## 5 Results and Discussion
### Enhanced Consistency of DL Model Performance Using Monte Carlo Dropout
Collectively, these findings position _TCNet_ as a more dependable and adaptable architecture within the context of inter-subject variability and challenging classification scenarios. In Figure 9 (_last row_), the plot illustrates the best-performing model for each subject along with its corresponding dropout rate. For subjects in the group where _FBCSP_ achieved over 70% accuracy, deep learning models--particularly _EEGNet_--consistently outperformed the baseline. _EEGNet_, configured with dropout rates of 5% and 11%, proved especially effective, followed in some cases by the _ShallowConvNet_ model. Notably, _TCNet_ was rarely the top-performing model within this higher-performing cohort. In contrast, for subjects with baseline _FBCSP_ accuracy below 70%, deep learning models showed considerable gains over the baseline. While _EEGNet_ continued to demonstrate reliable performance, _TCNet_ exhibited comparatively stronger results in this lower-performing group, particularly when employing dropout rates of 5%, 11%, or 17%. The _ShallowConvNet_ model also contributed moderate improvements in select instances. Across both performance strata, _EEGNet_ emerged as the most robust and generalizable architecture. Dropout rate selection played a pivotal role in optimizing model performance, with lower rates (5% and 11%) typically yielding higher accuracies. Conversely, higher dropout values (e.g., 30%)  
Figure 9: (**a**) _ShallowConvNet_; (**b**) _EEGNet_; (**c**) _TCNet Fusion_; (**d**) Average; Comparison of DL model performance (_ShallowConvNet_, _EEGNet_, _TCNet Fusion_) versus best classification accuracy across dropout rates in EEG-based motor imagery tasks, with the final row showing the optimal model and dropout rate per subject. A log scale is used on the y-axis to emphasize variability in low-accuracy subjects. were rarely associated with optimal results, suggesting a potential adverse effect on the models' learning capacity. These findings indicate that _EEGNet_, particularly when fine-tuned with appropriate dropout configurations, offers a clear advantage over traditional methods--especially for subjects with weaker baseline performance. Moreover, tailoring model and regularization parameters to individual subject profiles may further enhance classification outcomes. These results empirically validate the theoretical benefits of MCD. The notable performance gains observed in Group II subjects, who are most susceptible to overfitting due to lower signal-to-noise ratios, directly reflect the strong regularization effect of channel dropout. The model is prevented from learning subject-specific noise patterns and instead learns the underlying, generalizable MI-related signals. Finally, to validate the performance improvements from MCD, we conducted a comprehensive statistical analysis of model performance. Paired \(t\)-tests were performed on the classification accuracies across all subjects to compare the different architectural and regularization strategies. The resulting \(p\)-values, presented in the matrix in Figure 10, provide robust insights into the relative efficacy of each approach. The primary and most critical finding is the statistically significant impact of MCD. As shown, the integration of MCD led to a highly significant improvement in accuracy for all three architectures when compared to their respective baselines: _EEGNet_ vs. _MCD-EEGNet_ (\(p=0.0021\)), _ShallowConvNet_ vs. _MCD-ShallowConvNet_ (\(p=0.0013\)), and _TCNet Fusion_ vs. _MCD-TCNet Fusion_ (\(p=0.0008\)). Notably, the most potent effect (lowest \(p\)-value) was observed for _TCNet Fusion_, suggesting that the most complex model benefited the most from the powerful regularization offered by MCD. This provides strong quantitative evidence that our uncertainty-aware strategy yields a significant and consistent performance gain. The \(p\)-value matrix also reveals nuanced relationships between the models. At baseline, the  
Figure 10: \(p\)-value matrix from paired \(t\)-tests.