# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 6 Conclusions and Future Work  
This paper proposes a new method for EEG channel selection based on an optimization algorithm called the BAT-inspired algorithm. The main purpose of the proposed algorithm is to select the most relevant EEG channels that can provide a higher accuracy rate for stroke patient rehabilitation.  
The proposed method was tested using a standard EEG dataset collected from eight poststroke patients with hemiparesis of the upper extremities. The EEG data were captured using EEG caps with 16 active electrodes from g.Nautilus PRO, g.tec medical engineering GmbH, Austria.  
The proposed method used several EEG feature extraction methods such as the time domain, frequency domain, and entropy domain. In addition, the proposed method (BAT) was compared with four metaheuristic algorithms, namely, particle swarm optimization (PSO), grey wolf optimizer (GWO), cuckoo search (CS), and firefly algorithm (FFA). The performance of the proposed method was evaluated using the accuracy rate and the number of channels selected. The proposed method achieved the best results over all the feature extraction methods and showed a significant improvement using a standard statistical analysis test called the summation of ranks test.  
Finally, the proposed method succeeded in reducing the number of EEG channels to less than half while maintaining the accuracy rate.  
Regarding future works, the proposed method suffers from some problems; for example, the proposed method is unable to generate new solutions that have the ability to increase accuracy (i.e., stuck in local minima). For that, the proposed method will need to be improved to overcome this problem by modifying the mechanism of the BAT algorithm or hybridizing the BAT algorithm with another metaheuristic algorithm to be able to find the optimal solution. Regarding the second problem, the current version of the BAT algorithm focuses on finding the optimal solution that provides the highest accuracy rate and does not consider the number of EEG channels in the objective function. Therefore, in future work, we will work on the proposed multiobjective BAT version using the highest accuracy and the lowest number of EEG channels simultaneously.  
Conceptualization, M.A.A.-B., Z.A.A.A. and N.K.A.-Q.; methodology, M.A.A.-B., Z.A.A.A. and N.K.A.-Q.; software, M.A.A.-B. and Z.A.A.A.; validation, M.A.A.-B. and S.N.M.; investigation, M.A.A.-B. and Z.A.A.A.; resources, M.A.A.-B., Z.A.A.A. and C.G.; writing--original draft, M.A.A.-B., Z.A.A.A., N.K.A.-Q., S.N.M., N.S.A. and C.G.; Visualization, M.A.A.-B., Z.A.A.A., S.N.M., and C.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Research and Graduate Studies (DRG) at Ajman University, Ajman, UAE (Grant No. 2022-IRC-ENIT-7).  
Data are contained within the article.  
Author [PERSON] was employed by the g.tec medical engineering GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.