## 2 Related Work
### Uncertainty Estimation in Neural Models  
Standard DL models, while excelling at function approximation, typically produce point estimates and lack an intrinsic mechanism to express prediction confidence. This absence of uncertainty modeling, particularly epistemic uncertainty (i.e., uncertainty in the model's parameters due to limited data), can lead to overconfident predictions, a critical limitation in safety-conscious applications like brain-computer interfaces (BCI), where incorrect decisions may have significant consequences [58]. To address this, the field of Bayesian deep learning has increasingly focused on modeling uncertainty by learning a probability distribution over model weights, rather than relying on a single point estimate. While full Bayesian inference provides a robust framework for uncertainty quantification, which aims to reliably measure a model's confidence in its predictions, it is often computationally intractable for large models. As detailed by [PERSON] et al. [59], various approximation methods have been developed to overcome these challenges, such as variational inference, Laplace approximation, and sampling-based methods. Among these, Monte Carlo dropout (MCD) [60] has emerged as a particularly popular and computationally efficient technique. MCD utilizes dropout during inference to generate an ensemble of predictions from a single model, where the variance of these predictions serves as an estimate of epistemic uncertainty. Alternatively, non-Bayesian approaches, such as deep ensembles, have been developed. In these approaches, multiple independently trained models are combined, and their predictive disagreement is used to quantify uncertainty [61]. This method has gained popularity, especially in scenarios where full Bayesian inference is computationally prohibitive. A key advantage of deep ensembles is their ability to explore diverse model architectures and weight initializations, which provides reliable uncertainty estimates. For example, [PERSON] et al. [62] demonstrated that deep ensembles significantly improve prediction performance and uncertainty estimation in EEG analysis. By performing multiple forward passes on the same input, deep ensembles generate a distribution of predictions, with the variance acting as a proxy for epistemic uncertainty. This strategy has been successfully applied in EEG-based classification tasks, such as epileptic seizure detection, where it helps to reject the most uncertain data points, thereby improving system reliability. Building on these principles, our work leverages the computational efficiency of MCD, which has been shown to enhance both accuracy and the reliability of uncertainty estimates [59].  
The four research areas, i.e., accuracy, robustness, transparency, and portability, have evolved separately, with each focusing on improving one aspect of BCI systems. However, a significant gap remains: no unified framework addresses these challenges simultaneously. A robust yet uninterpretable model lacks clinical trust, while an interpretable but unreliable model is ineffective. This study proposes a solution by integrating MCD for uncertainty-aware robustness with LayerCAM for fine-grained interpretability, creating a synergistic pipeline. This combination not only enhances performance but also ensures that the improvements are grounded in focused and neurophysiologically plausible brain patterns.