## 4 Results
### Explainable MI-EEG Classification Results  
Figure 10 displays the average class score percentage gain for EEGNet across performance groups and class labels. This is used to look at how the proposed solution affected the class score. Figure 10a,b shows that the well- and mid-performing groups are biased towards a specific class, with only the poorly performing subjects presenting improvements for both. In contrast, ShallowConvNet's percentage gains, presented in Figure 11, consistently show that all groups improve for both classes. On the other hand, TCFusion barely shows any gain at all. Figure 12 shows both well- and mid-performing groups only experiencing tiny amounts of improvement when compared to the other two models. The only group to have achieved substantial improvements is the poorly performing one, but only for right-hand MI. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Avg. Ranking** & **Avg. \(t\)-Test \(p\)-Value** \\ \hline CSP & 5.32 & 0.03 \\ EEGNet & 2.98 & 0.55 \\ KCS-FCNet & 3.16 & 0.54 \\ KREEGNet & 2.88 & 0.48 \\ DeepConvNet & 6.32 & 0.01 \\ ShallowConvNet & 3.80 & 0.37 \\ TCFusionNet & 3.32 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average rankings and \(p\)-values. Figure 8: Models rankings vs. \(t\)-test \(p\)-values. Subjects are sorted based on EEGNet’s accuracy. Figure 9: Group-performing MI-EEG classification results. (**a**) Subjects with EEGNet accuracy above 80%. (**b**) Subjects with EEGNet accuracy between 60% and 80%. (**c**) Subjects with EEGNet accuracy below 60%. When examining the spatial location of the highlighted features, we generated topomaps from the CAMs by averaging their values across time and trials per class and true label. These topomaps were then normalized among each other, and finally, cubic interpolation was performed to achieve a continuous image [68]. As seen in the CAMs used per class, it is possible to observe a pattern emerging. Figure 12(a,c) shows the average topomaps for well- and poorly performing subjects. The well-performing subject has the most important features located in the right sensorimotor area, with no information being highlighted on the left side. Then, the model learns the features related to one class and classifies the other whenever the information in this area is non-conclusive, which explains the bias presented by the percentage gain. In contrast, the poor-performing subject displays information highlighted throughout their head, with both classes highlighting nearly identical regions. However, subjects with average performance, like the one shown in Figure 12(b), appear to mix the two. The most important information is found in the sensorimotor area for one side, while the rest of the EEG is disrupted due to the noise. This behavior is consistent between models as shown in Figures 14 and 15, except for TCFusion, as presented in Figure 16, which generates spatially noisy CAMs. Additionally, Figure 17 shows the improvement distribution for the different models across all three groups for the best fold across strategies. Noticeably, ShallowConvNet achieves consistently positive improvements, as opposed to other models, which achieve a mix of improvements and losses. However, in general, all models tend to show improve  
Figure 11: Class score percentage gain per MI class for ShallowConvNet. Figure 12: Class score percentage gain per MI class for TCFusion. Figure 10: Class score percentage gain per MI class for EEGNet. ments or at least remain the same, with a median improvement close to zero across all three groups and some edge cases showing massive losses. The only exception to this rule is TCFusion, which consistently loses accuracy across all three groups. Furthermore, Table 3 shows the average change in accuracy for all models across all subjects and all folds. All models see minor changes, with KREEGNet seeing the best improvement overall, but with high variability, and ShallowConvNet showing minor losses. Finally, when model rankings and similarity were re-evaluated, they changed greatly.