## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Labels** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Kappa** \\ \hline \multirow{6}{*}{1 st optimum CNN} & Left hand & 92 & 79 & 85 & \\ \cline{2-6}  & Right hand & 100 & 83 & 91 & \\ \cline{2-6}  & Both feet & 92 & 92 & 92 & 88 & 84.01 \\ \cline{2-6}  & Tongue & 75 & 100 & 86 & \\ \cline{2-6}  & Average & 90 & 88 & 88 & \\ \hline \multirow{6}{*}{2 nd optimum CNN} & Left hand & 93 & 93 & 93 & \\ \cline{2-6}  & Right hand & 75 & 75 & 75 & \\ \cline{2-6}  & Both feet & 92 & 100 & 96 & 88 & 83.97 \\ \cline{2-6}  & Tongue & 91 & 83 & 87 & \\ \cline{2-6}  & Average & 88 & 88 & 88 & \\ \hline \multirow{6}{*}{3 rd optimum CNN} & Left hand & 79 & 79 & 79 & \\ \cline{2-6}  & Right hand & 75 & 100 & 86 & \\ \cline{2-6}  & Both feet & 91 & 83 & 87 & \\ \cline{2-6}  & Tongue & 100 & 75 & 86 & \\ \cline{2-6}  & Average & 86 & 84 & 84 & \\ \hline \multirow{6}{*}{Average Ensemble} & Left hand & 100 & 93 & 96 & \\ \cline{2-6}  & Right hand & 79 & 92 & 85 & \\ \cline{1-1} \cline{2-6}  & Both feet & 100 & 100 & 100 & 92 & 89.33 \\ \cline{1-1} \cline{2-6}  & Tongue & 91 & 83 & 87 & \\ \cline{1-1} \cline{2-6}  & Average & 93 & 92 & 92 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance evaluation metrics for the proposed models. CNN can learn complicated features from a heterogeneous dataset, with the ability to expose the dataset's hidden characteristics while performing data fitting. Traditional ML algorithms generally build their models from a fixed set of functions, limiting the model's representation capability. We evaluated our EL-based model against state-of-the-art methods [19; 20; 21; 22; 23; 24; 25; 26] using the same dataset (BCI Competition IV-2a dataset) for fair comparison (Table 6). The accuracy of our method reached 92%, outperforming all state-of-the-art methods. Average Ensemble, along with the BO-based hyperparameter optimization, boosted CNN performance. To the best of our knowledge, this is the first study to employ BO for optimum hyperparameter selection in conjunction with the Average Ensemble approach to this specific EEG MI classification problem. This method was found to be the most effective in this experiment, with a Precision of 93%, a Recall of 92%, an F1-score of 92%, an accuracy of 92%, and a kappa score of 89.33%. In the field of medical diagnostics, these values are considered \"very good,\" and they can be further enhanced by exploring other EL strategies. CNN function evaluation is both time consuming and expensive. In our case, the expensive function is conducted only in a certain area space because the EI acquisition function aided in swiftly locating the search space. The objective function, represented as a GP model, is used to train the model through objective function evaluation. Analytical tracking was done with the GP model because it creates a posterior distribution over the objective function. The EL technique utilized in this study automated the randomization procedure, enabling the researchers and investigators to analyze numerous databases and collect relevant findings. Instead of being limited to a single classifier, they repeatedly construct several classifiers while randomly altering the inputs. We can achieve a more efficient prediction strategy by merging numerous single classifiers into one. It is true that all three optimum CNNs employ the same topology (two convolutional layers, two batch norm layers, two max pooling layers, three drop out layers, and four dense layers). Each model, however, has various hyperparameter values (dropout rate, activation