## 2 Literature Review
### Explainable Artificial Intelligence (XAI)  
XAI has emerged as a crucial research field with the objective of demystifying the complex operations and decision-making processes of AI models, making them transpar  
Figure 1: The Circumplex Model of Affect [34].  
ent and understandable for human users. This is especially important in the healthcare sector, where the reliability of ML methods for diagnostic and treatment strategies is paramount [38].  
XAI methodologies can be categorized based on their agnosticity to models and the scope of their explanations [39]. These methods are divided into model-agnostic and model-specific categories. Model-agnostic approaches, such as SHapley Additive ExPlanations (SHAP) and local interpretable model agnostic explanations (LIME), are versatile and applicable across various types of machine learning models, regardless of their underlying architecture. In contrast, model-specific methods like layer-wise relevance propagation (LRP) [40] and integrated gradients (IntGrad) [41] are designed to work with specific models, providing insights tailored to the unique characteristics of those models.  
Additionally, the scope of explanation in XAI methods can be classified as either local or global. Local explanations focus on individual instances or records, clarifying why a model made a particular decision in a specific case. Conversely, global explanations aim to illuminate the overall behavior and decision-making process of a model across all instances. This distinction is crucial for users who need to understand whether the explanations reflect isolated cases or generalize across the entire model.  
[PERSON] et al. [42] have developed a deep-learning-based EEG emotion recognition framework that utilizes XAI to assess the impact of individual features on prediction outcomes, focusing mainly on SHAP values. Similarly, [PERSON] et al. [43] employ SHAP to elucidate significant features across various modalities--audio, video, and text--in multimodal emotion recognition. [PERSON] et al. [44] delve into the interpretability of deep neural networks for EEG-based emotion recognition in autism, analyzing the reliability of various relevance mapping techniques such as LRP, PatternNet, pattern attribution, and smooth-grad squared. In a related study, [PERSON] et al. [45] compare multiple explainable models, including Grad CAM and LIME, for speech emotion recognition.  
[PERSON] et al. [46] applied SHAP values to map physiological features onto a binary classification system of arousal and valence using ECG data, highlighting the role of individual features and their interactions in describing emotions. [PERSON] et al. [47] propose an interpretable classification framework with XGBoost for emotion recognition, utilizing food images and EEG data within an arousal-valence model to provide both local and global interpretations through SHAP values.  
Interpretability in emotion recognition models is crucial, as it allows researchers to understand which features or signals contribute most significantly to the classification of emotions, thereby validating the model's decisions and ensuring its reliability across different datasets. In this study, we examined the impact of applying different techniques, namely SHAP and LIME, sequentially on the model's behavior. By employing SHAP, we assessed the global feature importance to understand the overall behavior of the model. Subsequently, we employed LIME to analyze the local explanations for individual predictions. By aggregating these local explanations, we gained valuable insights into the model's general behavior. We then compared these findings with the results obtained from SHAP to evaluate the consistency and robustness of the explanations provided by both methods.