## 3 Materials and Methods
### Data Description
In the experiment phase, the eye-tracking, ECG, and GSR data were collected using the final 12 VEs, each trio representing one quadrant of the CMA and designed to evoke a range of emotional responses corresponding to the different quadrants. Each VE included diverse elements of sights, sounds, and activities to create immersive and engaging experiences. In certain environments, participants were exposed to stimuli such as monsters, zombies, or a possessed woman to induce emotions associated with high arousal and negative valence, such as fear, stress, or anger. To evoke emotions of high arousal and positive valence, like excitement, participants were immersed in scenarios where they walked on a tightrope or danced with exotic performers. For inducing low arousal andpositive valence emotions, such as calmness and relaxation, participants encountered settings like a farm with bunnies, a forest with bird sounds, or various spas and tranquil locations around the world. Lastly, to elicit low arousal and negative valence emotions, such as depression and sadness, participants were immersed in environments depicting mourning scenes, refugee camps, or war zones. A total of 34 participants (17 males and 17 females) aged between 18 and 61 years volunteered for this phase after conducting a survey. 19 of them reported having used VR before. None have reported feeling motion sick amid or post-exposure to VR. Before the experiment, all participants signed a consent form and filled out a pre-exposure questionnaire, including the SAM and VAS. Then, they engaged with these environments in a randomized order, from which eye tracking, ECG, and GSR data were collected. They also repeated the SAM and VAS questionnaires after the exposure to VR. Initially, the interactions resulted in 408 trials; however, due to concerns about data quality and technical issues, the final dataset was narrowed down to 312 trials involving 26 participants. Subsequent to data collection, the raw eye-tracking data were processed using the GazeParser library in Python [50]. This processing extracted vital features such as fixation, micro-saccade, saccade, and blink. Fixation is defined as a temporary halt in eye movement; micro-saccades as minor; involuntary movements within a fixation; saccades as rapid movements between fixations; and blinks as periods when the eyes are closed. The researchers then computed sub-features for each main feature, utilizing statistical calculations like normalized count (NormCount), mean, standard deviation (SD), skewness (Skew), and maximum (Max). These eye-tracking features are detailed in Table 1.  
Additionally, the eye-tracking dataset includes a target column labeled 'Quadrant Category', which corresponds to a quadrant of the CMA for each trial. Table 2 delineates the nominal categories associated with each CMA quadrant. In total, the dataset comprises 312 rows and 50 columns, capturing a comprehensive set of variables crucial for the subsequent analysis. \begin{table}
\begin{tabular}{c c} \hline \hline
**Main Features** & **Statistical Metrics** \\ \hline \multirow{3}{*}{Fixation} & Number of Fixations (NormCount) per second \\  & First Fixation Duration \\  & Duration (M, Max, SD, Skew) \\ \hline \multirow{3}{*}{Micro-Saccade} & Number of Micro-Saccade (NormCount) per second \\  & Peak Velocity (M, Max, SD, Skew) \\  & Direction (Mean, Max, SD, Skew) \\  & Horizontal Amplitude (Mean, Max, SD, Skew) \\  & Vertical Amplitude (Mean, Max, SD, Skew) \\ \hline \multirow{3}{*}{Saccade} & Number of Saccade (NormCount) per second \\  & Duration (Mean, Max, SD, Skew) \\  & Direction (Mean, Max, SD, Skew) \\ \hline \multirow{2}{*}{Blink} & Number of Blink (NormCount) per second \\  & Duration (Mean, Max, SD, Skew) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: All eye tracking data features included in the present study.