## 5 Limitations  
This study has some limitations. First, the small sample size for certain pain intensity levels, particularly the 17 participants that reported intensity 5, poses significant challenges to machine learning classification. Techniques such as SMOTE-based oversampling and repeated nested CV were used to mitigate the small sample size and data imbalance; however, the large difference (>0.1) between the training and test F1 scores in the intensity 5 classification indicated potential overfitting. SMOTE helped balance the classes during training, but did not fully resolve the fundamental issue of having too few samples for certain categories. These results underscore how the underrepresentation of certain intensities can reduce model reliability and interpretability.  
Second, this study focuses on static snapshots of physiological responses rather than time-varying analyses, potentially overlooking the dynamic progression of emotional pain. In the present study, physiological features were extracted from a fixed 30 s epoch. This window was chosen to balance the requirements of multimodal data: rapidly changing signals such as SCR and RR benefit from shorter windows, whereas HRV requires at least ~30 s for reliable estimation. A 30 s segment therefore provided a practical compromise, and similar durations are commonly used in affective physiology research [32]. Nonetheless, we acknowledge that a static window may fail to capture the full temporal dynamics of time-sensitive measures like SCR and RR, potentially obscuring discriminative information. In future work, we will employ time-series approaches--such as sliding-window analysis or sequence models--to track these rapid fluctuations more precisely and to test whether dynamic features improve classification performance.  
Third, pain intensity labels were derived solely from participants' self-ratings, reflecting the study's aim to capture subjective experience despite identical stimuli. While appropriate for this purpose, such ratings inevitably introduce interpretive challenges, owing to their inherent subjectivity. To enhance objectivity in future work, we plan to integrate additional modalities--automated facial expression analysis, clinician or observer ratings, and other behavioral cues--alongside physiological signals and self-reports. This multimodal approach should yield a more comprehensive and reliable assessment of pain intensity.  
Finally, the scope of the emotional pain stimuli in this study was relatively narrow (primarily anger). This approach is limited in that it does not fully capture the multifaceted nature of emotional pain. Emotional pain encompasses a range of negative affective experiences, including sadness, fear, social rejection, shame, and loneliness, each of which may differentially influence ANS responses and pain perception. For example, social exclusion can heighten sympathetic activation, altering physiological measures such as SC, while feelings of helplessness may increase perceived pain sensitivity by evoking a sense of lost control over the stimulus. Such psychosocial variables may interact with physiological responses and subjective pain reports, thereby affecting the performance of our pain classification models. In future work, we plan to integrate a broader array of emotional stimuli and psychosocial factors to better reflect the heterogeneity and complexity of emotional pain. We will also employ validated instruments--such as the Orbach & Mikulincer Mental Pain Scale [48]--to systematically assess emotional pain and combine these measures with physiological data, enabling the development of more robust and generalizable pain classification models.  
Moving forward, we intend to recruit more participants, refine the stimulus protocol to capture multiple pain intensities more uniformly, and explore dynamic modeling approaches that account for the temporal evolution of emotional pain responses.