## 4 Discussion  
Intelligent optimization methods [26] along with deep learning networks [45; 46; 47; 48] have widely been investigated in recent years. Although their performances are slightly  
Figure 9: The “all measurement” and “single measurement” accuracy differences are distributed according to the four frameworks. Pair 1, pair 2, and pair 3 are denoted by green, blue, and magenta, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**LH-RH**} & \multicolumn{3}{c}{**RH-F**} & \multicolumn{3}{c}{**F-LH**} \\ \hline  
superior to traditional machine learning, the burdens in terms of computational complexity and massive data requirement are far from trading off. Secondly, traditional algorithms such as CSP have been proved in countless studies to be effective for practical BCI, yet its potential needs further exploration. Finally, most studies evaluated their proposed framework on the BCI competition dataset but in the absence of actual data of local population, which is one of the criteria both to evaluate the validity of the proposed method and examine the suitability of various populations. Consequently, the novel approach of this study make contributions as follows:  
* Propose a novel and reliable framework for motor imagery tasks based on spatial filters, automatic feature selection, and traditional classifier
* Evaluate the proposed methods on both self-acquired dataset and a BCI Competition dataset in terms of accuracy, F1-score, and ROC  
From the results, there are numerous key points that have to be taken into account. Firstly, the validity of the self-acquired dataset has been proven through similarities with BCI Competition dataset IIa. The pattern of better classification of hands vs. feet over that of left vs. right hand is seen in both datasets, Tables 1-4, and also consistent with previous studies [68]. Moreover, DFBCSP methods outweigh the FBCSP method in both datasets (single measurement cases for self-acquired data) and it has been clearly proven in several studies [18; 39; 40; 65; 69; 70]. This can be explained because FBCSP (without feature selection) generates an enormous amount of features, i.e., 22 \(\times\) 17 features in BCI competition datasets IIa, and obviously also contains redundant information that would degrade the performance. [PERSON] et al. [70] also stated that the mean accuracy of a framework without feature selection is far worse than that of a framework integrated with feature selection as it does not use irrelevant features. That is why several algorithms have been built to select informative features to mainly improve the performance and reduce computational costs. The third similarity between the two datasets is in the mean values and their standard deviation. Both datasets imply that the BCI performance varied among users (moderate mean value and high standard deviation) and not every subject is suitable for a certain framework, or decoder, or even type of BCI. The phenomenon is known as \"BCI illiteracy\" which will be explained later. Secondly, the robustness and reliability of the proposed method are clearly demonstrated by the results. ROC curve, accuracy, and F1-score of the proposed method are consistent throughout the study and they are comparative to other methods. Although the framework might not be better than other conventional ones, there are some cases that have proven its superiority. Likewise, other methods are superior to one another depending on subjects and pairs. It can be inferred that the subjects from both datasets suffer from \"BCI illiteracy\". BCI illiteracy is caused by a variety of factors that may be classified into two categories [71]: physiological and non-physiological causes. Regarding physiological reasons, variations in the brain's anatomy, such as the dispersion of neurons in the brain folds, may make recording brain signals through the electrodes problematic. Additionally, transitory variables should be considered.