## 5 Results and Discussion
### Accuracy of MI Responses: Results of Subject Grouping
Values represent the mean classification accuracy \(\pm\) standard deviation across all subjects, providing a measure of inter-subject performance variability. Figure 7 shows the EEG-based classification performance for each subject across the evaluated deep learning models. As previously noted, subjects are ranked according to their performance under the _FBCSP_ method (indicated by the black line), which serves as a baseline grounded in traditional signal processing. This ranking facilitates a contextual interpretation of the performance gains achieved by deep learning approaches relative to a well-established conventional method. Figure 6: Classification accuracy across channel configurations and models. The boxplots illustrate model-wise variability and robustness under different channel counts (8, 16, 32, and 64). Figure 7: _Cont._  
As illustrated in Figure 7a (top row), the empirical results for _ShallowConvNet_--the first and most basic model evaluated--demonstrate that the EEG channel montage reduction strategy yields consistent classification accuracy across subjects. This consistency serves as an indicator of the model's robustness, highlighting its generalizability across individuals. Figure 7: (**a**) _ShallowConvNet_; (**b**) _EEGNet_; (**c**) _TCNet Fusion_; Comparison of average classification accuracy across subjects for different models and channel montage configurations. In all three cases, each individual set is ranked according to the accuracy obtained using _FBCSP_ (indicated by the black line). The dashed line plotted at the 70% accuracy level serves as a threshold for splitting individuals into best-performing (above this level) and worst-performing (below this level) groups. Note that the _y_-axis is on a _log_ scale to improve the visibility of low accuracy values. Nonetheless, substantial variability in performance is observed across different montage configurations, with the exception of subjects exhibiting markedly low classification efficacy. For the next architecture, _EEGNet_--a structurally refined model relative to its predecessor--the empirical results depicted in Figure 7b (middle row) suggest a similar degree of consistency in classification performance across subjects. However, pronounced variability remains among subjects with notably low classification performance, indicating a limitation in robustness for this subgroup. In contrast, the optimized _TCNet Fusion_ architecture (Figure 7c, bottom row) exhibits superior inter-subject consistency, even among individuals with critically diminished classification accuracy. This result highlights the model's resilience to variations in channel configuration and supports its effectiveness within the proposed DL framework. A subject-wise comparison reveals several noteworthy patterns, as shown in Figure 8. For high-performing individuals (leftmost region), such as subjects 43, 14, and 3, classification accuracies remain consistently high across all models, with minimal variance. This implies that for subjects with well-defined discriminative EEG features, even simpler models (e.g., _ShallowConvNet_) can perform on par with more complex architectures. Moreover, deep learning models often underperform relative to the _FBCSP_ baseline, suggesting that in these cases, brain response patterns are sufficiently strong and structured to be accurately captured by conventional signal processing techniques. In the mid-range (e.g., subjects 22 through 15), the variability between models increases. For these individuals, _TCNet Fusion_ frequently achieves the highest accuracy, while _ShallowConvNet_ tends to underperform. As expected, the _FBCSP_ baseline model in this range is often exceeded by the best-performing DL models, emphasizing the effectiveness of learned features in scenarios of moderate classification complexity. Notably, in mid-range and lower-performing subjects, _TCNet_ often exceeds the accuracy of _FBCSP_ and sometimes even _EEGNet_, further reinforcing the potential of temporal-convolutional modeling in moderately challenging contexts.