuracy, aligning with the circumplex model of emotion.

Conclusions:

• This study provides initial evidence for distinct neural signatures of the nine Rasas, moving beyond Western emotion models.
• Higher frequency oscillations (beta, gamma) are crucial for discriminating between complex emotional states like Rasas.
• Findings may inform future research on performer-audience brainwave entrainment and enhance artistic experiences.