## 1 Introduction
The second method is challenging to implement and effortlessly leads to local solutions [29]. The drawback of the fourth method is the requirement of a lengthy training phase of models, which is insufficient for practical BCI. Although sub-band filtering in the third method requires high computation cost, it seems to have surpassed the other methods in terms of performance and robustness. With regard to feature selection, there are three main present methods: filter, wrapper, and embedded [30]. Filter method initiates with all of the features and these features are chosen based on criteria such as information gain, consistency, dependency, correlation, and distance measures [31; 32]. The wrapper method evaluates feature selection by comparing it to the classifier's performance [33; 34; 35]. However, when assessing each applicant feature subset, the wrapper method must train and test the classifier, and that is prone to overfitting and computationally costly. The embedded technique incorporates feature selection into the classifier's training phase, performing both tasks concurrently [36; 37]. Although proven to be efficient in most studies, the embedded technique has the highest computational cost in all three feature selection methods. In contrast, [PERSON] was used in a unified framework for feature extraction, selection, and classification [38]. [PERSON]'s ratio was also used in [39] as a channel selector along with the correlation coefficient. In [40], the author performed manual screening for optimal frequency bands and suggested building a more sophisticated algorithm for auto selection. Yet his following study [26] implemented temporal filter parameter optimization combined with optimization algorithms (particle swarm optimization, artificial bee colony, and genetic algorithm) and the drawback was long training time as the framework was so sophisticated. Hence, [PERSON]'s ratio is the right tool for a simpler, lower computational cost and more effective method. Furthermore, in [41], the author also suggested using mutual information-based feature selection (i.e., mRmR) as it is well-known for selecting the most significant features. Consequently, filter methods, Fisher's ratio, and mRmR, are utilized in this study due to their low computational cost. With regard to classifiers, the three most popular algorithms because of their robustness are linear discriminant analysis (LDA), support vector machine (SVM), and Bayesian [42; 43; 44]. Deep learning has emerged and is combined with traditional algorithms to create reliable and powerful techniques in MI tasks recognition problems in many studies [14; 45; 46; 47; 48]. However, deep learning not only poses a high computational cost but also extracts incomprehensible features [49]. Furthermore, deep learning has no clear advantages over conventional machine learning methods [50]. Hence, in this study, LDA is utilized for classification as it has been successfully implemented in various applications [51; 52; 53]. Lastly, most of the previous studies evaluated only on BCI competition datasets with the absence of comparing with their own data. [PERSON] et al. [38] established their own datasets of 16 subjects to evaluate the study's proposed framework, which used [PERSON]'s objective function for feature extraction and feature selection. The study's dataset size was small as it only contained 80 trials per class per subject and was not compared to other published datasets, such as BCI competition. [PERSON] et al. [18] also established a dataset of 7 subjects, 80 trials per class per subject, to evaluate his proposed combined CSP and time-frequency analysis framework. The study's proposed framework was also evaluated on other BCI competition datasets. Consequently, carrying out both subjective and objective datasets would be more reliable.