## 4 Results and Discussion
### Line Analysis across Reduced Features  
In this section, we presented the outcomes of applying the LIME algorithm to our reduced dataset, which consists of 15 features. These features are categorized into groups related to blinks, saccades, micro-saccades, and fixations. Like our application of LIME to the full dataset, we noted that LIME's local explanations can vary considerably between observations, highlighting the variability in feature importance depending on the instance being examined.  
Using an aggregation method similar to the one applied to the full dataset, Table 10 demonstrates the principal features identified as the most significant that influence the model's decisions for each quadrant by the local explanations of LIME. A comparative analysis with the SHAP results, as depicted in Table 9, reveals a consistent pattern in the influential features for Quadrants 0 and 2. Similarly, 'Number of Saccade' is identified as the most significant feature for Quadrant 1, while 'Blink Duration' is the predominant factor in Quadrant 3, according to both LIME and SHAP analyses. These findings indicate a substantial agreement between the SHAP and LIME algorithms regarding the key features impacting the model's decisions.  
Furthermore, Table 10 underscores the significance of saccadic eye movements, particularly the 'Number of Micro-Saccade', in influencing the model's decision-making process. This observation is corroborated by the SHAP analysis conducted on the reduced feature set, highlighting the consistent and pivotal role these eye movements play in influencing predictive outcomes.  
It is important to note that LIME, as a local explanation algorithm, provides insights into the model's decision-making process for specific instances rather than offering a comprehensive global perspective. Consequently, while the results discussed above indicate a substantial agreement between LIME and SHAP regarding the influential features, the  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline
**Quadrant 0** & **Key Features** & **Influence on Model Predictions** \\ \hline Quadrant & Number of Micro-Saccade & Lower counts of ‘Number of Micro-Saccade’ generally impact predictions in a positive way. This aligns with decreased micro-saccadic movements, correlating with high arousal/positive valence emotions (e.g., happiness, excitement). & Lower counts of ‘Number of Blink’ also positively impact the model predictions, indicating decreased blinking rate. \\ \hline Quadrant 1 & Number of Micro-Saccade & Lower ‘Number of Micro-Saccade’ values impact the predictions positively for low arousal/positive valence emotions (e.g., relaxation), indicating less rapid eye movements. & Lower ‘Number of Micro-Saccade’ values impact the predictions positively for low arousal/positive valence emotions (e.g., relaxation), indicating less rapid eye movements. \\ \hline Quadrant 2 & Number of Micro-Saccade & Higher values of ‘Number of Micro-Saccade’ is a significant predictor for low arousal/negative valence emotions (e.g., sadness and depression). & Higher counts of micro-saccades and smaller vertical movements are linked to low arousal/negative valence emotions. \\ \hline Quadrant 3 & Blink Duration & Lower values of ‘Max Micro-Saccade Direction’ impact the model positively, demonstrating high arousal/negative valence emotions (e.g., stress). \\ \hline \end{tabular}
\end{table}
Table 9: Key findings from the SHAP analysis for reduced features.  
localized nature of LIME's explanations may not fully capture the overall behavior of the model.