## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources
Notably, employing DTE based on a four-class inter-subject problem making use of SSMVEP subjects yields the highest CA across all the TDs when a single less-beneficial SD is removed and S7 is assigned the role of TD, while the highest CA is recorded when S2 is selected as the TD and seven less-beneficial SDs are removed for the four-class inter-subject scenario using our own recorded MI subjects. However, a decrease in the classification performances is observed across the individual TDs whenever a single less-beneficial SD is removed in each iteration, as a result of individual variations across the domains, since different domains respond differently to other domains. Number of Removed Low-beneficial Source Domains Using LSA-MMFT  
Table 4 depicts the number of low-beneficial SDs discarded in each iteration whenever an individual target domain is assigned when the LSA-MMFT algorithm is employed to perform source selection based on two-class, three-class, and four-class inter-session and inter-subject classification scenarios. In this case, employing LSA-MMFT to select high beneficial domains by evaluating label similarities across the domains and discard a single low-beneficial domain in each iteration yields remarkable CAs. However, significant \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-S2** & **X-S3** & **X-S3** & **X-Se** & **X-S1** & **X-S2** & **X-S3** & **X-Se** & **X-S1** & **X-S2** & **X-S3** \\ \hline
1 & 4 & 2 & 5 & 2 & 1 & 7 & 7 & 6 & 7 & 4 & 1 & 6 \\ \hline
2 & 3 & 4 & 5 & 4 & 6 & 2 & 7 & 5 & 6 & 6 & 5 & 7 \\ \hline
3 & 6 & 3 & 3 & 1 & 5 & 4 & 2 & 4 & 3 & 2 & 1 & 5 \\ \hline
4 & 3 & 7 & 3 & 1 & 6 & 4 & 6 & 7 & 1 & 7 & 2 & 3 \\ \hline
7 & 3 & 1 & 3 & 5 & 5 & 2 & 3 & 6 & 1 & 1 & 1 & 5 \\ \hline
8 & 6 & 2 & 1 & 3 & 6 & 7 & 2 & 6 & 2 & 3 & 7 & 4 \\ \hline
9 & 7 & 5 & 1 & 1 & 7 & 7 & 5 & 3 & 6 & 7 & 7 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal selected combination of beneficial source domains using DTE. variations in the CAs across individual TDs is observed with LSA-MMFT performing poorly across several individual TDs. As such, assigning Se1 the role of TD and discarding a single low-beneficial SD yields a remarkable CA; however, significant variations in neural dynamics result in a decline in the CAs across several individual TDs when LSA-MMFT is employed, based on the two-class inter-session classification scenario using the SSMVEP sessions, while a remarkable CA is also observed when S3 is selected as the TD and six low-beneficial SDs are removed in the two-class inter-subject classification scenario using the MI subjects acquired from the BCI competition IV-a database. Moreover, employing LSA-MMFT based on a two-class inter-subject scenario making use of SSMVEP subjects yields the highest CA across all the TDs when a seven low-beneficial SD is removed and S7 is assigned the role of TD, while the highest CA is recorded when S2 is assigned the role of TD and seven low-beneficial SDs are removed in the two-class inter-subject scenario using our own recorded MI subjects. When the LSA-MMFT framework is employed, based on a three-class problem using SSMVEP sessions, the highest CA is recorded when Se6 is assigned the role of TD and a single low-beneficial SD is removed, while the highest prediction rate is recorded when S3 is assigned the role of TD and seven low-beneficial SDs are discarded in the scenario using our own MI subjects obtained from the BCI competition IV-a database. Selecting S6 as the TD and removing seven low-beneficial SDs yield the highest CA across all the TDs when LSA-MMFT is employed for the scenario using SSMVEP subjects, while the highest CA is recorded when S9 is assigned the role of TD and a single low-beneficial SD is removed in the three-class inter-subject scenario using our own recorded MI subjects.