## 5 Results and Discussion
### Enhanced Consistency of DL Model Performance Using Monte Carlo Dropout  
Figure 9 (_first three rows_) presents a comparative analysis of the effects of dropout regularization across multiple DL architectures, stratified by subject performance levels. This evaluation highlights key trends in model robustness and the role of regularization strategies in EEG-based MI classification tasks, while also facilitating uncertainty estimation to enhance model reliability, as discussed in [23]. In particular, the consistency of classification accuracy across varying Monte Carlo dropout rates serves as a critical indicator of each model's resilience to regularization during inference. Among the models examined, _ShallowConvNet_ exhibits moderate stability. While it maintains relatively consistent performance for subjects in Group I--those with higher FBCSP baseline accuracies--more noticeable fluctuations emerge among subjects in Group II, where baseline performance is lower. These variations indicate a degree of sensitivity to the specific dropout rate applied. In contrast, _EEGNet_ demonstrates improved stability, particularly within Group I, where accuracy values are more tightly clustered across dropout conditions. However, some variability remains in Group II, suggesting residual sensitivity in more challenging classification scenarios. Most notably, _TCNet_ shows the highest degree of stability across all tested dropout rates. Its classification accuracy remains remarkably consistent across both subject groups, indicating a performance largely unaffected by changes in dropout rate and suggesting a performance that is less sensitive to changes in the dropout rate. Beyond stability, overall classification accuracy and each model's ability to outperform the FBCSP baseline are essential measures of effectiveness. _ShallowConvNet_ yields competitive results, frequently surpassing the baseline for Group I subjects. However, this advantage tends to diminish in Group II, with several cases where performance falls below the _FBCSP_ benchmark. _EEGNet_ performs strongly in Group I, consistently achieving notable gains over the baseline. In Group II, its results are more variable--while it sometimes improves over the baseline, there are also instances where it fails to do so. In contrast, _TCNet_ achieves the highest overall accuracy across the cohort and most consistently outperforms the FBCSP baseline. Accuracy levels in Group I often approach or exceed 0.9, and in Group II, _TCNet_ typically maintains or modestly surpasses baseline performance. These results indicate a more consistent performance across varying levels of subject difficulty. Figure 9: _Cont._  
Regarding model generalizability, _ShallowConvNet_, while effective in Group I, shows diminished reliability in Group II, where it often fails to exceed the _FBCSP_ baseline. _EEGNet_ follows a similar trend, offering strong performance in Group I but delivering inconsistent results in Group II, with performance sometimes falling short of the baseline. In contrast, _TCNet_ exhibits strong and consistent results across both groups. It maintains high performance in Group I while also producing stable and frequently improved outcomes in Group II, indicating an enhanced ability to extract discriminative features even from lower-quality EEG signals. The comparative analysis clearly identifies _TCNet_ as exhibiting the highest performance stability and consistency across the evaluated dimensions in our study. Its classification performance is highly stable under varying Monte Carlo dropout rates, showing limited sensitivity to regularization during inference. Additionally, its superior overall accuracy and frequent outperformance of the FBCSP baseline--especially in Group II--highlight its robustness to subject heterogeneity and dropout-induced regularization. While _ShallowConvNet_ and _EEGNet_ remain competitive within Group I, their increased performance variability and reduced reliability in Group II limit their applicability in more challenging contexts.