## 5 Conclusions  
In this paper, we present a novel method that is based on user specific band and subject specific band to select the frequency band and time segment for the classification of four-class motor imagery tasks. The method of common spatial patterns was used for pre-processing of the signal. The method reduced the number of channels from 22 to eight for the data set 2a from the BCI Competition IV. The different time intervals were examined with CSP, TDP, FFT, and PCA feature extraction methods and [PERSON]'s linear discriminant analysis (LDA) classifier to find out the best time intervals each subject who performed motor imagery. We counted the number of frequency bands in which [PERSON]'s linear discriminant analysis (LDA) classifier had the best accuracy rates in order to show that Fisher ratio and FDA-type F-score bands are effective in performance improvement. It is seen that, most of the time, a combination of Fisher ratio and FDA-type F-score bands would have better performance than just using Fisher ratio and FDA-type F-score bands alone. Different feature extraction techniques, like TDP, R\({}^{2}\), PCA, FCBF, EMD, ITD, CV, TDP, and FFT, are compared in this paper. From the different combination of the feature extraction techniques, it is observed that the combination of both user specific band and subject specific band improve the accuracy for the combination of time domain parameters, fast Fourier transform, and principal component analysis. The combination of these algorithms has significantly increased the accuracy when it is compared to the individual approaches. Different other combinations of the feature extraction techniques were also executed and compared to compare with the proposed approach. While the other standard combinations achieved the kappa coefficient accuracies of between 0.50 and 0.60, the proposed algorithm achieved a kappa coefficient accuracy of 0.64, which is significantly higher than the other approaches. The novelty of the approach is based on the combination of user specific band and subject specific band and two feature extraction methods. In the future, other combinations of feature extraction and classification approaches will be performed to further improve the accuracy.  
[PERSON] designed the task, supervised research, analyzed the results, provided feedback and gave new ideas, revised the draft and approved the final version of the article. [PERSON] implemented test software, executed experimental work, analyzed the results and did revisions to the final article.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & **The Proposed** & **FFT + CV** & **FBCSP** & **CNN** & **CNN +** & **FBCSP,** & **MDA** & **AR** \\  & **Method** & **+ PCA**[9] & **[10]** & **[17]** & **VAE**[18] & **MIRSR**[31] & **[32]** & **[33]** \\ \hline \hline
1 & 0.84 & 0.80 & 0.82 & - & 0.52 & 0.40 & 0.71 & 0.62 \\ \hline
2 & 0.36 & 0.28 & 0.51 & - & 0.34 & 0.20 & 0.31 & 0.44 \\ \hline
3 & 0.66 & 0.55 & 0.80 & - & 0.43 & 0.21 & 0.75 & 0.65 \\ \hline
4 & 0.52 & 0.49 & 0.51 & - & 0.90 & 0.95 & 0.47 & 0.42 \\ \hline
5 & 0.30 & 0.30 & 0.37 & - & 0.64 & 0.85 & 0.19 & 0.40 \\ \hline
6 & 0.55 & 0.41 & 0.33 & - & 0.64 & 0.61 & 0.20 & 0.35 \\ \hline
7 & 0.79 & 0.79 & 0.83 & - & 0.55 & 0.55 & 0.78 & 0.51 \\ \hline
8 & 0.84 & 0.68 & 0.78 & - & 0.50 & 0.85 & 0.77 & 0.64 \\ \hline
9 & 0.89 & 0.77 & 0.72 & - & 0.51 & 0.74 & 0.73 & 0.64 \\ \hline AVG. & 0.64 & 0.56 & 0.63 & 0.57 & 0.56 & 0.59 & 0.55 & 0.52 \\ \hline \hline \end{tabular}  
*MIRSR: mutual information-based rough set reduction; MDA: Multiple discriminant analysis; AR: Autoregressive; for [17], the result was converted to kappa value.  
\end{table}
Table 5: Comparison of the results of the proposed method with the results of the state-of-art methods.