## 4 Results and Discussion
### Shap Analysis Across Reduced Features
#### 4.4.5 Key Findings of SHAP Analysis for Reduced Features  
Table 8 illustrates the variations in F1-scores across all quadrants as the number of features changes. Employing these 15 reduced features-Number of Micro-Saccade, Number of Blink, Number of Saccade, Number of Fixations, Blink Duration, Fixation Duration, Saccade Duration, Saccade Direction, Saccade Amplitude, Saccade Length, Micro-Saccade Amplitude, Micro Saccade Direction, Micro Saccade Peak Velocity, Micro Saccade Vertical Amplitude, and Micro Saccade Horizontal Amplitude- yields F1-scores of 0.9148, 0.8186, 0.8806, and 0.8504 for Quadrants 0, 1, 2, and 3, respectively. However, optimizing the model to prioritize features with greater importance values may lead to further improvements in performance. Table 8 indicates that Quadrant 0 achieves 0.9254 with 13 features, Quadrant 1 reaches 0.8399 with 10 features, Quadrant 2 matches its F1-score with just 5 features and surpasses it with 6, and Quadrant 3 achieves 0.8721 with 10 features. This examination reveals that a reduced feature set can maintain high performance while enhancing computational efficiency.  
Key findings for four quadrants after SHAP analysis, including reduced 15 features, can be seen in Table 9, referring to the attributes that exhibit the greatest importance according to summary plots. The table 9 shows that 'Number of Micro-Saccade' is the most significantly impacting feature for the first three quadrants, whereas features related to blinking are crucial for Quadrant 3. Both Tables 6 and 9 infer that saccadic eye movements and blinking are the most significant main features according to SHAP.  
Understanding these features can significantly enhance applications in affective computing and human-computer interaction. By identifying key eye-tracking features that correlate with specific emotional states, we can develop more responsive and adaptive user interfaces that adjust in real-time to a user's emotional state. For instance, in VR environments, knowing that certain eye movement patterns indicate stress or relaxation can allow the system to modify the virtual experience accordingly, improving user comfort and engagement. Additionally, in educational technologies, detecting frustration or confusion through eye-tracking can trigger adaptive learning systems to provide additional support or alter instructional strategies, thereby enhancing learning outcomes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Feature Number** & **F1-Score for Q0** & **F1-Score for Q1** & **F1-Score for Q2** & **F1-Score for Q3** \\ \hline
2 & 0.7765 & 0.7127 & 0.7009 & 0.6906 \\
3 & 0.7763 & 0.7976 & 0.7552 & 0.6479 \\
4 & 0.9041 & 0.7967 & 0.7445 & 0.7436 \\
5 & 0.8935 & 0.7763 & 0.8186 & 0.7847 \\
6 & 0.8828 & 0.7745 & 0.8818 & 0.7530 \\
7 & 0.8936 & 0.7436 & 0.8714 & 0.7967 \\
8 & 0.8936 & 0.8181 & 0.9139 & 0.8402 \\
9 & 0.9042 & 0.8181 & 0.8918 & 0.8186 \\
10 & 0.9042 & 0.8399 & 0.8818 & 0.8721 \\
11 & 0.9777 & 0.8399 & 0.9033 & 0.8404 \\
12 & 0.9148 & 0.8399 & 0.9023 & 0.8826 \\
13 & 0.9254 & 0.8290 & 0.9023 & 0.9041 \\
14 & 0.9148 & 0.8186 & 0.8813 & 0.8613 \\
15 & 0.9148 & 0.8186 & 0.88066 & 0.8504 \\ \hline \end{tabular}
\end{table}
Table 8: F1-scores across different numbers of features using reduced dataset.