## 4 Results  
### MI Classification Performance  
All six CNN models and the straightforward filter bank CSP approach, CSP for simplicity, were trained, evaluated, and contrasted among themselves based on their cross-validation results. Figure 6 shows the different metrics for each model prior to the CAM-based enhancements. KREEGNet showed the best accuracy and AUC score, while DeepConvNet showed the worst. In general, kernel methods improved upon the baseline EEGNet. Despite having fewer feature extraction layers, ShallowConvNet remained compa  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Hyperparameter** & **Argument** & **Value** \\ \hline \multirow{4}{*}{Reduce learning rate on plateau} & Monitor & Training Loss \\  & Factor & 0.1 \\ \cline{1-1}  & \multirow{2}{*}{Patience} & \multirow{2}{*}{30} \\  & & \\  & Min Delta & 0.01 \\  & Min learning rate & 0 \\ \hline Adam & Learning rate & 0.01 \\ \hline \multirow{4}{*}{Stratified shuffle split} & Splits & 5 \\  & Test size & 20\% \\  & Validation size & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subject-dependent MI-EEG classification DL hyperparameters.  
Figure 5: Experiment’s workflow using MEDL. Each model generates CAMs that are then used to enhance the original EEG input. Model and CAM-based performance measures, along with the questionnaire, are used to perform multimodal analysis via QMIP-CCA.  
rable, while CSP and DeepConvNet lost a significant amount of accuracy compared to the other techniques. Moreover, despite its more complex architecture, TCFusion performed almost identically to EEGNet.  
Furthermore, when observing inter-subject accuracy across models, as seen in Figure 7, DeepConvNet's low performance becomes more apparent. Grouping subjects into three groups based on their classification accuracy on EEGNet (good, mid, and poor), DeepConvNet struggles a lot to properly classify good MI subjects, only ever outperforming EEGNet for the two worst subjects. In contrast, KCS-FCNet, although underperforming for good subjects, remains consistent for mid and poor subjects. Interestingly, ShallowConvNet performs well in the poor-performing group.  
Furthermore, additional statistical tests were carried out to judge the consistency of these models. Figure 8a shows the models ranked by performance for each subject, with 1 being the best and 7 being the worst. This clearly shows that CSP and DeepConvNet are the worst models overall, while all other models are consistent with each other. When evaluating the Friedman chi test, in this regard, we obtain a \(p\)-value of \(6.76\times 10^{-22}\), further supporting this. Additionally, Figure 8b shows the results of the \(t\)-test to judge the similarity between models, which correlates with the ranking results. Finally, Table 2 shows the average ranking and \(t\)-test \(p\)-value (ignoring the diagonal) for each architecture.  
Next, Figure 9 shows the distribution of subject performance across groups for each model. It is clear that DeepConvNet yields lower results compared to other models such as EEGNet, which are capable of producing comparable results.  
Figure 6: MI-EEG GiGaScience classification results. Blue: accuracy; orange: AUC; green: Kappa.  
Figure 7: Inter-subject accuracy results. Subjects are sorted based on EEGNet performance.