# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Results Discussion  
This research aims to choose the most relevant EEG channels that produce an effective accuracy rate for stroke patient rehabilitation by using a binary bat algorithm-based optimization technique to optimize TEF characteristics. To further improve the classification performance of stroke EEG signals, three types of features (namely, time, entropy, and frequency domain features) were used as the baseline for the classification task. Two measurements were applied to evaluate the performance of the proposed method, accuracy and no. of EEG channels selected. Furthermore, we adopted a statistical test using the sum of the ranks for the motor/imaging EEG dataset to examine the influence of the classification accuracy and the significance of the suggested method compared with state-of-the-art metaheuristic algorithms such as PSO, GWO, FFA, and CS. Firstly, the accuracy and the no. of EEG channels selected were evaluated by computing the best, worst, and mean accuracy values to capture the stability of the implemented algorithm. In the time domain, seven features were tested, and the HFD feature had the most significant influence on the improvement of the classification task in the BAT optimizer among other optimization algorithms for the best, mean, and worst cases of accuracy and the no. of channels selected, respectively, based on a kNN classifier. In addition, the BAT results outperformed in the testing values for the best accuracy case compared with PSO, which had the highest results compared with the GWO, FFA, and CS algorithms in the HjAc and HjComp features. Moreover, the results for the BAT optimizer registered high best, mean, and worst accuracy values and the no. of channels selected, respectively, with the HFD, HjMo, and Kurt features, respectively, while PSO had the most significant influence on the improvement of the classification task compared with BAT with the Hur feature based on best, mean, and worst accuracy and the no. of channels selected, respectively. For the best-case values, the BAT optimizer outperformed in the valuable and highest values among PSO and the other metaheuristic algorithms with the six features of HFD, HjAc, HjComp, HjMo, Kurt, and Skw, respectively (see Table 2). Furthermore, according to the results of the performance accuracy using seven entropy features based on the KNN classifier, the best classification performance was achieved with the highest accuracy using ConFuzEn with the PSO algorithm compared to the GWO, FFA, CS, and BAT algorithms. Other entropy features produced higher accuracy results with the BAT algorithm than the PSO, GWO, FFA, and CS algorithms according to the best, mean, and worst accuracy values and the no. of channels selected, respectively. The BAT algorithm registered best-case accuracy values of 95.5, 95.6, 88.1, 87.2, 91.4, and 84.6 using the FuzEn, impe, MFEmu, RCMFEmu, SampEn, and TsEn features, respectively, while PSO outperformed BAT with accuracy result values for the ConvFuzEn feature for the KNN classifier (see Table 3). Figure 8: Speed of metaheuristic algorithms in seconds. The BAT and PSO algorithms obtained high accuracies of 96.7 and 83.4, respectively, compared with other algorithms in the literature for meanF and medF frequency domain features. 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; furthermore, the PSO and BAT algorithms gained significantly in accuracy relative to the medF feature on the overall accuracy results in the best, worst, and mean accuracy cases. Based on the classification results on the basis of the features from the three different domains types, the BAT and PSO algorithms exerted significant impacts on the classification performance compared with the results of the GWO, FFA, and CS optimization algorithms concerning the accuracy and the no. of channels selected, respectively.