# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 5 Results and Discussion
### Discussion of Algorithm on Brain Signals
\begin{table}
\begin{tabular}{c c 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** & \(p\)**-Value** \\ \hline CSP & 77.8 & 2.8 & 93.1 & 40.3 & 9.7 & 43.1 & 62.5 & 87.5 & **87.5** & 56 & 32.09 & 0.013 \\ GLRCSP & 72.2 & 16.7 & 87.5 & 34.7 & 11.1 & 30.6 & 62.5 & 87.5 & 76.4 & 53.2 & 28.47 & 0.028 \\ CCSP1 & 72.2 & 20.8 & 87.5 & 13.9 & 1.4 & 30.6 & 62.5 & 87.5 & 76.4 & 50 & 31.71 & 0.112 \\ CCSP2 & 77.8 & 6.9 & 94.4 & 40.3 & 8.3 & 36.1 & 58.3 & 90.3 & 80.6 & 54.8 & 31.71 & 0.013 \\ DLCSPAuto & 77.8 & 2.8 & 93.1 & 40.3 & 13.9 & 43.1 & 63.9 & 87.5 & **87.5** & 56.6 & 31.46 & 0.011 \\ DLCSPcv & 77.8 & 1.4 & 93.1 & 40.3 & 11.1 & 25 & 62.5 & 87.5 & 73.6 & 52.5 & 32.13 & 0.046 \\ DLCSPVcdiff & 77.8 & 1.4 & 93.1 & 40.3 & 11.1 & 25 & 62.5 & 87.5 & 73.6 & 52.5 & 32.13 & 0.046 \\ SSRCSP & 77.8 & 6.9 & 94.4 & 40.3 & 12.5 & 37.5 & 58.3 & **94.4** & 80.6 & 55.9 & 31.49 & 0.008 \\ TRCSP & 77.8 & 8.3 & 93.1 & 41.7 & 25 & 34.7 & 62.5 & 91.7 & 83.3 & 57.6 & 29.41 & 0.005 \\ WTRCSP & 77.8 & 9.7 & 93.1 & 40.3 & 31.9 & 23.6 & 62.5 & 91.7 & 81.9 & 56.9 & 29.54 & 0.009 \\ SRCSP & 77.8 & 26.4 & 93.1 & 33.3 & 26.4 & 27.8 & 56.9 & 91.7 & 84.7 & 57.6 & 27.87 & 0.004 \\ SCSP & 81.9 & 12.5 & 93 & 45.8 & 27.8 & 27.8 & 59.7 & **94.4** & 83.3 & 58.5 & 29.45 & 0.003 \\ SCSP1 & **83.3** & 34.7 & **95.8** & 44.4 & 30.5 & 33.3 & **69.4** & **94.4** & 83.3 & **63.3** & **25.84** & 0.002 \\ SOSP2 & **83.3** & 20.8 & 94.3 & 41.7 & 26.4 & 22.2 & 56.9 & 90.3 & **87.5** & 58.2 & 29.44 & 0.003 \\ BGA-CSP2 & 56.3 & **50** & 81.7 & **50.4** & **54.5** & **55.9** & 54.2 & 58.4 & 70 & **59.1** & **9.73** & **0.032** \\ CSP (C3,C4, CZ) & 51.38 & 6.94 & 86.10 & 36.1 & 6.94 & 22.22 & 15.26 & 73.60 & 77.76 & 41.81 & 29.64 & - \\ \hline \hline \end{tabular} \(p\)-value, the paired \(t\)-test between results of CSP (C3, C4, CZ (all 3)) [39], with the seventh (CSP with three channels (CSP2)) model scenario and some previous methods.  
\end{table} 
Table 21: Checking the best kappa results of different methods compared with the proposed method [66]. Figure 16: Examining the best kappa different methods for selecting features (along with feature extraction, reducing features and channels, etc.) on three channels with more than three channels by combining different selected bands and so on. In most studies involving filter banks, first, all filter banks are applied to noise space filters to reduce noise, and then, features are extracted from them. Next, all the features of all filter banks are added together, and we select a set of features from them. In addition, in reducing the features for channels or selecting a channel, an interval of eight to 30 frequencies is considered. In this study, two or three filter banks with two important channels are applied to reduce the noise and extract more important information. We examined various algorithms tested on brain signals. We also proposed a new algorithm based on the Bond Graph method, i.e., BGA. Our design goal for the new algorithm is to effectively reduce the dimension of features and the effect of specified filter banks and channels. Most of these algorithms show close convergence with each other. Although the accuracy values for some subjects were low in some cases, it was still far enough from the local minimum trap. In most cases, BGA showed the best performance compared to the other algorithms. Figure 17: Checking the best kappa results of different methods compared with the proposed method. Figure 18: Converging of subjects 3 and 8 with algorithms on the seventh model scenario (CSP with three channels (PSO-CSP2, GA-CSP2, BGA-CSP2, and QGA-CSP2)).