# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Statistical Analysis Results  
Before presenting our findings for the accuracy and no. of channels selected for improving the classification performance of the stroke EEG signals' time, entropy, and frequency features analysis, we would like to examine the influence of classification accuracy in the motor/imaging EEG dataset by the adoption of the sum of ranks of the metaheuristic algorithms. In this way, the feature that improved the rank accuracy of selected channels can be identified. As shown in Table 5, we performed the sum of ranks of the PSO, GWO, FFA, CS, and BAT metaheuristic algorithms according to the individual feature domain. In the time domain, we tested seven features. The BAT algorithm significantly improved the accuracy rank, especially in HFD and Hjac, for an accuracy rank of 99 and 97.7, respectively. Moreover, other features in the time domain, such as HjComp, HjMo, Kurt, and Skw, showed an influential impact on the classification accuracy results relative to the sum of the rank of the BAT algorithm except for the Hur feature, which had a high effect on the PSO algorithm. However, in the statistical test phase, the BAT algorithm had low summation-of-rank results among other metaheuristic algorithms. On the other hand, the sum of ranks for the BAT algorithm showed a meaningful impact on the classification accuracy results relative to the seven features of the entropy domain except for the ConvFuzEn feature, which had a remarkable effect on the PSO algorithm with a difference of 0.1 compared with BAT concerning the sum-of-rank results in the statistical analysis. However, the BAT algorithm had low summation-of-rank results in the statistical test phase among the other metaheuristic algorithms. However, in the classification accuracy results from the frequency domain features, meanF showed a salient ameliorating effect on the accuracy with the PSO algorithm relative to the accuracy rank 96.7 compared with the medF feature, which had an 83.4 accuracy \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Feature/Algorithm** & **PSO** & **GWO** & **FFA** & **CS** & **BAT** \\ \hline \multirow{4}{*}{_meanF_} & Best & Accuracy & 96.7 & 87.3 & 64.1 & 52.5 & 96.3 \\ \cline{3-7}  & & No. channels & 5 & 3 & 2 & 1 & 7 \\ \cline{3-7}  & Worst & Accuracy & 65.4 & 66 & 50.2 & 41.9 & 90.3 \\ \cline{3-7}  & & No. channels & 10 & 6 & 3 & 2 & 12 \\ \cline{3-7}  & Mean & Accuracy & 91.2 & 76.4 & 54 & 50 & 93.8 \\ \cline{3-7}  & & No. channels & 8 & 4 & 2 & 2 & 10 \\ \hline \multirow{4}{*}{_MedF_} & Best & Accuracy & 82.7 & 69.2 & 53.6 & 46.6 & 83.4 \\ \cline{3-7}  & & No. channels & 6 & 3 & 2 & 1 & 6 \\ \cline{3-7}  & Worst & Accuracy & 69.5 & 55.5 & 41 & 20 & 68.7 \\ \cline{3-7}  & & No. channels & 10 & 6 & 3 & 2 & 14 \\ \cline{3-7}  & Mean & Accuracy & 76.3 & 60.6 & 46.1 & 36.5 & **78.4** \\ \cline{3-7}  & & No. channels & 9 & 4 & 2 & 2 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of BAT algorithm compared with several metaheuristic algorithms using frequency domain features. Figure 7: Convergence rate and channel distribution. rank with the BAT algorithm. Moreover, the BAT and PSO algorithms had low summation-of-rank results among other metaheuristic algorithms. Figure 8 shows the results of the speed of metaheuristic algorithms in seconds.