## 5 Discussion  
The results highlight the trade-offs between model complexity and performance in subject-specific MI-EEG classification. While deeper networks like DeepConvNet showed potential for extracting complex features, their high parameter count likely contributed to overfitting [69]. This highlights the importance of balancing complexity with regularization, particularly in tasks with limited and noisy training data. In contrast, Kernel-based models such as KREEGNet and KCS-FCNet demonstrated strong performance, suggesting that kernel methods are particularly effective in MI-EEG classification. Interestingly, ShallowConvNet's simplicity proved advantageous for subject-dependent classification, highlighting that fewer features may suffice in noisy conditions. Furthermore, the statistical tests corroborated the consistency of these findings, with models like EEGNet and KREEGNet maintaining robust performance rankings. Additionally, the Friedman chi-square test and \(t\)-test results suggest consistency between models, meaning subject groups should remain consistent across strategies. Overall, these findings suggest that simpler and more regularized architectures are better suited for subject-specific MI-EEG classification tasks. In contrast, deeper models require improved optimization techniques to mitigate overfitting [47]. Figure 24: The QMIP-CCA relevance analysis for ShallowConvNet. Questionnaire (**left**) and MI-EEG classification performance measures (**right**). Figure 25: The QMIP-CCA relevance analysis for TCFusion. Questionnaire (**left**) and MI-EEG classification performance measures (**right**). When the models were evaluated after CAM-based feedback, the results highlight differences in how the models' behavior changes due to the CAM-based enhancements. EEGNet shows class-specific biases for well- and mid-performing subjects, as shown in Figure 10, while poor-performing subjects see an improvement for both. This suggests that the model learns features corresponding to a single class for well- and mid-performing subjects, while models for poor-performing subjects achieve better generalizations. This is further supported by the topomaps shown in Figure 13, which shows most of the energy localized to one side of the head, hence explaining the bias for one specific class [70]. In contrast, ShallowConvNet achieves improvements for both classes across all three groups, implying that shallow and purely convolutional strategies achieve better generalization and benefits more from CAM-based feedback. Although ShallowConv's topomaps, shown in Figure 15, also localize information on one side, it also detects some relevant features on the opposite one, making it more robust to class bias. On the other hand, TCFusion's minimal gain (Figure 12) suggests that it is less effective at leveraging the discriminative power of CAMs, likely due to how noisy they are, as seen in Figure 16. This aligns with its overall negative trend in accuracy improvement, as seen in Figure 17. Additionally, the re-evaluated statistical and ranking tests show that pure convolutional strategies now outperform the kernel-based ones. This suggests that the non-linear mapping greatly influences our feedback approach's efficacy, making this strategy more consistent in architectures more reliant on convolutional layers. Then, the ability of a model to effectively leverage CAM-based enhancements appears to depend on its design's complexity. Purely convolutional strategies like ShallowConvNet and DeepConvNet demonstrate promising improvements, with models like EEGNet suffering from class bias, likely indicating poor generalization. Conversely, TCFusion and kernel-based strategies, although remaining consistent overall, become comparatively worse, hence suggesting that architecture complexity influences the efficacy of CAM enhancements. In turn, the QMIP-CCA results suggest a significant relationship between participants' self-reported feelings during the experiment and the performance improvements observed in MI-EEG measures.