## 3 Experiments and Results
### New Combinations of Signals Using Four Common Methods
#### 3.3.5 Discussion of Results  
Our proposed methods were compared with different previous methods based on CSP. For these comparisons, two datasets with new signals using CSP and FBCSP were used. Therefore, this paper generally explains the effectiveness of our proposed methods compared with previous methods. Table 14 uses different methods to compare the kappa of our proposed method (new signals and CSP) with those of CSP, GLRCSP, CCSP1, CCSP2, DLCSPauto, etc. [103]. The highest average kappa was obtained by our proposed method using a new combination of signals with CSP (FB 5 with 6 (68.60%)), which is higher than those of the other mentioned methods, with the smallest difference obtained by SCSP1 [103] (5%) and the largest one obtained by CCSP1 [103] (18%). It has an acceptable kappa (68%), which is sufficiently (8%) higher than the standard kappa (60%). With the proposed method (new combination of signals with CSP), the majority of the subjects obtained good kappa values (above 60%). But Subjects 2 and 5 had the lowest kappa values (between 45% and 50%). These kappa values are listed in Table 14 for the right and left hands. The \(p\)-values for all of the methods were calculated: most of the methods have \(p\)-values of less than 0.05 (\(p\)-value \(<\) 0.05), and some methods have values of more than 0.05 (\(p\)-value \(>\) 0.05). For new signals with CSP using LDA and ELM, the \(p\)-values are less than 0.05 (\(p\)-value = 0.009, \(p\)-value = 0.004, \(p\)-value \(<\) 0.05), and for new signals with CSP using KNN, the \(p\)-value is 0.052, which is higher than 0.05 (\(p\)-value = 0.052, \(p\)-value \(>\) 0.05). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Classifier–Channel–Signal Type** & **FB 1** & **FB2** & **FB3** & **FB4** & **FB5** & **FB6** & **FB7** & **FB8** & **FB9** \\  & **and 3** & **and 4** & **and 5** & **and 7** & **and 8** & **and 8** & **and 9** & **and 9** & **and 9** \\ \hline ELM\_Channel 8\_New Signal\_of\_FBs & 60.48 & 58.17 & 67.87 & 59.74 & **63.08** & 56.70 & 53.26 & **63.93** & 54.98 \\ \hline ELM\_Channel 12\_New Signal\_of\_FBs & 60.56 & 57.44 & 68.09 & 59.41 & **62.86** & 56.62 & 52.73 & **63.64** & 54.25 \\ \hline \end{tabular}  
* FB I and J mean filter bank I and filter bank J.  
\end{table} 
Table 13: ELM with PCA on Channels 8 and 12 on the left- and right-hand datasets composed of new hybrid signals. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Classifier–Channel–Signal Type** & **FB1** & **FB2** & **FB3** & **FB4** & **FB5** & **FB6** & **FB7** & **FB8** & **FB9** \\ \hline ELM\_Channel 8\_Normal\_FB & 50.51 & 50.12 & 50.4 & 49.96 & 50.6 & 50.28 & 50.39 & 50.08 & 50.08 \\ \hline ELM\_Channel 12\_Normal\_FB & 50.23 & 50.21 & 49.48 & 50.06 & 49.91 & 50.33 & 50.35 & 50.38 & 50.2 \\ \hline \end{tabular}  
* FB I and J mean filter bank I and filter bank J.  
\end{table} 
Table 12: ELM with PCA on Channels 8 and 12 on the left- and right-hand datasets composed of filter banks. Table 15 compares the kappa values of different methods [103; 104] with that of the proposed method (new signals and CSP). With the highest value of kappa, 95.89%, the proposed method greatly outperforms the other methods. Kappa values for the new signals with CSP using LDA for individuals are between 94% and 97%, which is a very high value for kappa. It is demonstrated that our model has increased accuracy and kappa to the highest values. For new signals with CSP using three classifiers, the \(p\)-values are less than 0.05 (\(p\)-value = 0.034, \(p\)-value = 0.010, \(p\)-value = 0.010). The three lowest \(p\)-values belong to SSCSPTDPs, NCSCSP_KNN, and NCSCSP_LDA, with \(p\)-values of 0.005, 0.010, and 0.012, respectively.