## 2 Related Work  
Since [PERSON] et al. 's work in 1990, CSP has been the go-to tool for feature extraction from EEG data [27]. CSP provides spatial filters that maximize the variance ratio of two multivariate signals, enabling the discrimination of two classes for classification purposes. Unfortunately, since the technique depends on the signals' variance, it becomes sensitive to noise and struggles with small datasets [28]. In turn, variants of CSP have sprung up to enhance the original algorithm. L1 CSP redefines the base objective in terms of the L1-norm instead of the usual L2-norm to reduce artifacts' influence on the signal [29]. In contrast, FBCSP adds a bandpass filtering stage for multiple, manually selected frequency bands before the usual spatial filtering. After that, it uses a mutual information algorithm to select discriminant features [30]. Lastly, SFBCSP selects the bandpass filters semi-automatically, rather than manually like FBCSP, by integrating a sparse regression model to learn the optimal features from each input filter band [31]. However, these models remain power-reliant and, in the case of FBCSP and SFBCSP, require some form of manual input. Once features have been extracted, classification can be performed through various methods. One such technique is support vector machines (SVMs). SVMs work by finding the optimal hyperplane for separating classes [32]. However, SVMs fail to classify data when features are too similar between classes [33]. Thanks to their ability to recognize and extract non-linear features from raw EEG data [34], DL models solve many of these traditional algorithms' shortcomings. CNNs are a family of DL strategies that scan the input signal for representative patterns, starting from simple structures until reaching a more complex combination of these initial features. These algorithms are commonly used for image processing, but by tuning the size and number of filters, it is possible to extract information from the temporal, spatial, and frequency domains, e.g., for MI-EEG classification. Also, unlike CSP, these models automatically fine-tune the best filters for the given task through gradient descent [35]. Some relevant examples of CNNs for MI-EEG include EEGNet, KREEGNet, ShallowConvNet, DeepConvNet, TCFusionnet, and KCS-FCNet. In particular, EEGNet obtains different features from EEG data in three steps: temporal convolution, depthwise convolution (to find patterns in time and space), and separable convolution (to combine the data from the first two steps) [36]. Variations of this original architecture have since emerged. TCFusionNet, for example, integrates the EEGNet architecture with residual blocks composed of dilated convolutions, which add these residual features to the ones from the separable convolution to increase the size of the receptive field while avoiding the exploding/vanishing gradient problem of deeper models [37]. ShallowConvNet works similarly to EEGNet but skips the separable convolution stage and uses regular convolution for spatial filtering. This reduces the number of parameters requiring training, leading to faster training and better interpretability, but at the cost of some performance. DeepConvNet, on the other hand, goes much further by adding a series of 2D convolutions that get bigger and bigger in order to pull information from the earlier stages and find complex structures [19]. Regarding deep kernel learning (DKL) methods, KREEGNet computes the functional connectivities from the temporal convolution through Gaussian similarity, alongside a delta kernel for label outputs, all to use central kernel alignment (CKA) as a regularizer [38]. Likewise, KCS-FCNet computes the functional connectivities from a temporal convolution through a Gaussian kernel, much like KREEGNet; however, it then relies on the measured connectivities as input for a fully connected block to classify the MI data on a high-dimensional space [39].