# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 4 Experiment and Results
### Experiments and Scenarios
#### 4.4.5 Results of Sixth and Seventh Model Scenarios  
Table 15 presents the accuracy of the two models with CSP (sixth and seventh models that we defined earlier). In this case, a feature reduction by 100 features was performed, which was followed by the creation of new signals using the filter banks 2, 5, and 6. As a result, matrices M1 and M2 are created as input to CSP based on two and three primary channels, respectively. Our proposed algorithm shows the best performance, i.e., 73.12% and 79.53% average accuracies for M1 and M2, respectively. Kappa was calculated as 46.24% and 59.05% for M1 and M2, respectively. The average accuracy for M2 was also relatively good for the other three algorithms, i.e., 75.07%, 73.99%, and 71.18% for PSO, GA, and QGA, respectively. While in M1, the accuracy drops by 4 to 5% when using two channels and three filter banks. Our proposed algorithm's average accuracy improves 6.41% from M1 to M2, and the average Kappa improves 12.82% from M1 to M2. In PSO, this improvement is 5.53% and 11.06% for the average accuracy and kappa, respectively. In GA, this improvement is 4.7% and 9.43% for average accuracy and kappa, respectively. In QGA, this improvement is 5.22% and 10.45% for average accuracy and kappa, respectively. This shows that the addition of the channels and filter banks has a significant impact on improving the accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**BGA**} & \multicolumn{2}{c}{**QGA**} \\ \cline{2-9}  & **M1** & **M2** & **M1** & **M2** & **M1** & **M2** & **M1** & **M2** \\ \hline
1 & 63.17 & 63.72 & **64.62** & **65.49** & 62.17 & 62.88 & 62.03 & 62.83 \\
2 & 60.42 & 60.67 & **61.68** & **61.56** & 59.20 & 60.69 & 59.47 & 59.73 \\
3 & 84.64 & 84.66 & **85.10** & **85.10** & 84.05 & 83.79 & 84.61 & 84.31 \\
4 & 61.44 & 60.73 & **62.40** & **61.33** & 60.30 & 60.23 & 60.12 & 60.52 \\
5 & 61.31 & **60.18** & **63.44** & 59.98 & 60.61 & 60.05 & 60.25 & 59.79 \\
6 & **62.49** & 61.85 & 62.25 & 62.34 & 61.25 & 61.48 & 61.12 & 62.20 \\
7 & 65.14 & 59.17 & **67.48** & **63.15** & 61.11 & 58.41 & 62.65 & 59.60 \\
8 & 61.56 & **62.99** & 61.79 & 62.50 & 60.47 & 62.48 & **62.00** & 62.22 \\
9 & 68.94 & **68.38** & 66.68 & 67.60 & 68.21 & 66.20 & **70.82** & 66.39 \\ Ave & 65.46 & 64.71 & **66.16** & **65.45** & 64.15 & 64.02 & 64.78 & 64.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The results of algorithms with four methods (TD, FD, wavelet, and autoregression) on two channels (C3 = 8 and C4 = 12) with FB 2 & 5 (25). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**BGA**} & \multicolumn{2}{c}{**QGA**} \\ \cline{2-9}  & **M1** & **M2** & **M1** & **M2** & **M1** & **M2** & **M1** & **M2** \\ \hline
1 & **63.17** & 63.36 & 63.03 & 63.88 & 62.19 & 62.89 & 62.06 & 62.50 \\
2 & **62.30** & **61.50** & 61.65 & 61.15 & 61.09 & 59.81 & 60.08 & 60.02 \\
3 & 84.34 & **85.25** & **84.88** & 85.03 & 82.53 & 83.11 & 82.85 & 83.06 \\
4 & 63.20 & 63.05 & **64.52** & **63.83** & 62.09 & 61.68 & 62.26 & 61.66 \\
5 & 59.31 & **59.92** & 59.42 & 59.85 & 59.90 & 58.96 & **60.56** & 59.20 \\
6 & **61.81** & **62.32** & 61.72 & 61.93 & 61.23 & 60.91 & 60.88 & 60.76 \\
7 & 61.13 & 61.40 & **61.88** & **62.06** & 60.75 & 60.96 & 61.44 & 61.62 \\
8 & 65.41 & 64.39 & 65.91 & **65.34** & 65.55 & 63.93 & **66.06** & 63.66 \\
9 & 76.04 & **76.75** & **76.31** & 76.06 & 74.23 & 75.22 & 75.16 & 75.04 \\ Ave & 66.30 & 66.44 & **66.59** & **66.57** & 65.51 & 65.27 & 65.71 & 65.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The results of algorithms with four methods (TD, FD, wavelet, and autoregression) on three channels (C3 = 8 and CZ = 10 and C4 = 12) with FB 2 & 5 (25). We investigate three well-known functions, i.e., maximum, minimum, and average functions for different participants.