## 5 Conclusions  
The primary objective of this study was to identify the critical features within eye tracking data that significantly influence predictive modeling in the context of emotional state detection, utilizing XAI methodologies such as SHAP and LIME. The empirical findings from our analysis have highlighted the robustness of the SHAP algorithm in pinpointing pivotal features that considerably affect the model's predictions on a global scale. This capability allows for the development of more streamlined models that not only maintain high accuracy--as evidenced by heightened F1-scores--but also benefit from a reduction in complexity. Concurrently, aggregating LIME's local explanations to derive global insights yields results that substantially align with SHAP. This paper significantly contributes to the ongoing discourse in XAI research, particularly demonstrating how models with a reduced set of features can mitigate overfitting risks and enhance model generalizability. By focusing solely on the most impactful features, these models not only become easier to interpret and understand but also promote greater transparency. This enhanced clarity facilitates deeper trust and accountability from users towards the model, as stakeholders can more readily grasp how decisions are being made. Additionally, the reduced computational demand results in lower operational costs, contributing to more efficient use of resources. Another yet crucial finding of this study relates to the specific eye tracking features--such as blinks, saccades, micro-saccades, and fixations--that have shown significant predictive power regarding the emotional states of individuals. These features provide profound insights into cognitive processes and emotional reactions, underscoring their importance in the development of affective computing systems. For instance, variations in blink duration and saccadic movements were linked to different emotional states across the Circumplex Model of Affect (CMA) quadrants, offering an understanding of how such physiological responses correlate with emotional experiences. \begin{table}
\begin{tabular}{l l l} \hline
**Quadrant** & **Key Features** & **Influence on Model Predictions** \\ \hline \multirow{4}{*}{Quadrant 0} & Number of & These two features are the most frequetly occurring features identified as the most significant on model’s predictions by local explanations of LIME. \\  & Micro-Saccade & Depending on the specific observation, they are indicative for high arousal/positive valence emotions (e.g., happiness, excitement). \\ \hline \multirow{4}{*}{Quadrant 1} & Number of & According to local explanations of LIME, they are the most frequently occurring features for low arousal/positive valence emotions (e.g., relaxation). \\  & Micro-Saccade & Their impact can be positive or negative on model’s decisions depending on the specific observation. \\  & Horizontal Amplitude & These two feature are significant predictors for low arousal/negative valence emotions (e.g., sadness and depression) by LIME. \\ \hline \multirow{4}{*}{Quadrant 2} & Number of & These two feature are significant predictors for low arousal/negative valence emotions (e.g., sadness and depression) by LIME. \\  & Micro-Saccade Vertical & Their impact can be positive or negative on model’s predictions depending on the specific observation. \\  & Amplitude & \multicolumn{1}{l}{} \\ \hline \multirow{4}{*}{Quadrant 3} & \multirow{4}{*}{Blink Duration} & ‘Blink Duration’ and ‘Micro-Saccade Horizontal Amplitude’ are the most frequently occurring features identified the as most significant on model’s decisions by LIME. \\  & Micro-Saccade & Depending on the specific instances of the dataset, they are indicative for high arousal/negative valence emotions (e.g., stress). \\ \hline \end{tabular}
\end{table} 
Table 10: Key findings from the LIME Analysis for reduced features. Our findings indicate that efficient feature selection not only preserves but can enhance the predictive performance of models.