Assessing Feature Importance in Eye-Tracking Data within Virtual Reality Using Explainable Artificial Intelligence Techniques  
[PERSON]  
1 Department of Computer Engineering, Faculty of Engineering, Gazi University, 06570 Ankara, Turkey; [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (M.Y. )2 School of Architecture, Faculty of Built Environment, Tampere University, P.O. Box 600, FI-33014 Tampere, Finland  
2  
[PERSON]  
1 Department of Computer Engineering, Faculty of Engineering, Gazi University, 06570 Ankara, Turkey; [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (M.Y. )2 School of Architecture, Faculty of Built Environment, Tampere University, P.O. Box 600, FI-33014 Tampere, Finland  
2  
[PERSON]  
2  
Footnote 2: email: {[[http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci)]([http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci))}  
Footnote 3: email: {[[https://www.mdpi.com/journals](https://www.mdpi.com/journals)]([https://www.mdpi.com/journals](https://www.mdpi.com/journals))  
Recent advancements in machine learning (ML) and deep learning have significantly enhanced emotion recognition capabilities. These technologies facilitate the development of complex algorithms capable of analyzing diverse data sources to infer and categorize human emotions. Such classification techniques are crucial, as they align with established models of emotion to systematically categorize emotional states, thereby broadening our understanding and application of affective computing. Despite the efficacy of ML techniques, they often need to catch up in terms of interpretability, which is crucial for understanding the outcomes they generate. Interpretability is defined as the extent to which a human can comprehend the reasons behind decisions made by a classifier. Models known as 'glass-box' models, such as linear regression, logistic regression, and decision tree (DT), provide transparency into their operational mechanisms, thereby facilitating human understanding. Conversely, 'black-box' models, which include deep neural networks, ensemble methods like random forests (RF) and gradient boosting machines (GBM), as well as support vector machines (SVM), possess complex internal workings that are less accessible and harder to interpret by humans [8]. In critical domains such as medicine and healthcare research, understanding the rationale behind a model's specific predictions is essential. This necessity extends to business environments, where stakeholders demand clarity on the underlying reasons for each prediction made by machine learning models [9]. To address these challenges, the field of explainable artificial intelligence (XAI) has gained prominence, focusing on making the decision-making processes of opaque 'black-box' models more transparent and comprehensible to human users [10]. Research in this area includes the development of methods like SHapley Additive ExPlanations (SHAP) [11] and local interpretable model agnostic explanations (LIME) [12], which elucidate the contribution of input variables to model predictions. These techniques enhance the interpretability of machine learning models, thereby increasing their transparency and reliability and providing insights into the feature significance that underpins model outputs [13]. Despite significant advances in emotion recognition and the application of XAI to quantify feature importance, there remains a notable gap in integrating these methodologies with established emotion models. This study aims to bridge this gap by leveraging the publicly available VR Eyes: Emotions Dataset (VREED) [14] to detect gaze behaviors associated with various emotional states. By incorporating XAI techniques such as SHAP and LIME in conjunction with the emotion model of the Circumplex Model of Affect (CMA) [15], we systematically assess the contributions of eye-tracking features--saccades, micro-saccades, blinks, and fixations--and their interactions in understanding human affect.