# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 2 Related Works  
EEG-based BCIs in particular have good temporal resolution [21], but the recorded wave activity may be corrupted in a variety of ways depending on the artifacts that occurred, so preprocessing the raw EEG signals is critical in the classification of MI-based signals. Most of the time, these artifacts can imitate or overlay the abnormal behavior of the brain. Studies have investigated artifacts' effects on EEG signals, including eye blinks, ocular movements, cardiac artifacts, and muscular activity, and noise from power lines can also intersect an EEG's frequencies [22; 23]. As a result, assessing the efficiency of EEG signals in the presence of background noise can be difficult. Spatial filters can produce a more localized signal for each electrode in this situation because EEG-based BCIs in particular have poor spatial resolution. To choose the best characteristics for BCI-MI-based EEG analysis, feature extraction is carried out for each recorded channel in multiple domains. The most popular technique is to use common spatial patterns (CSPs) to obtain discriminant features from high-dimensional EEG signals, which may be used to identify the spatial characteristics related to ERD produced by various MI tasks [24]. However, the efficiency of CSPs is easily impacted by noise and is highly sensitive to the choice of frequency bands [16]. Several feature extraction techniques were developed to analyze the EEG data in the time, frequency, and time-frequency domains to address the issue of efficacy [25]. Additionally, wavelet coherence (WC) bispectrum characteristics were suggested in [26] for distinguishing between right and left MI. The sizes of the features derived from EEG signals are usually pretty big for each channel, and they get bigger when one moves to the next channel. The classification of several features necessitates additional computation and time. To solve this issue, the most effective feature set must be obtained by selecting a subset of EEG channels that are more closely related to mental work than others. The four evaluation methodologies of filtering, wrapping, embedding, and hybrid can be used to categorize feature selection algorithms [27]. Based on specific statistical criteria, the filtering method used for motor intention-based EEG activities is taken into consideration. For instance, before classifying the motor intent activity, [PERSON] et al. [28] developed the statistical method of the Bhattacharyya bound for channel selection. The best candidate subset was chosen using this method, which also employed a CSP and sequential search strategy. [PERSON] et al. [29] suggested a different statistical method based on CSP rank for sorting filter coefficients with absolute values and then choosing the features with the biggest succeeding coefficient. Although the filtering technique has low accuracy, it operates quickly and is unaffected by the subject or classifier choice. On the other hand, the classifier and the subject are quite important in the wrapper selection process [30]. In this way, the subset candidates are assessed using classification accuracy and can thus make more accurate predictions than filtering techniques. As a result, the wrapper strategy is more computationally expensive than the filtering technique and is subject to the overfitting problem [31], which can be avoided by employing cross-validation measures for prediction. For choosing the best channel, the majority of wrapper strategies use the sequential forward search, backward elimination strategy [32], and heuristic search method [33; 34]. Without mentioning a stopping condition, the filtering and wrapping procedures have also been coupled for selection purposes. This hybrid strategy was created to deal with huge datasets. [PERSON] et al., for instance [35], offered the best possible channel selection technique based on CSPs.