## 5 Results and Discussion
### Accuracy of MI Responses: Results of Subject Grouping  
In our approach, we leverage channel-montage reduction to enhance validation reliability by balancing classification accuracy and inter-subject consistency under the practical constraints of each evaluated deep learning model. Table 1 reports the average classification accuracy across various channel configurations, enabling a comparative performance analysis. Notably, all three models improve with increasing electrode count up to 32 channels. _TCNet Fusion_ attains its peak accuracy (0.744) at 32 channels, slightly outperforming its performance at 64 channels (0.739). Likewise, _EEGNet_ and _ShallowConvNet_ reach peak accuracies of 0.737 and 0.727, respectively, also at 32 channels. These trends suggest that reducing from 64 to 32 channels may enhance performance by mitigating the effects of noisy or redundant electrodes. In terms of model-specific robustness to channel reduction from 32 to 8 channels, _TCNet Fusion_ demonstrates a high degree of stability, maintaining accuracy within the range of 0.700 - 0.744, corresponding to a relative drop of only 4.4%. _EEGNet_ exhibits a slightly smaller decline of 3.1% (from 0.737 to 0.706), while _ShallowConvNet_ shows the smallest reduction in accuracy (2.7%) but also records the lowest peak performance (0.727). These findings suggest that the architectural complexity of _TCNet Fusion_, and to a lesser extent _EEGNet_, allows them to better retain performance with sparse montage configurations compared to simpler models such as _ShallowConvNet_. The boxplots in Figure 6 provide a clear visualization of the inter-subject variance, where the height of each box indicates the interquartile range of subject accuracies and the whiskers represent the overall spread. This plot shows that _TCNet Fusion_ achieves an accuracy of 0.729 with 16 channels, representing a mere 2% drop from its peak performance at 32 channels. Similarly, both _EEGNet_ and _ShallowConvNet_ retain more than 95% of their respective peak accuracies at this reduced configuration. These results imply that halving the number of channels--from 32 to 16--leads to only marginal performance degradation. Consequently, 16-channel setups are a practical choice for portable EEG systems, offering a favorable trade-off between hardware simplicity and classification performance. Another important consideration is _inter-model consistency_, evaluated through accuracy variability across DL models. At 32 channels, the performance gap between the best model (_TCNet Fusion_: 0.744) and the worst-performing model (_ShallowConvNet_: 0.727) is 2.3%. However, when the number of channels is reduced to 8, this gap narrows to just 1.1% (_TCNet Fusion_: 0.700 vs. _ShallowConvNet_: 0.708), indicating that sparse montages (\(8-16\) channels) reduce inter-model variability. That is, montage reduction can enhance practical usability without sacrificing accuracy. Moreover, these results suggest that simpler models may suffice for low-channel configurations, whereas more advanced architectures, such as _TCNet Fusion_, are better suited for moderate- to high-density setups--up to at least 32 channels. Beyond this point, such as at 64 channels, model performance either remains comparable to the 32-channel setup or even degrades, with a noticeable increase in performance dispersion, regardless of the model architecture. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **8 Channels** & **16 Channels** & **32 Channels** & **64 Channels** \\ \hline _ShallowConvNet_ & \(0.708\pm 0.015\) & \(0.718\pm 0.012\) & \(0.727\pm 0.010\) & \(0.725\pm 0.011\) \\ _EEGNet_ & \(0.706\pm 0.014\) & \(0.720\pm 0.013\) & \(0.737\pm 0.009\) & \(0.733\pm 0.010\) \\ _TCNet Fusion_ & \(0.700\pm 0.016\) & \(0.729\pm 0.011\) & \(0.744\pm 0.008\) & \(0.740\pm 0.009\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance consistency averaged across tested models and channel montages.