## 1 Introduction  
Pain is a vital warning signal for human survival and adaptation. It represents a multidimensional experience that extends beyond physical reactions; rather, it incorporates emotional, cognitive, and social factors [1, 2]. Recent research has highlighted that pain is not solely a response to physiological stimuli; rather, it is closely intertwined with emotional states [3]. For instance, negative emotions, such as anger or sadness, can increase pain sensitivity and influence one's interpretation of pain experiences [4]. This connection is attributed to the shared neural circuits of pain and emotion (for example, the prefrontal and anterior cingulate cortices), offering neurophysiological evidence that \"emotional pain\" elicits physiological responses similar to those observed in physical pain [5, 6]. From this perspective, investigating the physiological signals of emotionally induced pain represents a critical extension of conventional pain research, which has primarily focused on physical pain elicited by mechanical pressure or thermal stimuli (Table 1) [7; 8; 9; 10; 11; 12]. Notably, most existing studies have sought to classify the presence or intensity of physical pain based on physiological signals [13]. However, only a few investigations have objectively analyzed and classified subjective pain from emotional stimuli. This research gap likely stems from the inherently subjective nature of emotional distress, significant individual differences, and the complex interaction between emotional states and physiological responses. Consequently, a more comprehensive approach is needed to elucidate these complexities. Skin conductance response (SCR) and skin conductance level (SCL) are particularly well suited for this research because they reflect autonomic nervous system (ANS) activity with sensitivity and are widely used to quantify changes in emotional states and pain responses [14; 15; 16]. Psychological metrics--such as intensity, arousal, and valence--allow for the quantitative assessment of subjective experiences associated with emotion-inducing stimuli. Integrating these physiological and psychological data can enhance the accuracy of emotional pain recognition. In addition, machine learning techniques adequately capture complex nonlinear relationships and provide an effective framework for such studies [17; 18]. Furthermore, many studies on pain classification using physiological signals have focused on physical pain; however, research on emotionally induced pain remains in its early stages. Previous studies have demonstrated the utility of physiological signals, such as electrocardiograms (ECG), electromyograms (EMG), electrodermal activity (EDA), and facial expressions, to detect physical pain, often employing machine learning algorithms to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
distinguish no pain from pain or categorize pain intensity (for example, low vs. high and low-medium-high) [17, 19-22]. In addition, these studies used distinct stimuli to evoke varying pain intensities. Instead of assessing the perceived pain from a single stimulus, these studies focused on responses to multiple stimuli designed to elicit different degrees of pain. However, efforts to integrate emotional pain or subjective psychological responses into physiological-signal-based analyses have been limited. Therefore, we aim to examine whether emotional pain states could be quantitatively assessed and classified by integrating physiological signals and psychological indices, focusing on differences across perceived pain intensity levels. Specifically, an anger-inducing video stimulus was used to elicit emotional pain in participants, during which ECG, EDA, respiration (RESP), photoplethysmogram (PPG) readings, and finger temperature (FT) were measured, and self-reported emotional ratings (valence and arousal) were collected. We performed paired _t_-tests and one-way analysis of variance (ANOVA) to examine psychophysiological differences across different pain states.