## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources
Notably, the highest CA is recorded when Se7 and Se8 are selected as high beneficial SDs for four-class inter-session classification, while the highest CA is recorded when S3 and S7 are selected as the optimal combination of beneficial SDs for four-class inter-subject classification using the MI subjects acquired from the BCI competition IV-a dataset. Notably, superior CAs for both four-class inter-subject classification problems are recorded when S2, S4, S7, and S9 and S3, S4, S5, S6, and S8 are selected as high beneficial SDs, using our own recorded SSMVEP and the MI dataset, respectively. Hence, discarding low-beneficial sources and selecting only a combination of high-beneficial sources is proven to significantly enhance the prediction rate and yields superior classification performances when the EMC-MMFT algorithm is employed. Table 2 depicts the number of discarded low-beneficial source domains across each individual target domain when ROD is employed for two-class, three-class, and four-class inter-session and inter-subject classification scenarios. In this case, the highest CA is recorded when the ROD algorithm assigns Se1 to be the TD and three less-beneficial SDs are removed, while the highest CA is observed when S3 is assigned the role of TD and two low-beneficial SDs are removed for the two-class inter-session scenario using the SSMVEP sessions and for the inter-subject classification problem using the MI subjects acquired from the BCI competition IV-a dataset, respectively. Moreover, for the two-class inter-subject classification problem using our own recorded SSMVEP subjects, the highest CA is observed when the ROD algorithm assigns S7 to be the TD and a single low-beneficial SD is removed, while the highest CA is observed when S2 is assigned the role of TD and two low-beneficial SDs are removed for two-class inter-subject classification, using our own recorded MI subjects. Furthermore, when the ROD algorithm is employed for a three-class problem, the highest CA is recorded when Se6 is selected to be the TD and two low-beneficial SDs are removed, while the highest CA is observed when S3 is assigned the role of TD and five low-beneficial SDs are removed for both of the inter-session classification problem using the SSMVEP sessions and the inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a database, respectively. In a similar manner, removing six low-beneficial SDs and assigning S6 to be the TD yields the highest CA across all the TDs when ROD is employed, based on a three-class inter-subject classification problem utilizing SSMVEP subjects, while the highest CA is recorded when S9 is selected to be the TD and six less-beneficial SDs are removed for the three-class inter-subject classification problem using our own recorded MI subjects. Employing ROD for source selection based on a more complex classification problem results in a significant decline in CA across the individual TDs. Hence, the highest CA is observed when Se4 is selected to be the TD and two low-beneficial SDs are removed for the inter-session classification scenario utilizing the SSMVEP sessions; meanwhile, removing five low-beneficial SDs and assigning S6 to be the TD yields a superior performance compared to the other individual TDs for the inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a dataset. Furthermore, assigning S7 the role of TD and discarding six low-beneficial SDs yields the highest CA across all the TDs when ROD is applied on SSMVEP subjects for a four-class inter-subject classification scenario, while the highest CA is recorded when S9 is selected to be the TD and six low-beneficial SDs are removed for a four-class inter-subject scenario using our own recorded MI subjects.