## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources
Significant variations in the classification performances across individual TDs further validate that the number of domains causes a minimal impact on the classification performance; however, discarding a single low-beneficial source domain in each iteration demonstrates to significantly affect the prediction rate, resulting in massive variations in the CAs across individual TDs. Number of Removed Low-Beneficial Source Domains Using DTE  
Table 3 shows the number of removed low-beneficial SDs across each individual target domain when DTE is employed to perform source selection based on two-class, three-class, and four-class inter-session and inter-subject classification scenarios. As such, removing four low-beneficial SDs and assigning Se1 the role of TD yields a remarkable CA; however, a decline in CA is observed across individual TDs when DTE is employed, based on the two-class inter-session classification scenario, while a remarkable CA is observed when S3 is assigned the role of TD and three low-beneficial SDs are removed for the two-class inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a database. Moreover, removing three low-beneficial SDs and selecting S6 as the TD \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**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
1 & 33 & 3 & 3 & 4 & 5 & 4 & 7 & 5 & 2 & 5 & 7 & 3 \\ \hline
7 & 1 & 2 & 1 & 3 & 6 & 2 & 1 & 7 & 3 & 2 & 1 & 2 \\ \hline
8 & 1 & 4 & 3 & 3 & 1 & 1 & 7 & 7 & 4 & 4 & 7 & 1 \\ \hline
9 & 6 & 2 & 4 & 1 & 6 & 6 & 1 & 6 & 7 & 2 & 4 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal selected combination of beneficial source domains using ROD. yields the highest CA across all the TDs when DTE is employed, based on a two-class inter-subject scenario utilizing SSMVEP subjects, while the highest CA is recorded when S2 is assigned the role of TD and four low-beneficial SDs are removed for the two-class inter-subject scenario using our own recorded MI subjects. Furthermore, when the DTE algorithm is employed for a three-class problem, the highest CA is recorded when Se1 is assigned the role of TD and a single less-beneficial SD is removed, while the highest CA is observed when S6 is assigned the role of TD and seven low-beneficial SDs are removed for both the inter-session scenario using the SSMVEP sessions and the inter-subject classification scenario using our own recorded MI subjects, respectively. In a similar manner, for a three-class inter-subject scenario using our own recorded SSMVEP subjects, the highest CA is observed when the DTE algorithm assigns S6 to be the TD and five low-beneficial SDs are removed, while the highest CA is observed when S9 is assigned the role of TD and three low-beneficial SDs are removed for the three-class inter-subject scenario using our own recorded MI subjects. Furthermore, a significant decline in CA across the individual TDs is observed for a more complex four-class classification problem when DTE is employed. In this case, assigning Se2 the role of TD and removing six low-beneficial SDs in the scenario utilizing the SSMVEP sessions yields a remarkable CA, while removing seven low-beneficial SDs and assigning S6 the role of TD for the scenario using our own recorded MI subjects acquired from the BCI competition IV-a database also yields a remarkable CA for both the inter-session and inter-subject classification scenarios, respectively.