# 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
Table 20 and Figure 16 show the results of kappa for GA, BGA, and PSO with three channels, and two proposed models (i.e., FM and CSP) are compared with the methods of some articles that use different channels and different filter banks [39; 48]. The best average kappa in this experiment is related to FBCSP in [48], which was 4.41% higher than  
Figure 14: The best results of different methods for feature selection (along with feature extraction, feature reduction, channels, etc.) on two channels, 8 and 12, by combining different select bands. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & **PSO** & **GA** & **BGA** & **PSO** & **GA** & **BGA** & **CSP** & **CSP** & **CSP** \\  & **FM3** & **FM3** & **CSP2** & **CSP2** & **CSP2** & **ALL** & **3.0** & **8.55** \\  & & & & & & & & **Channels** & **Channels** \\ \hline
1 & 26.72 & 26.06 & 24.38 & 50.40 & 48.26 & 56.32 & 81.94 & 51.38 & **83.32** \\
2 & 23 & 23.3 & 22.18 & 44.06 & 44.68 & **50.04** & 12.5 & 6.94 & 20.82 \\
3 & 70.5 & 69.76 & 65.06 & 63.92 & 50.74 & 81.7 & 93.04 & 86.1 & **94.28** \\
4 & 26.1 & 29.04 & 24.18 & 46.36 & 49.62 & **50.42** & 45.82 & 36.1 & 41.66 \\
5 & 19.84 & 18.84 & 19.8 & 42.24 & 41.02 & **54.46** & 27.76 & 6.94 & 26.38 \\
6 & 24.64 & 23.44 & 22.46 & 49.22 & 47.16 & **55.94** & 27.76 & 22.22 & 22.22 \\
7 & 22.8 & 23.76 & 21.5 & 45.4 & 49.08 & 54.18 & **59.72** & 15.26 & 56.94 \\
8 & 28.78 & 31.82 & 31.1 & 49.18 & 46.4 & 58.44 & **94.44** & 73.6 & 90.26 \\
9 & 53.5 & 52.62 & 48.46 & 60.56 & 54.94 & 70.04 & 83.32 & 77.76 & **87.50** \\ Avg & 32.88 & 33.18 & 31.01 & 50.14 & 47.98 & **59.06** & 58.47 & 41.81 & 58.15 \\ Std & 16.26 & 15.89 & 14.61 & 6.96 & 3.69 & 9.73 & 29.47 & 29.64 & 29.44 \\ \(p\)-value & 0.112 & 0.114 & 0.076 & 0.176 & 0.270 & **0.032** & **0.003** & - & **0.003** \\ \hline \hline \end{tabular} \(p\)-value, the paired \(t\)-test between results of CSP (C3, C4, CZ (all 3)) [39], with the fifth (four methods with three channels (FM3)) and seventh (CSP with three channels (CSP2)) model scenarios and some previous methods.  
\end{table} 
Table 19: Examining the best kappa different methods for selecting features (along with feature extraction, reducing features and channels, etc.) on three channels, 8, 10, and 12, by combining different selected bands. BGA with CSP. In this scenario, the other methods, in general, had 1.5-3% better kappa compared to BGA with CSP. This is because in BGA, we use only three channels with 60% of the frequency range, while in other studies, 22 channels with the entire frequency range have been used. Considering the significant difference in the channels and frequency range, the BGA had only 1.5% less kappa compared to the smallest kappa in the other works. Table 21 and Figure 17 show the results of kappa for various implementation methods with CSP [66]. The kappa values are in the range of 50% to 63%. BGA with CSP, three channels, and 60% of the frequency range obtained the second-best average kappa, i.e., 59%. While in all the other methods, all channels with all frequency ranges have been used. In Table 21, the statistical significance of performance between CSP (C3, C4, CZ) [39] with two of our model scenarios and other methods is evaluated. We used a paired \(t\)-test for the comping of \(p\)-value. All of the methods have a \(p\)-value of less than 0.05 (\(p\)-value \(<\) 0.05). Figure 18 shows one training epoch of the increasing accuracy of subjects 3 and 8 with algorithms using the seventh model scenario (CSP with three channels (CSP2)). BGA has the best converging to reach optimal results.