## 5 Results and Discussion
### CAM-Based Interpretability of Spatial Patterns  
To enhance model interpretability, we utilize LayerCAM to generate topographic maps (topograms) that visualize the spatial activation patterns learned by deep learning models. LayerCAM highlights the input regions that most strongly influence the model's predictions, providing valuable insights into the spatial representations underlying its classification decisions. This approach aligns with recent findings by [PERSON] et al. [79], who evaluated multiple interpretability techniques for EEG-based BCIs and emphasized the importance of selecting model-specific interpretation methods tailored to the structural and data characteristics of each model. Their results demonstrate that class activation mapping (CAM)-based approaches can meaningfully enhance the interpretability of MI classifiers, particularly when adapted to the architecture and signal variability inherent in EEG data. Having established model performance across channel montages, we next examine how Monte Carlo dropout enhances consistency and uncertainty estimation in these frameworks. Figure 12 (first two rows) presents LayerCAM-generated topographic activation maps (topograms) for four subjects--Subjects 43, 20, 5, and 23--belonging to Group I. These maps visualize the spatial attention patterns learned by various deep learning models, namely _EEGNet_, _MCD-EEGNet_, _ShallowConvNet_, and _MCD-ShallowConvNet_. By identifying the input regions most influential in the models' predictions, LayerCAM offers insights into how MCD regularization shapes the spatial representations underlying classification decisions. This figure enables both subject-specific and model-specific analyses of interpretability and neural encoding, particularly in cases where classification performance is suboptimal. In this case, the LayerCAM topograms for each subject were generated by averaging the activation maps across all correctly classified trials belonging to a specific MI class (e.g., left-hand imagery). This aggregation strategy ensures that the resulting maps represent stable, class-discriminative spatial patterns learned by the model for that subject, effectively reducing trial-to-trial noise and highlighting consistent areas of focus. For the best-performing individual (Subject 43), the comparison between _EEGNet_ and _MCD-EEGNet_ with an 11% dropout rate reveals a shift from broadly distributed activation across frontal and centro-parietal areas to a more localized and sharply defined pattern within the same regions. This suggests that MCD encourages the model to focus more precisely on task-relevant spatial features. A similar effect is observed for Subject 20 (the last individual of Group I), where _EEGNet_ displays widespread attention across frontal, temporal, and parietal regions, whereas _MCD-EEGNet_ at the same dropout level yields more compact and distinct activations, particularly in frontal and temporal zones. In both cases, MCD enhances spatial specificity while preserving core regions of interest. Subject 5 is also analyzed, as he benefits the most from _MCD-ShallowConvNet_, configured with a relatively high dropout rate (30%). The baseline _ShallowConvNet_ model emphasizes posterior regions--particularly occipital and parietal areas--with additional activation in frontal regions. After applying MCD, attention remains concentrated in the posterior and centro-parietal areas but becomes more refined and less diffuse, with reduced frontal activation. This suggests that MCD acts as a spatial filter, suppressing less relevant features while enhancing more discriminative patterns. By contrast, Subject 23--who appears more affected in Group I by MCD--illustrates that even minimal regularization can influence spatial attention. A lower dropout rate (5%) applied to _EEGNet_ results in slightly more focused and stable activations in the central regions and an increase in right-temporal area, despite the overall similarity to the baseline _EEGNet_ map.