## 3 Experiments and Results
### New Linear and Nonlinear Bond Graph Classifiers  
Table 18 shows results related to the performance of the proposed linear classifier, SVM, and LDA. These results are based on the preprocessed public speech imagery dataset that was examined. The results revealed average accuracies of 53.45%, 53.84%, and 61.02% for Channels 1, 2, and 4, respectively, with the second proposed model and 65.19% for Channel 3 with LDA. The result for the first proposed model is 60.44%, which is 5% lower than that of LDA and 5% higher than that of SVM. The first proposed model and LDA both obtained good results of approximately 84.21% from Channel 3, and the highest result obtained was 86.84% accuracy for the third subject and Channel 4 using two new models. In general, with this preprocessed dataset, the new models achieved suitable performance and accuracy. The remaining results are presented in Table 18 for further review. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **S1** & **S2** & **S3** & **S4** & **S5** & **Avg** & **Std** & \(p\)**-Value** \\ \hline NCSCSP\_ELM & 90.30 & 88.24 & 87.41 & 90.14 & 90.94 & 89.40 & 1.34 & 0.0085 \\ NCSCSP\_KNN & 96.92 & **97.40** & 94.01 & **99.51** & **98.62** & 97.29 & 1.88 & 0.0048 \\ NCSCSP\_LDA & **98.21** & 97.01 & **97.39** & 98.92 & 98.19 & **97.94** & **0.67** & 0.0054 \\ \hline \hline \end{tabular}  
* The \(p\)-values are from paired \(t\)-tests between results of CSP (C3, C4, CZ) [44] and other methods.  
\end{table} 
Table 16: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **Avg** & **Std** \\ \hline CSP\_Channels & 81.94 & 12.5 & 93.04 & 45.82 & 27.76 & 27.76 & 59.72 & **94.44** & **83.32** & 58.48 & 29.47 \\ CSP 8.55 Channels & **83.32** & 20.82 & 94.28 & 41.66 & 26.38 & 22.22 & 56.94 & 90.26 & 87.5 & 58.15 & 29.44 \\ CSS 13.22 Channels & **83.32** & 34.72 & 95.82 & 44.44 & 30.54 & 33.34 & 69.44 & **94.44** & **83.32** & 63.26 & 25.84 \\ oFBCSP & 72 & 38.9 & 82.2 & 38.1 & 56.3 & 25.5 & 80 & 78.5 & 74.7 & 60.7 & 20.33 \\ sFBCSP & 72.1 & 39.5 & 81.6 & 38.4 & 59.2 & 28.7 & 83 & 78.6 & 76 & 61.9 & 19.94 \\ aFBCSP & 74.7 & 41.6 & 82.4 & 40 & **60.8** & 30.9 & **84.9** & 78.7 & 77.2 & 63.5 & 19.61 \\ NCSCSP \({}^{1}\)ELM & 75.95 & **49.61** & 94.93 & 60.76 & 48.46 & 61.41 & 66.2 & 88.29 & 69.47 & 68.34 & **14.98** \\ NCSCSP \({}^{1}\)KNN & 70.68 & 29.89 & 96.65 & 41.21 & 38.63 & 47.18 & 53.53 & 87.96 & 65.63 & 59.04 & 21.56 \\ NCSCSP \({}^{1}\)LDA & 77.09 & 49.42 & **97.02** & **62.96** & 44.93 & **64.44** & 67.48 & 92.02 & 62.97 & **68.70** & **16.46** \\ \hline \hline \end{tabular}  
* New combinations of signals with CSP.  
\end{table} 
Table 17: Evaluation of the best kappa values of the results of different methods with the best average accuracy of the combined signals of the filter banks for the right and left hands. Table 19 shows the performance results of the proposed nonlinear classifiers SVM, LDA, and KNN. These results are similar to those in the previous table for the preprocessed speech imagery data examined. The results show average accuracies of 57.94%, 59.49%, 65.71%, and 62.29% for KNN, which had the highest values. The accuracy of the proposed algorithm was 5-6% lower. Our results were checked for each individual. The proposed model exhibited a good performance in some channels. The first subject performed 5% better in Channels 1 and 3. The third subject with Channel 1 was 10% better than those of LDA and KNN. However, in many cases, the others exhibited the best performance. Table 19 presents the results of the nonlinear performance comparison and interpretation. Tables 20 and 21 show the proposed linear and nonlinear classifier performance, respectively. These results are related to the preprocessed left- and right-image notion data examined. Table 20 lists the 22 channels with the total number of individuals.