# 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 17 shows the best results for four different algorithms for different settings, i.e., different combinations of filter banks and channels. Our proposed algorithm (BGA) on filter bank 5 and channel 12 has obtained the best accuracies in most subjects. The average best accuracy, in this case, is 63.2%. The second-best result is related to GA with filter bank 5 and channel 12 (i.e., 61.9%). Subsequently, BGA and PSO have achieved the best results of 61.6% and 61.7%, respectively, when the combination of filter banks 2 and 5 are used. This indicates that the combination of filter banks 2 and 5 provides valuable information, but the results are still 1.5% less than the case using only filter bank 5. In Table 18 and Figure 14, the results of accuracy for GA, BGA, and PSO with two channels and two proposed models (i.e., FM and CSP) are compared with the methods of some articles [33; 65]. BGA with CSP obtained the best accuracies in most subjects with the best average accuracy, i.e., 73.12%, which is about 5-6% more than the other three algorithms, while the results related to GA and PSO are within the same range as the other three algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Subject** & & & \multicolumn{3}{c}{**FB-Same**} & \multicolumn{3}{c}{**FB-Hybrid**} \\ \hline \multirow{4}{*}{Avg} & **Size/Function** & F1 & F2 & F3 & F4 & F5 & R1\_HF & R2\_HF & R3\_HF & R4\_HF & R5\_HF \\  & Size 3 & **0.545** & 0.541 & 0.59 & 0.595 & 0.59 & 0.557 & 0.545 & **0.554** & **0.559** & 0.554 \\  & Size 4 & 0.523 & 0.56 & **0.592** & 0.589 & **0.623** & 0.535 & 0.516 & 0.522 & 0.524 & 0.534 \\  & Size 5 & 0.506 & 0.523 & 0.569 & 0.577 & 0.59 & 0.526 & 0.518 & 0.511 & 0.512 & 0.511 \\  & Size 6 & 0.537 & **0.587** & 0.588 & **0.604** & 0.617 & 0.571 & **0.555** & 0.55 & 0.542 & **0.578** \\  & Size 7 & 0.529 & 0.567 & 0.587 & 0.581 & 0.604 & **0.575** & 0.548 & 0.547 & 0.545 & 0.571 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Reduction data based on pooling model on filter banks with different window sizes for all filter banks with the same function in layers or randomly FB 5 and all of FBs and RF for 10 iterations. In Table 18, the statistical significance of performance between RF and DDFS with two other methods [33] with the fourth model scenario (four feature extractions, i.e., time domain, frequency domain, autoregression, and wavelet coefficients with two channels), and the sixth model scenario (CSP with two channels) have been calculated. A paired \(t\)-test is calculated for comparing \(p\)-values. The methods are considered of statistical significance when the \(p\)-value is less than 0.05. BGA with CSP with the fourth and sixth model scenarios could achieve \(p\)-values 0.020 and 0.005, respectively. In Table 19 and Figure 15, 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 [39]. BGA with CSP obtained the best kappa in most subjects with the average kappa, i.e., 59.06%, which is only 0.84% less than standard kappa (i.e., 60%). This result is significantly higher (about 18%) than the article [39] with three channels, while this difference is only about 0.5-1% compared to the results of the article [39] that uses all or 8.55 channels.