## 3 Results
Dense_nodes1, Dense_nodes2, and Dense_nodes3 refer to the number of nodes in the first, second, and third dense layers, respectively. notice an average increase of 19.3%, 30%, 18.6%, 24.6%, and 27.5% in Accuracy, Precision, Recall, F1-score, and Kappa, respectively. We combined our three generated CNNs using the Average Ensemble strategy. The performance evaluation metrics for the proposed models are shown in Table 4. Precision is described as the positive predictive rate. Based on Precision results, our Ensemble method surpassed the weak-learners' average Precision by 5.7% which indicates the feasibility \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trial Number** & 20 & 51 & 31 & 2 & 21 \\ \hline
**Activation Function** & ReLU & ReLU & ReLU & ReLU & ReLU \\ \hline
**Dropout** & 0 & 0.3 & 0.5 & 0.16 & 0 \\ \hline
**Dense\_nodes1** & 629 & 529 & 520 & 549 & 724 \\ \hline
**Dense\_nodes2** & 947 & 965 & 451 & 321 & 32 \\ \hline
**Dense\_nodes3** & 862 & 698 & 965 & 383 & 665 \\ \hline
**Batch Size** & 60 & 61 & 128 & 44 & 69 \\ \hline
**Optimizer** & AdaMax & AdaMax & Nadam & Nadam & SGD \\ \hline
**Accuracy** & 80\% & 80\% & 74\% & 72\% & 72\% \\ \hline
**Time (seconds)** & 36.06 & 37.7 & 32.9 & 48.78 & 33.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The top five most optimum trials. Figure 10: Plot of accuracy versus various epochs of the first optimum CNN. The perfect number of epochs (32) is highlighted by the blue circle, which indicates where the checkpoint callback saved the model’s weights the last time it was executed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Base CNN Architecture** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline Before BO + checkpoint callback & 68 & 58 & 68 & 62 & 54.7 \\ \hline \multirow{4}{*}{After BO + checkpoint callback} & 1 st optimum CNN & 88 & 90 & 88 & 88 & 84.01 \\ \cline{2-6}  & 2 nd optimum CNN & 88 & 88 & 88 & 88 & 83.97 \\ \cline{1-1} \cline{2-6}  & 3 rd optimum CNN & 86 & 86 & 84 & 84 & 78.63 \\ \cline{1-1} \cline{2-6}  & Average & 87.3 & 88 & 86.6 & 86.6 & 82.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the performance of the base CNN architecture before and after applying BO and checkpoint callback. and superiority of Ensemble model designed in this paper. Moreover, it provided 100% Precision on the left hand and both feet, stressing the importance of aggregating the performance of multiple weak learners. Our Ensemble method's Recall exceeded the average Recall of the weak learners by 5.34%. The F1-score reported a similar increase value (5.34%) on the other side. The \"feet\" label was identified with 100% confidence based on the three aforementioned metrics. In terms of Accuracy score, our Ensemble technique achieved 92%, representing a 5.34% improvement over the weak learners. This is the same increase rate recorded for Recall and F1-score. [PERSON]'s kappa is an important statistic that measures inter-annotator agreement. The employed Ensemble method scored the highest kappa score, reflecting a 7.13% increase over the weak learners. This significant increase in Precision (5.7%), Recall (5.34%), Accuracy (5.34%), F1-score (5.34%), and Kappa (7.13%) demonstrates the feasibility and superiority of the Ensemble model proposed in this study. Overall, the Ensemble method produces extremely promising results across all five metrics. Table 5 compares our optimized Average Ensemble model to five traditional ML algorithms (LDA, SVM, RF, MLP, and GNB) on the BCI Competition IV-2a dataset. All the traditional classifiers were evaluated using the same metrics. Our proposed method outperformed the traditional algorithms by a wide margin across all five metrics. Among the traditional algorithms, SVM classifier performs the best, with a Precision of 70.2%, a Recall of 70.83%, an F1-score of 70.35%, an Accuracy of 70%, and a Kappa score of 60.02%, whereas GNB is the worst classifier in this experiment.