## 1 Introduction  
The act of rehearsing body movements by means of their mental representation is known as motor imagery (MI). Brain-Computer Interfaces (BCIs) implement this paradigm by capturing brain activity patterns associated with elicited mental tasks and converting them into commands for external devices, having potential in a wide range of clinical, commercial, and personal applications [1, 2]. Usually, the brain activity stimulated by MI is measured through Electroencephalography (EEG), which has several advantages: non-invasive data capture from the scalp surface, high temporal resolution, and inexpensive and portable devices with ease of setup [3]. Despite this, EEG data has a limited spatial resolution, and their weak electrical potentials make them susceptible to various disturbances, including electromyographic signals from the movement of the heart, eyes, tongue, and muscles, during data acquisition [4]. Furthermore, there is substantial variation in brain neural activity from one session to another and between different subjects [5]. This situation is partly explained because the EEG data used to drive effectors in MI-related BCI systems are profoundly affected by the ongoing mental states. Hence, the individual ability to generate mental imagery signals with a higher SNR of movement dictates how easily the neural responses elicited will be detectable [6]. In other words, a set of neurophysiological and non-neurophysiological causes in the brain structure or function of evaluatedsubjects might be different from each other, making the implemented BCIs unfit for use on a few individuals [7; 8]. As a result, a critical factor for the widespread application of these EEG-based systems is the significant performance differentiation in MI coordination skills among individuals. Therefore, using conventional classification algorithms may result in many subjects having poor accuracy, less than 70% (poor MI coordination skills), meaning that between 15-30% of the population struggle to improve their ability to monitor BCI systems concerning mental imagery tasks [9]. There are several strategies for improving BCI performance to enhance MI-BCI skills, such as using subject-specific designs and subject-independent set-ups. The former is designed and trained per subject, requiring prior calibration and rigorous system adaptation, and resulting in time-consuming and inconvenient BCI systems [10]. The latter instead trains a generalized model to be used by newly incorporated subjects. To this end, most BCI systems rely on temporal, spectral, and spatial features mainly computed on a single-trial basis to distinguish different MI patterns. In particular, since MI is a task-related power modulation of brain rhythms mainly localized in the sensorimotor area, Filter Bank Common Spatial Pattern (FBCSP) is a widely employed algorithm for effectively extracting EEG features by introducing an array of spatial bandpass filters [11]. However, these handcrafted feature extraction methods have the disadvantage of low temporal resolution, poor generalization over multiple subject sets, and a complicated tuning process [12]. As a solution for ineffective discriminability in decoding MI tasks using EEGs, Deep Learning (DL) algorithms have increasingly been applied to boost the classification accuracy of subject-independent classifiers. Among DL models, convolutional neural networks (CNN) with kernels that share weights for multidimensional planes have achieved outstanding success in extracting, directly from raw EEG data, unlocked local/general patterns over different domain combinations like time, space, and frequency [13; 14; 15; 16; 17; 18]. The earlier layers learn low-level features, while the deeper layers learn high-level representations. DL architectures can also be adapted to allow learning models to mimic extracting EEG features by imposing explicit properties on the representations learned [19].