Background/Objectives: Quantifying emotions evoked by stimuli is a challenging task due to their subjective nature and the complexity of psychological states. Understanding how the brain processes emotions is critical, with applications in clinical, entertainment, and engineering fields. However, research on emotion processing in the brain is still in its infancy. This study focuses on quantifying emotions evoked by listening to slow-tempo musical stimuli (Raag Bhairavi), using Russell's circumplex model of emotion. The model utilizes the dimensions of valence and arousal to classify emotions. Frontal theta and gamma power asymmetry indices serve as key neurophysiological markers for emotion quantification, with participants categorized into Appreciators and Non-Appreciators based on their self-reported preferences for the musical stimulus. Methods: This empirical study employs Russell's 2D valence-arousal model to quantify evoked emotions. Electroencephalogram (EEG )data was recorded while participants listened to slow-tempo musical stimuli (Raag Bhairavi). Indices Used: Frontal theta and gamma power asymmetry indices were extracted to represent brain activity associated with emotional responses. Labelling: Participants provided self-reported responses to the stimulus, categorized as Appreciators (positive preference) or Non-Appreciators (negative/neutral preference). Model Development: Multiple models were created using frontal theta and gamma asymmetry indices. Classification: Support Vector Machine (SVM) was used for classification. Performance was validated using metrics such as accuracy and the Area Under the Curve (AUC). Results: The Total Gamma Theta (TGT) model achieved the best performance with a classification accuracy of 90.9% and an AUC of 90.74%. The Gamma Theta On 7 and 8 channel(GT78)model followed with an accuracy of 87.9% and an AUC of 89.28%. Conclusions: This study demonstrates the effectiveness of using EEG-based asymmetry indices for emotion quantification, providing a foundation for advancements in clinical, entertainment, and engineering applications.