## 5 Conclusions  
Brain-computer interface has attracted a lot of interest in the hope of improving human life. Motor imagery-based BCI is expected to have potential in the medical field, especially in neurorehabilitation. However, this type of system has been encountered several challenges in terms of the limited number of commands, insufficient performance and training time, robust online analysis, and so forth. Various algorithms and approaches have been proposed to deal with the challenges, and CSP is one of the candidates for a practical BCI. Hence, numerous researchers put their effort in harnessing the full potential of CSP and its variants through methods such as the combination of CSP and time-frequency analysis, simultaneous optimization of the spatial spectrum filter, filtering the EEG signals into several frequency bands, and intelligent optimization. In addition, filter, wrapper, and embedded methods have also been established to further improve the performance. Yet, there are still limitations requiring further investigations to shed light on. To this end, the authors hope to make contributions to this crusade. The study successfully proposed a novel DFBCSP framework in combination with [PERSON]'s ratio and the mRmR algorithm. The framework was evaluated on both datasets and produced noteworthy findings mentioned in the previous section. The proposed method could be applied in any classification problems, mainly in electrophysiological signal processing but not limited to other researches requiring pattern recognition, biometrics identification, and machine learning-related problems. Furthermore, the proposed method could be utilized in small-sized data, which is essential in real-world applications. Lastly, the reassurance of the discrimination between hand vs. feet better than left vs. right hand provides more proof about responses on motor cortex when performing motor imagery in the field of neuroscience.  
Nevertheless, there are some drawbacks to the study. Firstly, the performance of the proposed method is yet satisfactory for practical BCI, especially in a medical context. One way to address this issue is utilizing SVM and tuning its parameters to obtain optimal results. In addition, trial selection could be performed in the pre-processing step to eliminate undesired trials. Secondly, other metrics such as information transfer rate (ITR) and computational time, which are critical for practical BCI, are not considered in the paper. Thirdly, the study is carried out only in offline analysis. The authors hope to conduct further investigations into these matters. Segmenting the time window into 1 s makes it suitable to establish a model to perform online analysis. Hence the authors hope to establish a robust decoder for real-time motor imagery training with feedbacks. In future investigations, comparison with other feature selection methods or combination with optimization methods [26; 27; 29] while minimizing computation cost should be carried out.  
Conceptualization, M.T.D.N.; methodology, M.T.D.N.; validation, M.T.D.N., N.Y.P.X. and B.M.P.; formal analysis, M.T.D.N.; investigation, M.T.D.N.; resources, M.T.D.N.; data curation, M.T.D.N. and Q.K.L.; writing--original draft preparation, M.T.D.N. and B.M.P.; writing--review and editing, M.T.D.N. and B.M.P.; visualization, N.Y.P.X.; supervision, T.-H.N. and Q.-L.H.; project administration, T.-H.N., Q.K.L. and Q.-L.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.  
The authors declare no conflict of interest.