# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 4 Experiment and Results
### Experiments and Scenarios
Two filter banks, i.e., 2 and 5 with two channels, i.e., 8 and 12 which are representing C3 and C4, respectively, are used. 2. The second model scenario: a new hybrid filter (combination of two, three, or four filter banks) with one channel with four algorithms with ELM classification is examined for each participant. A hybrid filter bank with two channels, i.e., 8 and 12, representing C3 and C4, respectively, is used. 3. The third model scenario: In this model scenario, we first  
average accuracy of our proposed algorithm is better than the others. In channel 8, our algorithm demonstrated a slightly better accuracy compared with PSO and GA (i.e., 0.1%) and outperformed compared with the QGA by about 1%. In channel 12, our algorithm outperformed other algorithms by about 1-2%. However, for some test subjects, the highest accuracy was not obtained by our proposed algorithm. For example, in channel 8, the highest accuracy was obtained by GA for the first, seventh, and ninth subjects, i.e., 62.2%, 61.4%, and 60.8%, respectively. However, in the same channel, our algorithm had better accuracy for the second, third, fifth, and eighth subjects, i.e., 61.7%, 61.9%, 60.8%, and 60.3%, respectively. In channel 12, our proposed algorithm had the highest accuracy for the first, second, third, seventh, eighth, and ninth subjects, i.e., 60.5%, 60.3%, 63.0%, 66.6%, 60.4%, and 62.5%, respectively. Table 8 shows the results of a dimensional reduction by 100 features performed by four algorithms on filter bank number 5 (frequency range was 20 to 24). The average accuracies for channel 8 were calculated as 60.8%, 61.2%, 61.4%, and 60.1%, respectively, and for channel 12, they were calculated as 61.5%, 61.9%, 63.2%, and 61.1%, respectively for four algorithms, i.e., PSO, GA, BGA (with the third model), and QGA. In general, for each channel and subject, the highest accuracies are about 0.2 to 1% higher than the other algorithms. Our proposed algorithm performed better for most of the cases. However, for some test subjects, the highest accuracy was not obtained by our proposed algorithm. For example, in channel 8, the highest accuracies were obtained by GA for the first, third, and sixth subjects, i.e., 61.6%, 62.0%, and 63.2%, respectively, and for the ninth subject, the highest accuracy, i.e., 61.8%, was obtained by QGA. In channel 12, the highest accuracy, i.e., 65.2%, was obtained for the fourth subject by PSO algorithm. The rest of the highest accuracies was obtained by our proposed algorithm in both channels for different subjects. \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}  & **Channel 8** & **Channel 12** & **Channel 8** & **Channel 12** & **Channel 8** & **Channel 12** & **Channel 8** & **Channel 12** \\ \hline
1 & 60.3 & 59.1 & **62.2** & 59.0 & 60.0 & **60.5** & 59.1 & 59.8 \\
2 & 60.9 & 58.9 & 60.3 & **60.3** & **61.7** & **60.3** & 58.8 & 59.5 \\
3 & 60.5 & 61.6 & 61.3 & 62.4 & **61.9** & **63.0** & 59.8 & 61.7 \\
4 & **65.5** & 64.5 & 63.7 & 64.3 & 64.2 & 65.0 & 63.2 & 63.8 \\
5 & 60.1 & 62.3 & 58.9 & 62.8 & **60.8** & 62.4 & 58.3 & 62.0 \\
6 & **63.0** & 60.1 & 61.7 & 59.6 & 61.9 & 60.1 & 61.5 & **60.4** \\
7 & 61.1 & 64.4 & **61.4** & 65.0 & 60.8 & **66.6** & 60.5 & 65.0 \\
8 & 59.1 & 59.1 & 58.6 & 60.1 & **60.3** & **60.4** & 58.8 & 59.7 \\
9 & 59.8 & 61.6 & **60.8** & 62.0 & 60.1 & **62.5** & 60.2 & 61.1 \\ Ave & 61.1 & 61.3 & 61.0 & 61.7 & **61.3** & **62.3** & 60.0 & 61.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Feature selection accuracy of PSO, GA, BGA, and QGA with 100 features, FB2, and ELM for 100 iterations.