## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources
Table 6 displays the mean classification result for a comparative performance evaluation across all four domain selection algorithms under two-class, three-class, and four-class inter-subject classification scenarios. In this case, X-S1 represent an inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a dataset, while X-S2 represent an inter-subject classification scenario using our own recorded SSMVEP subjects, and X-S3 represent an inter-subject classification scenario using our own recorded MI subjects. Moreover, Superior performances across all inter-session classification scenarios under Table 6 is denoted by bold texts. From the comparison, one finds that the complexity of a classification problem including the effect of low-beneficial sources has severe implications for the classification performance when a single low-beneficial domain is removed from a pool of domains in each iteration. Consequently, ROD outperforms both DTE and LSA-MMFT, with a mean CA of 85.1% being recorded when ROD is employed. However, an 8.5% increase is observed when EMC-MMFT is employed in X-S1, with a superior mean CA of 93.9% being recorded. Moreover, a 20.5% increase and a superior mean CA of 84.5% are observed when EMC-MMFT is applied and compared with DTE. In this instance, DTE outperforms both ROD and LSA-MMFT, with a mean CA of 64% being recorded when DTE is employed in X-S2 for a two-class problem. A superior mean CA of 78.4% is recorded when EMC-MMFT is employed in X-S3 for a two-class problem, and \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Two-Class** & **Three-Class** & **Four-Class** \\ \cline{2-4}  & **X-Sessions** & **X-Sessions** & **X-Sessions** \\ \hline LSA-MMFT & 75.7 (11.26) & 64.1 (14.24) & 58.3 (6.79) \\ \hline DTE & 85.1 (21.3) & 68.9 (17.93) & 61.2 (14.43) \\ \hline ROD & 81.6 (20.5) & 63.2 (22.73) & 57 (12.88) \\ \hline EMC-MMFT & **94.2 (11.26)** & **84.1 (14.24)** & **75.06 (6.79)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean (%) and standard deviation (in parenthesis) for domain selection algorithms based on cross-session classification. a 10.8% increase is observed as compared to the performance of ROD, which achieves a mean CA of 67.6% and outperforms both DTE and LSA-MMFT. Moreover, for the three-class inter-subject scenario, ROD yields a remarkable mean CA compared to both DTE and LSA-MMFT, with a mean CA of 69.4% being recorded when ROD is employed in X-S1. However, a superior mean CA of 80% is recorded when EMC-MMFT is applied, while a 10.6% increase in CA is observed compared to ROD's. For the three-class inter-subject classification scenario based on X-S2, a superior mean CA of 72.8% is recorded when EMC-MMFT is applied, while a 30.5% increase in CA is observed when it is compared to DTE, whose highest mean CA of 42.3% is recorded when it is applied and compared to both ROD and LSA-MMFT. A superior mean CA of 64.3% is achieved when EMC-MMFT is implemented based on X-S3 for a three-class problem, with a 16.7% increase in CA being observed when compared to ROD's, which achieves a mean CA of 47.6% and, at the same time, outperforms both DTE and LSA-MMFT. Furthermore, in our study, performance evaluation has also been carried out for a more complex classification problem--in this instance, a four-class problem. DTE yields a superior performance compared to ROD and LSA-MMFT, with the highest mean CA of 56.9% being recorded when DTE is employed based on X-S1. However, a 10.5% increase in the mean CA is observed when EMC-MMFT is employed and compared with DTE, with a superior mean CA of 67.4% being recorded when EMC-MMFT is employed. Moreover, a 27.6% increase in the mean CA is observed when EMC-MMFT is employed and compared with DTE, with a superior mean CA of 59.8% being recorded when EMC-MMFT is employed based on X-S2, while DTE achieves the highest mean CA of 59.8% compared to both ROD and LSA-MMFT. An inferior mean CA is observed when ROD, DTE, and LSA-MMFT are employed based on X-S3 for a four-class problem.