## 4 Results and Discussion
### Cross-Subject Method Comparison Results
The figure highlights an expected drop in ACC when transitioning to subject-independent evaluation, as models are tested on previously unseen subjects. Despite this decline, the models, particularly KCS-FCnet, maintain strong performance, demonstrating their ability to generalize effectively to unseen data. Although overall performance was slightly reduced in the female subgroup, KCS-FCnet consistently outperformed all other models, as shown in Figure 11. This highlights its robust generalization capabilities, particularly for pain detection in previously unseen subjects--a critical requirement for reliable assessment in non-verbal patient populations. These results underscore the model's potential for real-world, subject-independent applications. Notably, KCS-FCnet exhibited particularly strong performance in the male group, achieving the highest scores across all evaluation metrics, further reinforcing its effectiveness and robustness across diverse demographic profiles. Lastly, recent advances in EEG-based pain detection and assessment have demonstrated the utility of both subject-dependent and subject-independent (LOSO) approaches, yet challenges in generalization and interpretability persist. For instance, in [40] authors proposed an EEG-based method for acute pain detection using a compact CNN, achieving strong performance in subject-dependent tasks, but reporting notable performance degradation in cross-subject settings due to individual variability. Similarly, authors in [8] highlighted the potential of functional connectivity metrics in pain quantification, yet emphasized their sensitivity to demographic and neurophysiological differences, limiting subject-independent applicability. In contrast, in [7], a deep attentive recurrent convolutional network is proposed to encode spatial, spectral, and temporal EEG features to assess pain intensity, achieving promising results under controlled subject-specific evaluations. Most recently, authors in [52] leveraged deep learning within a BCI framework to enable objective pain assessment in real-time, noting, however, that their subject-independent results still suffered from substantial inter-subject variability. Compared to these studies, our proposed KCS-FCnet framework integrates kernel-based functional connectivity, \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Approach** & **Accuracy** & **Kappa** & **AUC** \\ \hline FBCSP [45] & 57.6 \(\pm\) 7.8 & 15.3 \(\pm\) 15.6 & 57.7 \(\pm\) 7.8 \\ EEGNet [46] & 77.7 \(\pm\) 3.7 & 55.5 \(\pm\) 7.5 & 86.5 \(\pm\) 3.7 \\ DeepConvNet [47] & 58.2 \(\pm\) 8.5 & 16.4 \(\pm\) 17.1 & 76.9 \(\pm\) 9.6 \\ ShallowConvNet [48] & 70.6 \(\pm\) 8.0 & 41.1 \(\pm\) 16.0 & 85.9 \(\pm\) 4.2 \\ TCFusionnet [49] & 81.0 \(\pm\) 17.1 & 62.0 \(\pm\) 34.1 & 93.0 \(\pm\) 5.6 \\ KCS-FCnet (ours) & 80.8 \(\pm\) 6.4 & 61.6 \(\pm\) 12.8 & 90.8 \(\pm\) 7.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Male cross-subject pain detection results. A LOSO cross-validation is carried out. Average ACC and standard deviation are presented. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Approach** & **Accuracy** & **Kappa** & **AUC** \\ \hline FBCSP [45] & 62.5 \(\pm\) 10.1 & 25.0 \(\pm\) 20.2 & 62.5 \(\pm\) 10.1 \\ EEGNet [46] & 63.3 \(\pm\) 10.6 & 26.6 \(\pm\) 21.1 & 78.3 \(\pm\) 17.9 \\ DeepConvNet [47] & 56.2 \(\pm\) 5.9 & 12.5 \(\pm\) 11.8 & 72.9 \(\pm\) 11.6 \\ ShallowConvNet [48] & 67.1 \(\pm\) 10.4 & 34.2 \(\pm\) 20.1 & 81.1 \(\pm\) 10.7 \\ TCFusionnet [49] & 64.6 \(\pm\) 14.7 & 29.2 \(\pm\) 29.3 & 79.6 \(\pm\) 14.8 \\ KCS-FCnet (ours) & 67.5 \(\pm\) 14.0 & 35.0 \(\pm\) 28.1 & 80.9 \(\pm\) 18.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Female cross-subject pain detection results. A LOSO cross-validation is carried out. Average ACC and standard deviation are presented. demonstrating superior subject-independent performance, especially when stratifying by sex. Figure 11: Females’ group accuracy comparison within the subject-dependent and cross-subject pain detection experiments.