## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources  
In this section, we evaluate and compare the classification performances of ROD, DTE, EMC-MMFT, and LSA-MMFT across individual TDs when low-beneficial sources are discarded and high beneficial sources are selected for transfer mapping under two-class, three-class, and four-class inter-session and inter-subject classification scenarios. In this case, ROD, DTE, and LSA-MMFT iterate through the domains and remove a single low-beneficial domain in each iteration until only two domains remain, respectively, mainly to minimize the effect of NT. However, whenever a session/subject is assigned the role of TD, a single low-beneficial domain is removed in each iteration, and the remaining sources are assigned the roles of the SDs utilized for TL [36]. EMC-MMFT first performs source selection, whereby less-beneficial domains are discarded, while the best combination of closely related high beneficial sources are selected as the SDs utilized when TL is performed. Selected Combination of High Beneficial Source Domains Using EMC-MMFT  
Table 1 depicts the optimal selected combination of closely related beneficial sources with a high transferability, with X-Se and X-S1 representing the SSMVEP inter-session and inter-subject, respectively. X-S2 represents the MI inter-subject (our own recorded  
Figure 16: Enhanced multi-class MMFT performance evaluation based on a four-class cross-subject scenario (BCI competition IV-a). MI dataset), and X-S3 also represents the MI inter-subject (BCI competition IV-a dataset), whereby the selected sources are utilized as SDs to enhance the prediction rate across individual TDs when TL is employed. In this case, the EMC-MMFT algorithm select only the best combination of closely related high beneficial sources as the SDs from a pool of SDs and then assign a single session/subject to be the TD while each of the nine sessions/subjects takes a turn being the TD, respectively. Notably, EMC-MMFT maintains superior classification performances across each of the nine TDs for all two-class inter-subject and inter-session classification scenarios, with the highest CA being recorded when EMC-MMFT is employed and selects Se1, Se5, and Se8 as the optimal combination of highly beneficial SDs for the SSMVEP sessions, while the highest CA is observed when S3 and S8 are optimally selected as being high beneficial SDs for a two-class inter-subject classification problem using the MI subjects acquired from the BCI competition IV-a dataset. Moreover, selecting S2, S4, S7, and S9 and S3, S4, S5, S6, and S8 as high beneficial SDs yields superior CAs for both of the two-class inter-subject classification problems, i.e., when using our own recorded SSMVEP and the MI dataset, respectively. A similar trend in classification performance is observed for a three-class problem, when EMC-MMFT is employed to select only the best combination of high beneficial sources as SDs. In this case, the highest CA is observed when Se4, Se5, Se6, Se7, and Se8 are optimally selected as the best combination of high beneficial SDs for three-class inter-session classification, while the highest CA is observed when S2 and S7 are selected as the optimal combination of SDs for inter-subject classification, utilizing the MI subjects acquired from the BCI competition IV-a dataset. In a similar manner, selecting S7 and S4 as high beneficial SDs yields superior CAs for both of the three-class inter-subject classification problems, using our own recorded SSMVEP and the MI dataset, respectively. Furthermore, the EMC-MMFT framework yields superior CAs across the individual TDs for a more complex classification problem--in this instance, a four-class problem.