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 VRgenerated 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 Thelxinoë framework, aiming to enhance VR experiences by integrating multiple sensor data for realistic and emotionally resonant touch interactions. NTRODUCTION In a poetic sense, the eyes have long been regarded as the "window into the soul" offering a glimpse into the depths of human emotions and experiences [1]. In the realm of modern technology, this poetic vision transforms into a scientific reality, particularly in VR. The "pupils" serve as gateways not just "to the brain" but to the autonomic nervous system which subtly dilates and contracts in response to our emotions [1].

Medicine has provided the field of artificial intelligence with a plethora of challenging and appealing problems to be solved, particularly in clinical diagnosis ("given a set of signs collected from a patient, select the best diagnosis") and in therapy advice ("given an established diagnosis, select the best course of actions for treatment"). Artificial intelligence, in turn, has offered promising technologies for problem solving in the medical domain [7]. The field of oncology has proven to be particularly fit for modelling and analysis based on artificial intelligence, at least prospectively [5, 3], due to two major reasons: 1. Symptoms in oncology are frequently difficult to identify before later stages of the disease, and cancer can be treated most effectively if identified at early stages of development. Signs of the disease can be diffuse and require high expertise to be selected, collected and analysed. Hence, technologies that can highlight evidence of cancer at early stages are most welcome and challenging at the same time.