## 3 Experiments and Results
### New Linear and Nonlinear Bond Graph Classifiers
Nine of the brain channels had between 50.69% and 54.47% accuracy, which are related to the better performance of the proposed linear model. Eleven channels have between 50% and 53.85% accuracy, which is obtained with SVM, and four channels have between 50.38% and 53.47% accuracy with LDA, which has the best average. The two channels have the same degree of accuracy, as is intended for both. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Channel** & **SVM** & **LDA** & **SG Linear M1** \\ \hline \multirow{8}{*}{Avg} & 1 & 0.579 & 0.487 & 0.501 & 0.553 \\  & 2 & 0.595 & 0.527 & 0.513 & 0.554 \\ \cline{1-1}  & 3 & 0.657 & 0.513 & 0.553 & 0.56 \\ \cline{1-1}  & 4 & 0.623 & 0.513 & 0.558 & 0.561 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Nonlinear classifiers on Dataset SI. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Channel** & **SVM** & **LDA** & **BGL M1** & **BGL M2** & **BGL M3** \\ \hline \multirow{8}{*}{Avg} & 1 & 0.501 & 0.487 & 0.532 & 0.535 & 0.523 \\  & 2 & 0.513 & 0.512 & 0.517 & 0.538 & 0.527 \\ \cline{1-1}  & 3 & 0.553 & 0.652 & 0.604 & 0.596 & 0.554 \\ \cline{1-1}  & 4 & 0.558 & 0.593 & 0.601 & 0.61 & 0.601 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Linear classifiers on Dataset SI. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Channel** & **SVM** & **LDA** & **SVM** & **BG N-Linear** \\ \hline \multirow{8}{*}{Avg} & 1 & 0.579 & 0.487 & 0.501 & 0.553 \\  & 2 & 0.595 & 0.527 & 0.513 & 0.554 \\ \cline{1-1}  & 3 & 0.657 & 0.513 & 0.553 & 0.56 \\ \cline{1-1}  & 4 & 0.623 & 0.513 & 0.558 & 0.561 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Nonlinear classifiers on Dataset SI. The best results for each subject are related to the highlighted categories listed in Table 21. For the best performance accuracy, Subject 5 had 60.14% accuracy for Channel 22 with the SVM classifier, and Subject 7 obtained the best accuracy of 61.80% for Channels 6, 5, and 7 and 61.11% accuracy for Channel 13 with the LDA classifier. Finally, the proposed method for Subjects 4, 5, and 9 with Channels 15, 12, and 13, with accuracies of 60.41%, 61.11%, and 60.41%, respectively, showed good performance. As shown in Table 21, only one channel with an average accuracy of 49.76% belonged to the proposed method. The rest of the accuracies were distributed between different models, which achieved good accuracy. Further details are provided in Tables 20 and 21. In Tables 22 and 23, the test data results on the two benchmarks are shown as linear and nonlinear, respectively, and  
52 percent for the different band filters. Each filter bank was analyzed separately. For a better understanding, some variables are stated (M1 stands for formula window size 7, M2 stands for window size 14, and M3 stands for window size 35; S represents the selected window size for blending). The results in Figure 30 and Table 25 are expressed in the same way as the results in Figure 29 and Table 24. The difference is that the results are for Channel 10, which investigates nine subjects. Channel 10 resembles Channel 8 used for some filter banks, which obtains a maximum average of 52%. In this channel, the best accuracy was for Subject number 7 at 59.03% and 59.07% for bands 4 and 6, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Subject** & **M-Scale** & **FB1** & **FB2** & **FB3** & **FB4** & **FB5** & **FB6** & **FB7** & **FB8** & **FB9** \\ \hline \multirow{9}{*}{S7} & M1-S3 & 0.478 & 0.479 & 0.479 & 0.489 & 0.483 & 0.482 & 0.469 & 0.459 & 0.483 \\  & M1-S7 & 0.51 & 0.49  
The results in Figure 31 and Table 26 are similar to those in the previous figures and tables, and the only difference is in the channels, with Channel 12 being investigated in this test. The best results for Subjects 3, 5, and 7 are that Subject 3 had the best performance with filter bands 4 and 1 at 58.79% and 57.23%, and Subjects 7 and 5 had results that were1% to 2% lower than those of Subject 3, respectively.