## 5 Results and Discussion
### CAM-Based Interpretability of Spatial Patterns
This observation is consistent with findings in [80], who reported that the effectiveness of Monte Carlo dropout in MI classification tasks may degrade for certain subjects due to inherent EEG data uncertainties. Their study highlights that, although MCD generally improves robustness, it can also reduce classification confidence in cases characterized by high aleatoric variability or atypicalsignal characteristics. This underscores the importance of subject-specific considerations when applying uncertainty-aware regularization strategies in EEG-based deep learning. Figure 12: (**a**) Group I; (**b**) Group II; Comparison of LayerCAM topographic activation maps between standard and MCD-enhanced models. Each subject is represented by a pair of maps: the left shows the standard model, and the right displays the MCD-enhanced version. The maps use a color scale in which yellow indicates high relevance, green denotes moderate relevance, and purple/dark blue – low relevance. Consequently, the application of MCD across all subjects in Group I consistently yields more localized and structured attention maps, enhancing interpretability by emphasizing critical EEG channels and regions, with the theoretical averaging of stochastic forward passes providing stabilized representations of influential features. From a neurophysiological perspective, the focused activations in MCD-regularized models, particularly in the centro-parietal cortex, suggest a potential alignment with known motor imagery-related brain regions, pending expert validation. This spatial refinement boosts classification reliability by reducing dependence on noisy or non-informative signals. It also improves resilience to inter-subject variability and enhances generalization across sessions. This particularly benefits lower-performing subjects by steering models toward task-relevant patterns and mitigating overfitting to individualistic noise. Regarding the worst-performing Group II, Figure 12 (last two rows) presents Layer-CAM topograms for four subjects--Subjects (9, 17, 24, and 2), visualizing spatial attention patterns across _EEGNet_, _MCD-EEGNet_, _ShallowConvNet_, _MCD-ShallowConvNet_, _TCNet_, and _MCD-TCNet_. These maps reveal how MCD regularization reshapes spatial representations underlying classification decisions, offering crucial insights for this lower-performing group. For Subject 24, _EEGNet_ shows diffuse activation across frontal, central, and parietal regions with poorly defined boundaries. In contrast, _MCD-EEGNet_ (23% dropout) produces sharply focused maps with localized peaks in the same regions, suggesting MCD encourages reliance on spatially specific, reliable features while filtering discriminative noise. This pattern of enhanced spatial specificity is similarly observed in Subject 2. The baseline _EEGNet_ displays widespread posterior and frontal attention, while _MCD-EEGNet_ (30% dropout) confines activation primarily to bilateral occipital areas with reduced frontal involvement. This shift indicates MCD prioritizes spatially stable posterior features, acting as a spatial filter that suppresses less relevant regions. The effects of MCD regularization extend beyond _EEGNet_ architectures. Subject 9, analyzed with _TCNet_, shows distinct CP2 and T8 hotspots that become more symmetric and diffuse under _MCD-TCNet_ (5% dropout), demonstrating that even minimal regularization can balance spatial attention and reduce electrode-specific overfitting. Conversely, Subject 17's _ShallowConvNet_ focal activations at Fp1/FC3 and P8/O2 transform into uniformly distributed occipital-temporal patterns under 30% MCD, illustrating how higher dropout rates introduce spatial smoothing and conservative attention reallocation across different model architectures. Consequently, the application of MCD across Group II consistently produces more spatially coherent attention maps, enhancing interpretability through clearer identification of influential EEG channels.