## 1 Introduction  
The prospect of translating thoughts into actions via brain-computer interfaces (BCIs) has captivated neuroscience for decades, offering the potential to restore function and communication to individuals with severe motor disabilities [1,2]. This vision was transformed into reality through a series of landmark studies that demonstrated real-time neural control of external devices, first in primates [3,4] and, pivotally, in humans with tetraplegia using intracortical recordings [5,6]. While much of this pioneering work utilized invasive recordings, non-invasive modalities like electroencephalography (EEG) have become a primary focus for broader clinical applications, particularly through the decoding of motor imagery (MI) [7,8]. MI involves the mental simulation of motor actions without physical movement, engaging neural circuits similar to those activated during execution [9]. This cognitive process underpins MI-based BCIs, which translate neural activity into control signals for external devices, with applications in neurorehabilitation and assistive technologies [10]. While the potential of these technologies is immense--ranging from restored communication for locked-in patients to intuitive control of prosthetic limbs--their transition from laboratory demonstrations to life-saving clinical tools faces a major hurdle [11]. The primary challenge for EEG-based BCIs in practical applications is maintaining long-term robustness in the face of non-stationarity. A model that performs well in a controlled session may struggle in real-world settings, where factors such as user fatigue, environmental noise, and subtle shifts in electrode placement continuously alter the EEG signal distribution, presenting a significant barrier to BCI adoption [12]. MI classification using deep learning (DL) models is a cornerstone of BCI research, offering advancements over classical techniques, with a recent focus on enhancing motor decoding from various brain regions [13,14]. However, several challenges impede robust system development. Spatiotemporal uncertainty arises from EEG signal variability, limited datasets (often fewer than 50 subjects [15]), and inter-subject heterogeneity [16]. The high dimensionality and noise of EEG data complicate feature extraction and training, often leading to overfitting and requiring architectures that balance complexity with generalizability [17,18], a challenge also addressed by advanced signal processing methods like spatially regularized filter banks [19]. The ongoing evolution of DL architectures, including the recent application of spatiotemporal transformers, aims to better model neural population dynamics and improve decoding performance [20,21,22]. Despite these architectural advancements, practical considerations like channel configurations continue to impact performance: lower channel counts enhance portability but may reduce accuracy and stability [23,24]. Furthermore, the black-box nature of DL models still hinders the understanding of learned representations, necessitating interpretable solutions for reliable real-world applications [25,26]. In response to these montage variability challenges, channel dropout has emerged as a promising solution. As a variant of Monte Carlo dropout (MCD), it addresses montage variability by randomly disabling EEG channels during training and inference. This technique enhances robustness through a powerful concept: uncertainty estimation. Instead of relying on a single, potentially overconfident prediction, MCD effectively gathers insights from a large committee of sub-models to assess consensus. Strong agreement indicates confidence in the prediction, while disagreement highlights uncertainty, ultimately enhancing generalization [23]. However, its effectiveness is limited by persistent overfitting due to scarce dataand high intra- and inter-subject variability [27], as well as architecture-specific dropout tuning and computational costs that hinder real-time deployment [28].