## 4 Results and Discussion  
### Subject-Dependent Method Comparison Results  
Figures 6 and 7 and Table 4 present a comparative evaluation of subject-dependent pain detection performance across multiple machine learning and deep learning models. Among these, the proposed KCS-FCnet model consistently outperforms all competing methods, achieving the highest average accuracy (76.4%), AUC (81.2%), and Cohen's Kappa (52.8%). This strong performance highlights the model's capacity to reliably differentiate between no-pain and high-pain EEG trials at the individual level. In comparison, traditional machine learning approaches such as Power Features + SVM and FBCSP yield substantially lower accuracies (56.5% and 53.0%, respectively), reinforcing the limitations of handcrafted features in capturing the complex neural signatures associated with pain. Also, TCFusionNet, the second-best performer, trails KCS-FCnet by \(\sim\)8% in accuracy and \(\sim\)4% in AUC, indicating that while temporal-convolutional fusion enhances spatio-temporal modeling, it does not match the pairwise channel dependency modeling capabilities of KCS-FCnet. In contrast, EEGNet and ShallowConvNet, despite being optimized for EEG data, show lower performance--likely due to their limited modeling of nonlinear inter-channel relationships. Their focus on spatial-temporal convolution, without incorporating functional connectivity, restricts their ability to capture pain-related brain dynamics. Notably, even established deep learning models like EEGNet and DeepConvNet fall short, suggesting that their architectures are insufficient to capture the intricate spatio-frequency dependencies encoded in pain-related EEG signals. Importantly, a paired two-tailed _t_-test of  
Figure 5: Proposed pipeline for subject-dependent and cross-subject pain detection testing.  
the subject-dependent accuracies (Figure 7) supports our conclusions. KCS-FCnet achieves statistically superior performance over all other methods (\(p<0.05\)), while the differences between DeepConvNet vs. FB-CSP and ShallowConvNet vs. EEGNet are not significant (\(p>0.05\)).  
Moreover, KCS-FCnet's superior results can be attributed to its integration of kernel-based functional connectivity analysis with convolutional neural feature extraction. By modeling nonlinear inter-channel relationships through Gaussian kernel similarity measures, the model effectively uncovers pain biomarkers that traditional and even some advanced deep learning models overlook. Furthermore, its relatively low standard deviation in accuracy (\(\pm 9.1\%\)) indicates strong performance consistency across subjects, which is critical in clinical applications where subject-specific variability often hampers generalization. Collectively, these findings underscore the potential of KCS-FCnet as a robust, interpretable, and individualized framework for EEG-based pain detection--particularly valuable for non-verbal patients where objective assessment is essential.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Approach** & **Accuracy** & **Kappa** & **AUC** \\ \hline Power features + SVM [40] & \(56.5\pm 7.5\) & \(-\pm-\) & \(-\pm-\) \\ FBCSP [45] & \(53.0\pm 3.8\) & \(5.9\pm 7.6\) & \(53.0\pm 3.8\) \\ EEGNet [46] & \(62.5\pm 9.9\) & \(24.9\pm 19.7\) & \(64.6\pm 14.7\) \\ DeepConvNet [47] & \(52.4\pm 8.2\) & \(4.9\pm 16.3\) & \(60.1\pm 14.4\) \\ ShallowConvNet [48] & \(61.8\pm 10.5\) & \(23.5\pm 21.1\) & \(66.9\pm 13.5\) \\ TCFusionnet [49] & \(68.6\pm 10.6\) & \(37.2\pm 21.2\) & \(77.3\pm 13.8\) \\ KCS-FCnet (ours) & \(76.4\pm 9.1\) & \(52.8\pm 18.1\) & \(81.2\pm 11.5\) \\ \hline \end{tabular}
\end{table}
Table 4: Average subject-dependent pain detection performance. Standard deviation is also included. \(-\) stands for not provided.  
Figure 6: Subject-dependent pain detection results. An accuracy comparison between FBCSP, EEGNet, and KCS-FCnet (ours) is presented. Subjects are sorted regarding the KCS-FCnet performance.