## 1 Introduction
However, a few issues remain challenging for applying CNN learners to achieve accurate and reliable single-trial detection of MI tasks: (i) The DL outputs require collecting substantial training data for avoiding overfitting inherent of small datasets so that the provided superior performance comes at the expense of much higher time and computational costs [20]; (ii) finding representations extracted from EEG data invariant to inter-and intra-subject differences [21; 22]; and (iii) for dealing with non-stationary and corrupted by noise artifacts, EEG-based training frameworks involve complex, nonlinear transformations that generate many trainable DL parameters, which in turn requires a considerable number of examples to calibrate them [23], fitting the data with inconvenient understanding. Consequently, the use of weights learned by CNNs tends to be highly non-explainable [24]. Although DL allows higher accuracy values, the outputs are usually learned by big, complex neural models, fitting the data with inconvenient understanding and thus tending to be highly non-explainable. Thus, the value of neural activity interpretation becomes evident in purposes like a medical diagnosis, monitoring, and computer-aided learning [25]. Recently, to promote understanding of their internal functioning, explainable CNN models have been devised with multiple 2D filtering masks that give insight into the role and derived contribution from intermediate feature layers, making the classifier's operation more intuitive [26; 27]. Since the way the convolution kernels learn features within CNN frameworks directly influences final performance outcomes, visualizing the inputs that mainly excite the individual activation patterns of weights learned at any layer of the model can aid interpretation [28]. Several approaches to analyzing EEG decoding models via post hoc interpretation techniques are reported to enhance the ability to provide explainable information about sensor-locked activity across multiple BCI systems of diverse nature, introducing techniques like kernel visualizations, saliency map, EEG decoding components, score-weighted maps, ablation tests, among others [29; 30; 31]. Still, the approaches for building class activation mapping-based (CAM) visualizations have increased interest in MI research, which performs a weighted sum of the feature maps of the last convolutional layer for each class using and a structural regularizer for preventing overfitting during training [32,33]. Specifically, the visualizations generated by gradient-based methods such as GradCam provide explanations with fine-grained details of the predicted class [34, 35, 36]. However, the CNN-learned features to be highlighted for interpretation purposes must be compatible with the neurophysiological principle of MI [37,38]. Despite the assumption that users possess mental skills that are developed to a certain degree, a lack of skills results in a mismatch between stimulus onset and elicited neural responses, incorrectly activating specific sensorimotor representations involved in the planning and executing motor acts [39]. One more aspect for visualization is the neural mechanisms of event-related Des/Synchronization. This contralateral activation, extracted from time and frequency bands over the sensorimotor cortex region, must be identified correctly to evaluate MI recognition's (in)efficiency [40,41]. Consequently, additional efforts are to be further conducted to improve the CNN-based training, aiming at better explaining spectral, temporal, and spatial behavioral patterns that act as constraints/guidelines in interpreting motor imagery skills [42,43]. This study explores the ability to distinguish between motor imagery tasks and the interpretability of the brain's ability to produce elicited mental responses with improved accuracy.