## 3 Experiments and Results
### New Combinations of Signals Using Four Common Methods
#### 3.3.3 Lagrangian Polynomial Equation Using New Combinations of Signals  
In this subsection, the results are explained for normal sub-band signals and new combinations of signals for single electrodes. The results in Table 10 show the average accuracies of new combinations of signals for Channel 5 using ELM and LDA. Table 11 shows 1 and 30 selected features for a channel using nine filter banks.  
Table 10 shows the results after converting data to coefficients by using the Lagrangian method for Channel 5. The 30 features selected as the best coefficients were considered for classification. The results increase from 51% to 58% for some combinations of filter banks. In comparing the best new combination of signals relative to the rest, the results vary between 2% and 6%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier-Channel**} & **FB 1 4** & **FB1** & **FB4** & **FB7** & **FB1** & **FB2** & **FB5** & **FB7** & **FB8** \\  & **and 7** & **and 8** & **and 8** & **and 8** & **and 9** & **and 9** & **and 9** & **and 9** & **and 9** \\ \hline ELM\_Channel 5 & 56.41 & 57.12 & 56.80 & 57.18 & **58.11** & 57.39 & 57.56 & 57.29 & **58.16** \\ \hline LDA\_Channel 5 & 57.41 & 57.35 & 57.54 & **58.06** & 57.98 & 57.78 & 58.02 & **58.25** & 58.03 \\ \hline \hline \end{tabular} \({}^{1}\) FB I and J mean filter bank I and filter bank J.  
\end{table}
Table 10: Average accuracy on Channel 5 with FBs and 30 selected features with Lagrangian formula.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Classifier/FS Numbers**} & **5** & **10** & **15** & **20** & **25** & **30** & **35** & **40** \\ \hline LDA\_Accuracy & 84.05 & 83.75 & 83.94 & 83.96 & 84.07 & 83.96 & 83.91 & 84.01 \\ KNN\_Accuracy & 86.51 & 86.47 & 86.52 & 86.52 & 86.58 & 86.46 & 86.55 & 86.45 \\ LDA\_Kappa & 68.11 & 67.49 & 67.89 & 67.91 & **68.13** & 67.92 & 67.83 & 68.01 \\ KNN\_Kappa & 73.01 & 72.94 & 73.04 & 73.03 & **73.15** & 72.92 & 73.09 & 72.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average accuracies of two classifiers based on feature selection (FBCSP) with different numbers of features.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Subject/FS Numbers**} & **5** & **10** & **15** & **20** & **25** & **30** & **35** & **40** \\ \hline
1 & 87.12 & 86.58 & 87.3 & 87.35 & 87.2 & 86.59 & 87.12 & 87.31 \\
2 & 55.94 & 55.73 & 55.8 & 55.92 & 55.72 & 56.52 & 56.46 & 55.67 \\
3 & 92.71 & 92.63 & 92.97 & 92.64 & 92.68 & 92.35 & 92.99 & 92.49 \\
4 & 55.81 & 55.91 & 55.58 & 55.66 & 56.1 & 55.92 & 55.58 & 56.47 \\
5 & 59.81 & 59.65 & 60.11 & 60.09 & 60.1 & 59.39 & 60.27 & 59.13 \\
6 & 59.59 & 59.34 & 59.42 & 59.59 & 60.26 & 59.5 & 59.39 & 59.05 \\
7 & 89.5 & 89.97 & 90.22 & 89.73 & 89.6 & 89.98 & 89.65 & 89.35 \\
8 & 93.34 & 93.33 & 93 & 93.41 & 93.55 & 93.27 & 93.4 & 93.64 \\
9 & 63.31 & 63.31 & 62.95 & 62.89 & 63.16 & 62.78 & 62.98 & 62.97 \\ Avg & 73.01 & 72.94 & 73.04 & 73.03 & **73.15** & 72.92 & 73.09 & 72.9 \\ Std & 16.01 & 16.06 & 16.16 & 16.09 & **15.97** & 15.96 & 16.05 & 16.12 \\ \(p\)-value & 0.0039 & 0.004 & 0.0041 & 0.0041 & 0.0039 & 0.0042 & 0.004 & 0.0039 \\ \hline \hline \end{tabular} \({}^{1}\) FB I and J mean filter bank I and filter bank J.  
\end{table}
Table 9: Kappa values of KNN classifier based on feature selection (FBCSP) with different numbers of features.