## 1 Introduction
To overcome these limitations and address the \"black-box\" nature of deep learning, researchers have turned to class activation maps (CAMs), particularly LayerCAM. This visualization technique functions like a relevance heatmap for the brain, overlaying a map on the EEG sensor layout that highlights exactly which regions and channels were most influential in the model's decision-making process [29]. While previous research has separately addressed either the robustness or interpretability of MI classifiers, a unified framework that enhances both simultaneously is still lacking, yet essential for practical BCI systems. Integrating channel dropout with CAMs offers a synergistic approach, where dropout boosts robustness and CAMs refine interpretations of critical channels and dynamics, especially in low signal-to-noise ratio or sparse montage scenarios [30]. However, the ultimate challenge that both channel dropout and CAMs must collectively address is the substantial subject variability that characterizes MI-based BCI systems. This variability manifests through differences in brain anatomy, cognitive strategies, and neural response patterns, creating a complex landscape where the non-stationarity and inherent variability of EEG signals across subjects obscure physiologically meaningful spatiotemporal features [31]. Consequently, diffuse activations complicate identifying task-relevant cortical regions, such as the primary motor cortex, while montage variability and limited resolution hinder generalization. Traditional methods like common spatial patterns (CSP) offer interpretability but are sensitive to noise and session variability [32]. To address this multi-faceted challenge of subject variability, the combined application of channel dropout and CAMs offers a more comprehensive solution than either approach in isolation. By providing robust feature extraction through channel dropout while maintaining interpretability through CAM visualization, this integrated approach enables researchers to understand how different subjects engage distinct neural patterns during MI tasks. This understanding proves crucial for developing subject-specific adaptations and optimized montage designs that ensure reliable classification across diverse user populations. Recent explainable AI approaches have further enhanced this integration by leveraging spectral and spatial features, validated against neurophysiological data, to balance performance and interpretability [33]. Additionally, hybrid regularization techniques that integrate dropout with batch normalization and data augmentation offer additional pathways for improvement, emphasizing the need for adaptive, subject-aware tuning to ensure robust MI classification [34]. Despite these promising developments, the combined use of channel dropout and CAM to address spatiotemporal uncertainty in MI classification remains underexplored, particularly in low-performing subjects where interpretability becomes critical for practical BCI deployment. Moving forward, this integrated approach represents a unified framework for achieving robust, interpretable MI classification that effectively addresses the interconnected challenges of spatiotemporal uncertainty, montage variability, and subject heterogeneity in real-world BCI applications. This paper proposes a novel uncertainty-aware deep learning framework for MI-EEG classification, aiming to simultaneously enhance model robustness and interpretability, particularly in low-performing subjects. In this sense, a channel-wise Monte Carlo dropout (MCD) and LayerCAM-based spatial interpretability are integrated, a combination not previously explored in the MI-EEG domain. This unified framework enables the dynamic modeling of spatiotemporal uncertainty and yields neurophysiologically meaningful insights by refining spatial attention patterns.