# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Frequency Domain Results  
Based on Table 4, the frequency domain features of meanF and medF obtained high accuracies of 96.7 and 83.4 for the PSO and BAT algorithms, respectively. In the best, worst, and mean cases, meanF gained a significant accuracy relative to the medF feature on the overall accuracy results for the PSO and BAT algorithms. The best accuracy of medF was obtained at 96.7 with PSO, while the worst and mean achieved accuracies were 90.3 and 93.8 with the BAT algorithm, respectively. Moreover, the BAT algorithm scored high accuracy classification results of 83.4 and 78.4 relative to the best and worst accuracies with the feature of medF, respectively, while PSO gained an accuracy of 69.5. The convergence rate and the channel distribution are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{2}{c}{**Feature/Algorithm**} & **PSO** & **GWO** & **FFA** & **CS** & **BAT** \\ \hline \multirow{8}{*}{\(MFEmu\)} & Best & Accuracy & 85.2 & 73.6 & 54.3 & 46.8 & **88.1** \\ \cline{3-8}  & & No. channels & 7 & 3 & 2 & 1 & 7 \\ \cline{3-8}  & Worst & Accuracy & 70.8 & 55.9 & 45.1 & 40.5 & **75.5** \\ \cline{3-8}  & Mean & Accuracy & 12 & 6 & 3 & 2 & 12 \\ \cline{3-8}  & Mean & Accuracy & 80.7 & 66.1 & 49.4 & 44.8 & **84** \\ \cline{3-8}  & & No. channels & 9 & 5 & 2 & 2 & 9 \\ \hline \multirow{8}{*}{\(RCMFEmu\)} & Best & Accuracy & 84.3 & 67.7 & 51.9 & 46.6 & **87.2** \\ \cline{3-8}  & & No. channels & 4 & 2 & 2 & 1 & 8 \\ \cline{3-8}  & Worst & Accuracy & 63.7 & 47 & 44.9 & 41.2 & **75.3** \\ \cline{3-8}  & Mean & Accuracy & 12 & 6 & 3 & 2 & 13 \\ \cline{3-8}  & Mean & Accuracy & 77.3 & 56.5 & 47.7 & 43 & **80.3** \\ \cline{3-8}  & & No. channels & 8 & 4 & 2 & 1 & 10 \\ \hline \multirow{8}{*}{\(SampEn\)} & Best & Accuracy & 87.9 & 71.5 & 52.9 & 51.9 & **91.4** \\ \cline{3-8}  & Worst & Accuracy & 6 & 3 & 1 & 1 & 7 \\ \cline{3-8}  & No. channels & 75.4 & 53.2 & 42.6 & 40.4 & **75.9** \\ \cline{3-8}  & Mean & Accuracy & 10 & 6 & 3 & 3 & 14 \\ \cline{3-8}  & Mean & Accuracy & 81.4 & 63.7 & 48.2 & 44.3 & **83.6** \\ \cline{3-8}  & & No. channels & 8 & 5 & 2 & 2 & 10 \\ \hline \multirow{8}{*}{\(TsEn\)} & Best & Accuracy & 83.9 & 72.5 & 52.3 & 46.6 & **84.6** \\ \cline{3-8}  & & No. channels & 7 & 3 & 1 & 1 & 4 \\ \cline{3-8}  & Worst & Accuracy & 65.9 & 54.4 & 41.2 & 40.2 & **61.3** \\ \cline{3-8}  & & No. channels & 12 & 7 & 3 & 2 & 13 \\ \cline{3-8}  & Mean & Accuracy & 78.7 & 64.5 & 47.2 & 42.8 & **79.8** \\ \cline{3-8}  & & No. channels & 10 & 5 & 2 & 1 & 10 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 6: _Cont._As mentioned, Table 4 shows the performance of the proposed method (BAT) compared with the PSO, GWO, FFA, and CS algorithms using frequency domain features. The BAT algorithm achieved better results than all other algorithms except in some cases in which the PSO algorithm slightly outperformed it, such as in \(meanF\), where the best accuracy was 96.7 for PSO and 96.3 for BAT. However, the overall runs of the BAT algorithm achieved better results with accuracy measures equal to 93.8, 91.2, 76.4, 54, and 50 for the BAT, PSO, GWO, FFA, and CS algorithms, respectively. The convergence rate and the channel distribution are shown in Figure 7.  
Figure 6: Convergence rate and channel distribution.  
Figure 7: \(Cont\).