## 2 Related Work
Nevertheless, as computational power has increased, more powerful and complex algorithms have been developed to extract more information from EEG signals. Deep belief networks (DBNs) stack multiple restricted Boltzmann machines (RBMs), which learn to reconstruct a given input through unsupervised training; however, as a result of using RBMs, DBNs require a pre-training stage on a separate dataset, which is a luxury only available for larger databases [40]. Another set of neural networks for EEG data is autoencoders, which have been used as dynamic principal component analysis (PCA) tools to select the most relevant characteristics before classification [41]. Ideally, the model will filter out any kind of noise contaminating the signals, as it acts as redundant information, leaving only features intrinsic to the EEG for classification [42]. Unfortunately, physiological noise is extremely difficult to properly filter, as it is influenced by factors such as stress levels, personal background, and even the testing environment [43]. Alternatively, RNNs are a natural choice for EEG analysis, as their ability to remember previous inputs makes them ideal for time series data, thanks to their strong modeling capabilities, making them useful at leveraging cross-series information, even when handling heterogeneous signals [44]. Recently, transformer networks have utilized an attention mechanism to capture global information from EEG by encoding information from temporal, frequency, and spatial features, allowing the model to identify relevant sample dependencies while dealing with the low signal-noise ratio problem [45]. Despite these models' multiple advantages, their use is severely limited in tasks requiring interpretability, as these architectures lack tools to allow users to properly assess the cause for a given output, as it is difficult to confirm whether their performance derives from their extracting meaningful information or noise [46]. Furthermore, precisely due to their complexity, they are vulnerable to overfitting [47]. Now, multiple strategies have emerged to provide interpretable results from DL-based methods. The authors of [48] proposed four different types of explanations offered via the many algorithms: example, attribution, hidden semantics, and rules. Example algorithms check which inputs are similar to each other according to the model. Attribution looks at which elements of the input had the greatest influence on the given output. Hidden semantics employs the network's neurons to explain the output; for instance, when classifying animals, it determines whether the neurons focus on the head, legs, or other parts of the creature. Finally, rules explain the output in terms of a series of decisions taken by the model; in their most simple incarnation, these rules take the form \"If X is present, then Y\". However, when working with EEG data, not every algorithm will provide useful insight. Rules as explanations reduce the model to a decision tree algorithm but do not necessarily create explainable rules themselves [49]. This leaves open the other three types of explanations. Hidden semantics are useful for evaluating the importance of specific features at certain points, but they produce abstract results the deeper the neurons are [50]. For inter-subject analysis, example algorithms are capable of finding similarities between subjects, providing information as to which patients share similar attributes, but they struggle to provide explainability to extracted features, as these must already make sense to the BCI professionals beforehand [51]. Next, attribution has the most potential for BCI insight, as these techniques map the importance of the output back to the original input (EEG data). Common attribution algorithms are CAMs, which create a mask that highlights which pixels from the input image supply the most important information for the output. Introduced by [52], CAMs map out the elements within an image most relevant for the CNN.