## 5 Conclusions
For example, reducing the number of features to 9 or 19, from an initial set of 49, retained high levels of accuracy, thereby underscoring the effectiveness of targeted feature selection in XAI implementations. Furthermore, our analysis reveals that saccadic eye movements and blinking are the most crucial main eye features. In addition to these findings, it is important to acknowledge that human data is inherently subject to various biases and pre-existing emotional states. Such biases can stem from individual differences in emotional baseline levels, past experiences, and personal predispositions, all of which can influence eye tracking metrics. For example, an individual's current mood or stress level might affect their saccadic movements or blink rate, potentially introducing variability in the data. Recognizing and accounting for these biases is essential for developing more accurate and generalizable predictive models. Finally, this study not only advances the field of affective computing by integrating eye tracking metrics with advanced XAI techniques but also sets a benchmark for developing cost-effective, accurate, and user-trustworthy predictive models. Future research on this topic could expand this study to include multi-class classification across the four quadrants of CMA, offering further improvements in model performance and interpretability. Moreover, extending the dataset with additional physiological signals, namely, ECG and GSR data, could provide a more holistic view of the emotional landscape and improve the performance of emotion detection models. Conceptualization, M.B. and M.Y. ; formal analysis, M.B. ; investigation, M.B. ; data curation, M.Y. ; methodology, M.B. and M.Y. ; project administration, H.E.I. ; resources, H.E.I. ; software, H.E.I. ; supervision, M.Y. ; validation, H.E.I. ; visualization, M.B. ; writing--original draft, M.B. ; writing--review and editing, M.Y. and H.E.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The dataset utilized in this study were sourced from the publicly available VREED (VR Eyes: Emotions Dataset) at [[https://www.kaggle.com/datasets/lumaatabbaa/vr-eyes-emotions-dataset-vreed](https://www.kaggle.com/datasets/lumaatabbaa/vr-eyes-emotions-dataset-vreed)]([https://www.kaggle.com/datasets/lumaatabbaa/vr-eyes-emotions-dataset-vreed](https://www.kaggle.com/datasets/lumaatabbaa/vr-eyes-emotions-dataset-vreed)) (accessed on 1 July 2024). The relevant codes for this research are available upon request. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} VREED & VR Eyes: Emotions Dataset \\ SHAP & SHapley Additive exPlanations \\ LIME & Local Interpretable Model Agnostic Explanations \\ LRP & Layer-wise Relevance Propagation \\ IntGrad & Integrated Gradients \\ SKT & Skin temperature \\ BVP & Blood volume pulse \\ WPS & Wrist pulse signal \\ EEG & Electroencephalogram \\ ECG & Electrocardiogram \\ EMG & Electromyogram \\ GSR & Galvanic skin response \\ VR & Virtual reality \\ CMA & Circumplex Model of Affect \\ CNN & Convolutional Neural Network \\ \end{tabular}