## 4 Results
### Experimental Results
#### 4.2.1 Domain Selection Based on a Two-Class Problem
Hence, a superior CA of 95% is recorded when S6 is selected to be the TD, and S2, S4, S7, and S9 are selected as the optimal combination of highly beneficial SDs using EMC-MMFT, as illustrated in Figure 6. Notably, a 40% increase in CA is observed across S1, with the highest CA of 85% being recorded when EMC-MMFT is applied, while a 45% CA is recorded when DTE is employed. In this case, selecting only the best combination of highly beneficial SDs is demonstrated to effectively increase CA in the TD. Hence, a 33.75% increase in CA is observed across S8 when ROD is compared with EMC-MMFT, whereby a CA of 80% is recorded when EMC-MMFT is employed and a CA of 46.25% when ROD is applied. Moreover, removing a single less-beneficial domain in each iteration is demonstrated to significantly affect the CA, with the lowest CA of 43.75% being observed across S5 when LSA-MMFT is employed. However, selecting only highly beneficial domains results in a 23.75% increase in CA, whereby an accuracy of 67.5% is recorded across S6 when EMC-MMFT is applied. The impact of less- and highly beneficial domains on transfer mapping performance is further investigated using nine of our own MI subjects. As such, the EMC-MMFT framework is further compared with the DTE, ROD, and LSA-MMFT algorithms under a two-class inter-subject classification scenario. Consequently, the highest CA of 85% is observed across S2 when DTE is employed, while the highest CAs of 82.5% and 78.75% are recorded across S2 when LSA-MMFT and ROD are applied, respectively. In this case, superior prediction rates are observed across all the individual TDs when EMC-MMFT is employed, whereby the highest CA of 92.5% is recorded across S9 when S3, S4, S5, S6, and S8 are selected as the optimal combination of highly beneficial SDs using EMC-MMFT, as illustrated in Figure 7. Notably, a significant increase of 35% in CA is observed across S9 when EMC-MMFT is compared with DTE, whereby a CA of 57.5% is recorded when DTE is employed, while EMC-MMFT demonstrates its superiority, with a CA of 92.5%  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**TD**} & \multicolumn{3}{c}{**Two-Class (SDs)**} & \multicolumn{3}{c}{**Three-Class (SDs)**} & \multicolumn{3}{c}{**Four-Class (SDs)**} \\ \hline  & **X-Se** & **X-S1** & **X-S2** & **X-S3** & **X-Se** & **X-S1** & **X-S2** & **X-S3** & **X-Se** & **X-S1** & **X-S2** & **X-S3** \\ \hline \multirow{2}{*}{1} & \(2,3,4,\) & \(2,3,5,\) & \multirow{2}{*}{\(7,8\)} & \multirow{2}{*}{\(2,4\)} & \(2,3,7\) & \( \begin{array}{c}4,5,6,\\ 7,8\end{array} \) & & \multirow{2}{*}{\(3,8,9\)} & \(2,3,4,7\) & \(5,7,8,9\) & \(2,3,5,6\) & \(2,7,9\) & \(2,3,4,5\) & \(2,3,4,5\) \\ \hline \multirow{2}{*}{2} & \(1,4,6,\) & \multirow{2}{*}{\(3,8\)} & \multirow{2}{*}{\(3,5,6,7\)} & \multirow{2}{*}{\(1,5,6,7\)} & \multirow{2}{*}{\(1,5,6,7\)} & \multirow{2}{*}{\(7,8\)} & \multirow{2}{*}{\(3,7\)} & \multirow{2}{*}{\(1,9\)} & \multirow{2}{*}{\(1,3,5,7\)} & \multirow{2}{*}{\(5,8\)} & \multirow{2}{*}{\(1,3,4,\)} & \multirow{2}{*}{\(3,5,7\)} \\  & & & & & & & & & & & & \\ \hline \multirow{2}{*}{3} & \(1,5,8\) & \multirow{2}{*}{\(4,5,8,9\)} & \multirow{2}{*}{\(2,4,7\)} & \multirow{2}{*}{\( \begin{array}{c}2,4,5,\\ 6,8\end{array} \)} & \multirow{2}{*}{\(5\)} & \multirow{2}{*}{\(2,7,8\)} & \multirow{2}{*}{\(4\)} & \multirow{2}{*}{\(1,4,8,9\)} & \multirow{2}{*}{\(1,2,5,9\)} & \multirow{2  
being recorded. Moreover, utilizing only the best selected highly beneficial SDs is proven to effectively increase the CA in the TD, with a 32.5% increase in CA observed across S4 when EMC-MMFT is compared with both DTE and LSA-MMFT. In this case, a CA of 73.75% is recorded when EMC-MMFT is employed, while a CA of 41.25% is recorded when DTE and LSA-MMFT are applied, respectively. The results further illustrate that removing a single less-beneficial domain in each iteration can significantly deteriorate the classification performance, as a result of each subject responding differently to another different subject.