  In this study, we present a method for emotion recognition in Virtual Reality
(VR) using pupillometry. We analyze pupil diameter responses to both visual and
auditory stimuli via a VR headset and focus on extracting key features in the
time-domain, frequency-domain, and time-frequency domain from VR generated
data. Our approach utilizes feature selection to identify the most impactful
features using Maximum Relevance Minimum Redundancy (mRMR). By applying a
Gradient Boosting model, an ensemble learning technique using stacked decision
trees, we achieve an accuracy of 98.8% with feature engineering, compared to
84.9% without it. This research contributes significantly to the Thelxino\"e
framework, aiming to enhance VR experiences by integrating multiple sensor data
for realistic and emotionally resonant touch interactions. Our findings open
new avenues for developing more immersive and interactive VR environments,
paving the way for future advancements in virtual touch technology.
