## 3 Materials and Methods
### Data Description  
The present study utilized the publicly available VREED (VR Eyes: Emotions dataset), a multimodal affective dataset where emotions were elicited using immersive 360-degree video-based virtual environments (360-VEs) collected through VR headsets [14]. VREED is particularly useful for addressing our research question due to several key factors. Firstly, it is one of the pioneering multimodal VR datasets specifically designed for emotion recognition, incorporating both behavioral and physiological signals, including eye-tracking, ECG, and GSR data. This multimodal approach provides a comprehensive view of emotional responses, making it well-suited for our goal of analyzing visual attention and gaze patterns in VR environments. Secondly, the dataset was meticulously curated, with environments selected based on feedback from focus groups and pilot trials, ensuring that the stimuli used effectively elicit the intended emotional responses. This rigorous selection process enhances the validity of the data, making it a reliable source for examining the correlation between eye-tracking metrics and emotional states. Additionally, VREED includes data from a diverse group of participants across various age ranges, which improves the generalizability of our findings. The balanced representation of different emotional quadrants within the CMA further strengthens the dataset's relevance to our research. While ECG and GSR data provide valuable physiological insights, in the present study, we chose to focus exclusively on eye-tracking metrics since our primary objective was to analyze visual attention and gaze patterns in VR environments. Eye-tracking data offers a unique and direct window into cognitive and emotional processes, providing rich insights into how individuals interact with virtual environments. Given that eye movements are closely linked to visual attention and can be precisely quantified, they serve as a reliable indicator of emotional states. Moreover, eye-tracking metrics such as saccades, fixations, and blinks offer specific and actionable data that can be directly correlated with emotional responses. This focus aligns with our goal of advancing the understanding of affective computing within VR by leveraging XAI techniques to interpret these specific metrics. The eye-tracking, ECG, and GSR data were collected from 34 healthy participants, comprising 17 males and 17 females, aged between 18 and 61 years. All participants were required to sign a consent form and complete a pre-exposure questionnaire. They interacted with 12 distinct VEs selected based on a focus group and a pilot trial. During the selection phase of VEs, the focus group--comprising six experts in human-computer interaction and psychology--identified the potential 126 VEs, which they manually found on the Youtube platform [14]. The focus group selected 21 VEs out of all 126, adhering to specific exclusion criteria. Subsequently, a pilot trial involving 12 volunteers (6 males and 6 females) aged between 19 and 33 years further refined these 21 environments using two established psychological measurement tools: the Self-Assessment Manikin (SAM) and the Visual Analog Scale (VAS) [48,49]. SAM, utilizing cartoon-shaped manikins, helps visualize the arousal-valence dimensions of emotions. At the same time, VAS is a 0 to 100 linear scale for quantifying various emotional states such as joy, happiness, calmness, relaxation, anger, disgust, fear, anxiousness, and sadness. Since the objective of the pilot trial was to select three 360-VEs corresponding to each quadrant of the CMA, ensuring a comprehensive representation of emotional states, a total of 12 VEs were chosen for the final experiment according to SAM (arousal, valence) and VAS (joy, anger, calmness, sadness, disgust, relaxation, happiness, fear, anxiousness, and dizziness) ratings.