## 4 Results and Discussion
### Time Analysis across 49 Features  
In this section, we present the results of LIME applied to our dataset, comprising 49 features. LIME offers valuable insights into the local interpretability of machine learning models by generating explanations for individual predictions. By applying LIME to each instance within our dataset, we aimed to uncover specific insights into how these features influence model predictions across diverse instances. This local focus allows us to see how the influence of a feature can vary from one instance to another, providing a nuanced understanding of the model's behavior in specific contexts. We observed that LIME's local explanations can significantly differ from one observation to the next, highlighting the variability in feature importance depending on the instance being examined.  
Then, we aggregated these local explanations to derive insights into the model's overall behavior to be able to compare with the results of SHAP. For this purpose, we identified the most significant feature for each instance within our testing data and subsequently calculated the two most frequently occurring features for each quadrant. Table 7 shows the most influential features for each quadrant when LIME is applied.  
Comparing these findings from LIME with the SHAP results in Table 6, we note that 'SD Micro-Saccade Peak Velocity' in Quadrant 1, 'Number of Micro-Saccade' in Quadrant 2, and 'Max Micro-Saccade Direction' in Quadrant 3 emerge as the most influential features across both algorithms. This comparison highlights the consistent significance of certain features across both LIME and SHAP in different quadrants of the dataset. In contrast, the most influential features in Quadrant 2 are distinct and unrelated to each other. These results demonstrate that while both SHAP and LIME are effective tools in XAI and sometimes produce similar results, their different methodologies, scopes, and applications can lead to varied outcomes.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Quadrant** & **Key Features** & **Influence on Model Predictions** \\ \hline Quadrant 0 & SD Micro Saccade Peak Velocity & Higher values of ‘SD Micro Saccade Peak Velocity’ positively impact predictions, correlating with high arousal/positive valence emotions (e.g., happiness, excitement). \\ \cline{3-3}  & SD Saccade Amplitude & Lower values of ‘SD Saccade Amplitude’ also positively impact the model predictions, indicating smaller saccadic movements. \\ \hline Quadrant 1 & Max Saccade Length & Higher ‘Max Saccade Length’ are crucial for predicting low arousal/positive valence emotions (e.g., relaxation). \\ \cline{3-3}  & SD Saccade Amplitude & Longer saccades may indicate a state of calmness or relaxation. \\ \hline Quadrant 2 & Number of Micro-Saccade Micro-Saccade & ‘Number of Micro-Saccade’ is a significant predictor for low arousal/negative valence emotions (e.g., sadness). Higher values for ‘Skew Saccade Duration’ positively affect the model, indicating longer saccade durations linked to low arousal/negative valence. \\ \hline Quadrant 3 & Mean Blink Duration & Lower values of ‘Max Micro-Saccade Direction’ impact the model positively, demonstrating high arousal/negative valence emotions (e.g., stress). \\ \cline{3-3}  & Mean Blink Duration & Higher values of ‘Mean Blink Duration’ have a positive impact on model predictions, indicating that individuals tend to blink longer durations when they are stressed. \\ \hline \end{tabular}
\end{table}
Table 6: Key findings from the SHAP analysis for all features.