# 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} \hline \hline \multirow{2}{*}{**Subject**} & **PSO** & **PSO** & **GA** & **GA** & **BGA** & **BGA** & **QA** & **QA** \\  & **FB2 \& **5 \& **FB 5** & **FB 5** & **FB 5** & **FB 2 \& **5** & **FB 5** & **FB 2 \& **5** & **FB 2 \\  & **Ch8** & **Ch12** & **Ch8** & **Ch12** & **Ch8** & **Ch12** & **Ch8** & **Ch12** \\ \hline
1 & 60.7 & 60.4 & 61.6 & 59.4 & 61.7 & 61.1 & **61.9** & 59.8 \\
2 & **61.1** & 59.8 & 59.9 & 60.0 & **61.1** & **60.1** & 58.3 & 59.5 \\
3 & 60.6 & 62.4 & 62.0 & 61.8 & 62.0 & **63.3** & 61.4 & 61.7 \\
4 & **65.5** & 65.0 & 62.5 & 64.9 & 65.1 & 64.4 & 62.2 & 63.8 \\
5 & 61.5 & 62.9 & 59.5 & 63.5 & 62.3 & **63.9** & 59.1 & 62.0 \\
6 & 62.0 & 61.3 & **63.2** & 59.4 & 59.9 & 62.8 & 61.5 & 60.4 \\
7 & 60.6 & 62.7 & 61.8 & 66.0 & 60.7 & **67.4** & 60.9 & 65.0 \\
8 & 59.0 & 58.8 & 59.3 & 59.5 & 60.7 & **61.1** & 56.8 & 59.7 \\
9 & 59.6 & 61.8 & 60.7 & 62.4 & 60.8 & **64.6** & 61.0 & 61.1 \\ Ave & 61.2 & 61.7 & 61.2 & 61.9 & 61.6 & **63.2** & 60.3 & 61.4 \\ Std & 1.75 & 1.75 & 1.3 & 2.37 & 1.43 & 2.11 & 1.74 & 1.8 \\ \hline \hline \end{tabular}  
* The table is comparing first and second model scenarios using algorithms. FB1 & J means that the new signal is the combination of filter bank I with J. Ch 12 and Ch 8 mean Channel 12 and Channel 8, respectively.  
\end{table} 
Table 17: The best results of different methods for feature selection (with feature extraction, feature reduction, and channels, etc.) on a single channel 8 or 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** & **RF \& **RF, 8–30** & \\  & **FM2** & **FM2** & **FM2** & **CSP1** & **CSP1** & **CSP1** & **DFFS** & **Hz** & **RQNN** \\ \hline
1 & 63.17 & 64.62 & 62.17 & 68.94 & **70.88** & 70.32 & 63.69 & 66.52 & 61.11 \\
2 & 60.42 & 61.68 & 59.20 & 67.67 & 68.30 & **68.50** & 61.97 & 56.62 & 61.11 \\
3 & 84.64 & 85.10 & 84.05 & 81.66 & 71.17 & 89.50 & **91.09** & 90.36 & 79.17 \\
4 & 61.44 & 62.40 & 60.30 & 66.18 & **69.73** & 68.41 & 61.72 & 59.83 & 60.42 \\
5 & 61.31 & 63.44 & 60.61 & 65.27 & 68.80 & 68.20 & 63.41 & 57.63 & **71.53** \\
6 & **62.49** & 62.25 & 61.25 & 65.36 & 67.83 & **69.90** & 66.11 & 65.19 & 61.11 \\
7 & 65.14 & 67.48 & 61.11 & 68.93 & 70.72 & **70.74** & 59.57 & 64.86 & 58.33 \\
8 & 61.56 & 61.79 & 60.47 & 64.91 & 66.57 & **70.32** & 62.84 & 66.57 & 67.36 \\
9 & 68.94 & 66.68 & 68.21 & 76.98 & 69.51 & 82.18 & **84.46** & 77.69 & 79.36 \\ Avg & 65.46 & 66.16 & 64.15 & 69.54 & 69.29 & **73.12** & 68.32 & 67.25 & 66.59 \\ Std & 7.21 & 6.98 & 7.45 & 5.53 & 1.46 & 7.07 & 10.65 & 10.08 & 7.77 \\ \(p\)-value & 0.086 & 0.191 & **0.020** & 0.285 & 0.398 & **0.005** & - & 0.242 & 0.199 \\ \hline \hline \end{tabular}  
* \(p\)-value, the paired \(t\)-test between results of RF & DFFS [33], with fourth (four methods with two channels (FM2)) and sixth (CSP with two channels (CSP1)) model scenarios and some previous methods.  
\end{table} 
Table 18: 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. In Table 19, the statistical significance of performance between CSP with three channels (C3, C4, and CZ) with two other methods [39], the fifth model scenario (four feature extractions, i.e., time domain, frequency domain, autoregression, and wavelet coefficients with three channels), and the seventh model scenario (CSP with three channels) have been calculated. The methods are considered statistically significant when the \(p\)-value is less than 0.05. BGA with CSP in the seventh model scenario and two models from other papers could achieve \(p\)-values of 0.032, 0.003, and 0.003, respectively.