## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources
Notably, the four-class classification problem demonstrates to pose severe implications for the classification performance when LSA-MMFT is employed. Hence, assigning Se4 the role of TD and removing five low-beneficial SDs yield a remarkable CA, while selecting S6 as the TD and removing seven low-beneficial SDs also yield remarkable CAs for both the inter-session and inter-subject classification scenarios using the BCI competition IV-a dataset, respectively. Furthermore, for the four-class inter-subject scenario using our own recorded SSMVEP subjects, the highest CA is observed when S7 is assigned the role of TD and six low-beneficial SDs are removed, while the highest CA is observed when S1 is selected as the TD and two low-beneficial SDs are removed in the scenario using our own recorded MI subjects. However, removing a single low-beneficial SD in each iteration from a pool of domains consisting of both low- and high beneficial domains results in a decrease in the CA across individual TDs, mainly as a result of individual variations across the domains. Table 5 depicts the mean prediction rate for a comparative performance evaluation across all four domain selection algorithms under two-class, three-class, and four-class inter \begin{table}
\begin{tabular}{c 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
2 & 6 & 4 & 1 & 7 & 2 & 3 & 1 & 3 & 1 & 4 & 2 & 6 \\ \hline
3 & 6 & 6 & 1 & 7 & 6 & 7 & 1 & 7 & 4 & 5 & 3 & 4 \\ \hline
6 & 2 & 1 & 7 & 1 & 1 & 7 & 7 & 5 & 3 & 7 & 6 & 2 \\ \hline
7 & 4 & 4 & 7 & 2 & 7 & 4 & 7 & 4 & 7 & 5 & 6 & 3 \\ \hline
8 & 6 & 6 & 7 & 7 & 5 & 5 & 2 & 7 & 7 & 2 & 3 & 3 \\ \hline
9 & 1 & 1 & 7 & 7 & 7 & 6 & 5 & 1 & 3 & 6 & 2 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal selected combination of beneficial source domains using LSA-MMFT. session classification scenarios, whereby X-Sessions represents the inter-session classification scenario using our own recorded SSMVEP sessions. As such, DTE outperforms both ROD and LSA-MMFT, with a mean CA of 85.1% being recorded when DTE is employed for a two-class problem. However, a 9.1% increase in CA is observed, with a superior mean CA of 94.2% being recorded when EMC-MMFT is applied in a two-class inter-session classification scenario. Moreover, for a three-class inter-session scenario, DTE yields a remarkable mean CA compared to both ROD and LSA-MMFT, with a mean CA of 68.9% being recorded when DTE is employed. However, a superior mean CA of 84.1% is recorded when EMC-MMFT is applied, whereby a 15.2% increase in CA is observed compared to DTE. Furthermore, performance evaluation has also been carried out for a more complex classification problem--in this instance, a four-class problem. Superior performances across all inter-session classification scenarios under Table 5 is denoted by bold texts. As such, DTE yields a superior performance compared to ROD and LSA-MMFT, with the highest mean CA of 61.2% being recorded when DTE is employed in a four-class inter-session scenario. However, a 13.85% increase in the mean CA is observed when EMC-MMFT is employed and compared with DTE, with a superior mean CA of 75.05% being recorded when EMC-MMFT is employed. In this case, the superior performances recorded when EMC-MMFT is employed can be attributed to the minimal effect of NT, a result of the best selected combination of closely related highly beneficial SDs, which, in turn, significant increases the CA across individual TDs when TL is applied. However, a decline in CA when ROD, DTE, and LSA-MMFT are employed can be attributed to removing only a single low-beneficial SD in each iteration, since the remaining low-beneficial SDs still pose severe implications for the CA in the next iteration as a result of each domain responding differently to another domain.