## 4 Results and Discussion
### Shap Analysis across 49 Features  
#### 4.2.1 Results for Quadrant 0  
After the application of the ET Classifier, we employed the SHAP algorithm to generate visual representations of the influence exerted by all 49 features in Quadrant 0 of the CMA. These influences are depicted in the summary plots shown in Figure 2, which arranges the features according to their importance to the model. The results highlight that the 'SD Micro-Saccade Peak Velocity' plays a significant role in influencing the model's predictions. Other features are presented in descending order of importance. In Figure 2, the y-axis lists the feature names, arranged from the most to the least important from top to bottom. The x-axis displays the SHAP values, which indicate the degree of change in the model's log odds prediction. The color of each dot on the plot represents the value of the corresponding feature, with red indicating high values and blue indicating low values. Each point on the graph represents an observation from the dataset. The analysis shows that high values of 'SD Micro-Saccade Peak Velocity' correspond to positive SHAP values, suggesting a positive impact on the model's output. Conversely, lower values of 'SD Saccade Amplitude' are associated with positive SHAP values, indicating that these lower values positively impact the model's predictions. A detailed examination of features associated with blinking reveals that lower values in 'Skew Blink Duration', 'Number of Blink', 'Max Blink Duration', 'SD Blink Duration', \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CMA** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1-Score** \\
**Quadrant** & & & & & \\ \hline
0 & 0.9361 & 0.9369 & 0.9555 & 0.9148 & 0.9361 \\
1 & 0.9148 & 0.9090 & 1.0000 & 0.8620 & 0.9129 \\
2 & 0.9574 & 0.9571 & 0.9607 & 0.9607 & 0.9571 \\
3 & 0.9042 & 0.9045 & 0.9000 & 0.9183 & 0.9039 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for the extra trees classifier for each quadrant individually with all 49 features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CMA** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1-Score** \\
**Quadrant** & & & & & \\ \hline
0 & 0.9148 & 0.9156 & 0.9333 & 0.8936 & 0.9148 \\
1 & 0.8191 & 0.8190 & 0.8200 & 0.8367 & 0.8186 \\
2 & 0.8829 & 0.8775 & 0.9411 & 0.8571 & 0.8806 \\
3 & 0.8510 & 0.8504 & 0.8600 & 0.8600 & 0.8504 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the extra trees classifier for each quadrant individually for reduced features after PCA. and 'Mean Blink Duration' are correlated with higher SHAP values. This suggests a positive influence on the model's predictions for emotions typical of Quadrant 0, which includes high-arousal/positive-valence emotional states like happiness, excitement, and alertness. Indeed, high arousal states are associated with decreased blinking. Generally, decreased blinking may indicate that an individual is engrossed in activities that require visual attention, such as reading intensely, watching an engaging movie, or participating in stimulating conversations. Similarly, features related to fixation durations--specifically 'First Fixation Duration', 'Mean Fixation Duration', and 'SD Fixation Duration'--show that higher values positively impact the model, while lower values of 'Skew Fixation Duration', 'Max Fixation Duration', and 'Number of Fixations' have a positive impact. Although the results show some uncertainty, this aligns with observations that individuals experiencing high arousal and positive emotional states, such as during enjoyable activities, tend to show longer fixation durations due to both increased attention and positive emotional engagement. In addition to that, individuals experiencing positive emotions often show an exploratory attentional style with frequent gaze shifts, resulting in shorter fixation durations.