## 3 Experiments and Results
### New Combinations of Signals Using Four Common Methods  
#### 3.3.1 CSP Using New Combinations of Signals  
Table 2 shows the results of ten extracted features as five pairs of features by CSP. Three classifiers, LDA, KNN, and ELM, were used in our experiments. For the evaluation, we calculated the average kappa of all subjects for the left and right hands. Note that the new combinations of signals are presented as a combination of filter banks with their numbers. For example, a combination of filter bank I and filter bank J is expressed as \"FB I and J\". Based on Table 2, in the comparison of KNN and ELM, KNN has the lowest kappa. LDA has a slightly better kappa compared to ELM and KNN. The highest kappa value is for FB 5 and 6 (68.70%). CSP could remove noise well and extract good, suitable features for the left and right. LDA and ELM classifiers were successful in detecting suitable distinctive patterns between two classes, while there may be more of the same patterns for two classes. In general, Table 3 shows the results of five pairs of features (ten features) for subjects with CSP that were used for our experiments with the LDA classifier. The details of subjects with LDA show slightly better accuracy (left and right hands). Except for two subjects, the rest have high kappa values (more than 60%), which indicates that some subjects achieved a low kappa value and other subjects achieved a high kappa value. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Subject** & **CSP(m)** & **SVM** & **LDA** & **ELM** & **KNN** & **TREE** \\ \hline S1 & 4 & 0.664 & 0.547 & 0.634 & 0.687 & 0.652 \\ S1 & 5 & 0.676 & 0.582 & 0.601 & 0.681 & 0.658 \\ S2 & 4 & 0.624 & 0.561 & 0.677 & 0.619 & 0.576 \\ S2 & 5 & 0.615 & 0.569 & 0.698 & 0.644 & 0.559 \\ S3 & 4 & 0.615 & 0.571 & 0.657 & 0.639 & 0.546 \\ S3 & 5 & 0.632 & 0.581 & 0.668 & 0.664 & 0.552 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of all states for 3D lips for A and M. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Classifier** & **FB 1 1 and 5** & **FB2 and 5** & **FB4 and 5** & **FB3 and 6** & **FB5 and 6** & **FB4 and 8** & **FB6 and 8** & **FB5 and 9** & **FB8 and 9** \\ \hline ELM & 63.31 & 62.55 & 63.51 & 63.19 & **68.34** & 63.34 & 66.92 & **67.79** & 62.21 \\ \hline KNN & 51.99 & 50.96 & 51.81 & 50.97 & **59.04** & 47.95 & 55.36 & **56.02** & 49.57 \\ \hline LDA & 64.37 & 64.31 & 64.64 & 61.52 & **68.70** & 61.06 & 64.24 & **68.22** & 63.34 \\ \hline \hline \end{tabular} \({}^{1}\) FB I and J mean filter bank I and filter bank J.  
\end{table} 
Table 2: Average kappa values of three classifiers using hybrid signals from the left and right hands with 10 features. Table 4 shows the details of the average sensitivity of subjects for some of the new combinations of signals. It shows that the difference between left- and right-hand classes is less than 2%. And the difference between sensitivity and accuracy is small. Table 5 shows the results of ten extracted features (five pairs of features) with CSP that were used for our experiments with three classifiers: LDA, KNN, and ELM. For the evaluation, the average kappa values of all subjects were calculated for the hand and foot. Based on Table 5, in the comparison of KNN and ELM, ELM has the lowest kappa. LDA has a slightly better kappa than ELM and KNN. The highest kappa value is for FB 3 and 9 (95.90%). CSP can remove noise very well and extract excellent features for the hand and foot. The LDA and KNN classifiers are successful in detecting the most distinctive patterns between two classes. In general, Table 6 shows the results of five pairs of features (ten features) for subjects with CSP that were used for our experiments with the LDA classifier. The details of subjects