## 6 Concluding Remarks  
_Accuracy of Subject-grouped MI Responses._ Three DL architectures are explored in MI classification, each with distinct strengths: _ShallowConvNet_ is simpler and often effective with smaller datasets, _EEGNet_ offers efficient parameterization, and _TCNet Fusion_ excels at capturing complex temporal dependencies. While DL models--particularly _TCNet Fusion_--show superior performance in moderate- to high-density montages (16-32 channels), their effectiveness varies across subjects. In about 30% of cases, simpler methods like _FBCSP_ outperform DL models, highlighting inter-subject heterogeneity in EEG data, as also noted in [38]. Among the evaluated models, _ShallowConvNet_ achieves the lowest peak accuracy (0.727) but exhibits minimal performance loss (2.7%) under montage reduction, reflecting robustness in low-density setups. _EEGNet_ balances accuracy (0.737 peak) and stability (3.1% drop), while _TCNet__Fusion_ reaches the highest accuracy (0.744) with a modest 4.4% drop, indicating adaptability to varying data quality. Sparse montages (8-16 channels) offer practical advantages for portable systems and reduce performance variability across models. These findings emphasize the need for adaptive strategies--such as subject-specific fine-tuning [83] or hybrid ensembles [84]--to ensure reliable EEG classification in real-world applications. _Improving DL Model Consistency with Monte Carlo Dropout._ The obtained results underscore the critical role of dropout regularization in enhancing the stability and generalization of DL models for EEG-based MI classification. While _TCNet_ demonstrates a high degree of robustness to dropout variation and consistent performance across diverse subject groups, _EEGNet_ emerges as the most flexible and broadly effective architecture--particularly when configured with optimized dropout rates. These findings reinforce the importance of dropout regularization in stabilizing and generalizing DL models for EEG applications, as also reported by [34], which examines the impact of various regularization techniques--including dropout--on deep learning architectures, revealing that dropout consistently outperforms L2 regularization and data augmentation across different dataset sizes. These results suggest that complex architectures can be made more robust through appropriate regularization, which suggests a potential to improve their applicability to a wider range of EEG signal qualities, not just high-quality recordings. Moreover, successful MI deployment requires not only sophisticated architectures but also careful consideration of regularization strategies capable of adapting to individual subject characteristics and signal quality variations. Collectively, these findings highlight the value of model-specific regularization tuning and underscore the need for subject-aware design strategies to ensure reliable deployment in heterogeneous EEG contexts. _CAM-Based Interpretability of Spatial Patterns._ The LayerCAM analyses across both groups demonstrate that Monte Carlo dropout consistently refines spatial attention patterns in EEG-based deep learning models, regardless of performance level or architecture. MCD regularization consistently transforms diffuse activations into more spatially focused and coherent representations, manifesting as enhanced centro-parietal localization in Group I and improved spatial stability in posterior-frontal areas for Group II. This suggests MCD acts as an adaptive spatial filter that emphasizes task-relevant neural signatures while suppressing spurious activations. The optimal dropout rates vary significantly between subjects (5-30%), indicating that MCD effectiveness requires careful subject-specific tuning. Moderate dropout rates preserve focal specificity while attenuating noise, whereas higher rates introduce beneficial spatial smoothing at the potential cost of over-regularization.