# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 2 Related Works
Before ranking all the channels according to their scores and using the classification accuracy to evaluate the chosen ideal channel combination, they first applied the L1 norm of a CSP to group the channel contribution scores. Finally, using the defined criteria for a particular classifier's learning process, the embedded approach was used to choose significant channels. To choose channels with the best classification outcomes, [PERSON] et al. [36] used feature selection techniques, recursive feature removal, and zero-norm optimization trained with support vector machines. The use of these techniques allowed the EEG channels to be significantly reduced [37]. The crucial step in the classification of EEG data is to use pertinent features that significantly affect the system's ability to classify data effectively [38; 39; 40]. Therefore, to achieve the best classification performance, it is necessary to use valid features from EEG signals [41]. Furthermore, global optimization techniques and machine learning classification can be combined to address such problems in the BCI domain, including feature extraction and selection [42]. Several algorithms gained a lot of interest for their applications in feature selection, for instance, differential evolution (DE), simulated annealing (SA), particle swarm optimization (PSO), artificial bee colony (ABC), and ant colony optimization (ACO) [43]. Research has been conducted on channel selection for different BCI paradigms including recursive channel elimination for motor imagery tasks [44]. In a study that was conducted by [PERSON] et al. [45], the researchers used a channel selection technique that was built into the training of an SVM classifier. The space of features was searched using GA in the references [46; 47], and the fitness function was the weighted linear combination of the number of features and the accuracy of the SVM. [PERSON] et al. in [48] suggested a PSO-based rough set feature selection approach to determine the best subset of features, and the accuracy of a neighborhood classifier was utilized as the evaluation criterion for the feature subset. DE was utilized by [PERSON] et al. [49], and PSO and GA were utilized by [PERSON] et al. [50] in order to find the best feature subset, respectively. In [51], the process of selecting characteristics for EEG-based emotion identification used ant colony optimization, simulated annealing, genetic algorithms, particle swarm optimization, and differential evolution. The performance of these EA-based approaches for feature selection is quite encouraging. However, the size of the search area is predetermined in these algorithms; therefore, throughout each iteration, it is important to make a decision regarding whether or not each feature should be chosen. Under these circumstances, the computational effort will be wasted on some features that are superfluous, unnecessary, or of a trivial nature. In addition, evolutionary algorithms (EAs) readily converge to a local optimum as a result of the wide search space as well as the interference of redundant, irrelevant, and trivial features. In this study, we design EA-based feature selection methods for motor imagery BCI through the reduction of dimensionality. The performance of a classifier will dramatically decline as the number of features increases due to the dimensionality curse, which occurs when the number of features surpasses a specific threshold. Additionally, the training procedure takes longer the higher the feature dimensionality becomes [46]. In order to locate a large number of existing channels, feature selection is required with a thorough search for extracting related channels [10; 52]. Researchers have focused on feature selection issues and suggested complete search-, greedy search-, and random search-based approaches [11]. High computational costs and convergence to local optimum remain issues with these selection techniques [53].