## 4 Results and Discussion
### Shap Analysis Across Reduced Features  
#### 4.4.1 Results for Quadrant 0  
After the application of the ET classifier on the reduced features, we employed the SHAP algorithm to generate visual representations of the influence exerted by these 15 features for Quadrant 0 of the CMA. These influences are depicted in the summary plot shown in Figure 10.  
The results highlight that the 'Number of Micro-Saccade' plays a significant role in influencing the model's predictions. The analysis shows that lower values of 'Number of Micro-Saccade' generally correspond to positive SHAP values, suggesting a positive impact on the model's output, aligned with the decrease in micro-saccadic movements observed when individuals are concentrated and emotionally involved. Similarly, low values of 'Micro-Saccade Peak Velocity', referring to the highest speed reached by micro-saccades during their rapid movements, impact the model's decisions in a positive way. This means that individuals tend to make slower eye movements when they experience the emotional states typical of this quadrant. On the other hand, other micro-saccade-related features have an inconsistent impact on the model output. The figure also shows that a decreased 'Number of Blink' impacts the model in a positive way by causing higher SHAP values. Similarly, lower values for 'Blink Duration' also result in higher SHAP values. This suggests a positive influence on the model's predictions for emotions typical of Quadrant 0, which include happiness, excitement, and alertness. These results correlate with the situation when all 49 features were included in the predictions. Similarly, a lower 'Number of Fixations' influences the model positively, indicating that less frequent eye movements are linked to positive emotional states represented in quadrant 0. The 'Fixation Duration' feature has a mixed impact, indicating an impact in both ways that lower values of 'Fixation Duration' result in both positive and negative SHAP values. Although these results show that individuals who experience high arousal \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Quadrant 0** & **Key Features** & **Influence on Model Predictions** \\ \hline Quadrant & SD Micro-Saccade Peak Velocity & These two features are the most frequently occurring features identified as the most significant on model’s predictions by local explanations of LIME. Depending on the individual instances, they are indicative for high arousal/positive valence emotions (e.g., happiness, excitement). \\ \hline Quadrant 1 & Max Saccade Direction & These features are the most frequently occurring features identified as the most significant on model’s predictions for emotions characterized by low arousal/positive valence (e.g., relaxation) according to LIME. \\ \multicolumn{2}{p{85.4 pt}}{Number of Micro-Saccade} & This impact can be both positive or negative depending on the specific instance of the dataset. \\ \hline Quadrant 2 & Number of Micro-Saccade & Different values of these features, whether lower or higher depending on the instance, signify emotions associated with low arousal/negative valence emotions (e.g., sadness). \\ \multicolumn{2}{p{85.4 pt}}{SD Micro-Saccade Peak Velocity} & These two features are the most influential features by local explanations of LIME. \\ \hline Quadrant 3 & Max Micro-Saccade Direction & According to local explanations of LIME, these two features are the most frequently occurring explanations for emotions characterized by high arousal/negative valence (e.g., stress). \\ \multicolumn{2}{p{85.4 pt}}{SD Blink Duration} & Their impact can be both positive or negative depending on the specific instance of the dataset. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Key findings from the LIME analysis across all features. and positive emotional states tend to show shorter fixation durations, this aligns with the exploratory and dynamic attentional styles of individuals.