## 3 Results
### Four-Class Problems
#### 3.3.2 Enhanced Multi-Class MMFT Four-Class Cross-Session Classification  
Results from Figure 9 illustrated that the complexity of a four-class problem and non-related sources constituted NT. As a result, the classification performance in the target domain significantly deteriorated. However, closely related sources (sessions) have been demonstrated to enhance classification performance, while a combination of both non-related and related sources has been demonstrated to yield a significant variation in classification performance across individual target domains, as depicted in Figure 9. In this section, enhanced multi-class MMFT is employed to search and select only the optimal combination of closely related sources, then utilize the best-selected sources to enhance the performance of individual target domains for a four-class cross-session classification problem. Table 3 depicts the best-selected combinations among all source domains as each session took a turn as the target domain. The selected sources vary across individual target domains since the proposed enhanced multi-class MMFT only searches for closely related sources for a specific target domain. As such, a superior classification performance of 70.5% was noted before domain selection across Se4 as a target domain, and the remaining eight sessions are SDs. However, a highest accuracy of 87% was recorded across Se4 after (Se7-Se8) were optimally selected as the best combination of closely related source domains. Hence, a 16.5% increase in accuracy after source selection can be observed across Se4 when enhanced multi-class MMFT is employed, as illustrated in Figure 10. Based on these results, it is worth noting that a combination of both non-related and related sources, including the complexity of a four-class problem, will yield significant variations in classification performances across individual TDs. Hence, classification performance before source selection is characterized by poor prediction rates. However, employing enhanced multi-class MMFT to select only the best sessions proved to effectively improve classification performance across target domains. \begin{table}
\begin{tabular}{c c} \hline
**Target Domain** & **Selected Source Domain(s)** \\ \hline
1 & 2, 3, 5, 6 \\ \hline
2 & 1, 3, 5, 7 \\ \hline
3 & 1, 2, 5, 9 \\ \hline
4 & 7, 8 \\ \hline
5 & 2, 3 \\ \hline
6 & 3, 4, 8 \\ \hline
7 & 3, 4, 6, 9 \\ \hline
8 & 4, 6 \\ \hline
9 & 3, 7 \\ \hline \end{tabular}
\end{table} 
Table 3: Selected source domain for a four-class cross-session classification. Figure 9: Results for four-class cross-session classification. 3.3.3. Multi-Class MMFT for Four-Class Cross-Subject Classification  
The impact of multi-class cross-subject classification on transfer learning performance is further investigated based on a four-class problem. A comparative performance analysis is carried out between the proposed multi-class MMFT, MEKT, and two traditional ML algorithms. However, to emulate the same transfer learning condition for a four-class cross-subject classification using ML, eight subjects are used to train, while a single subject is used to test the classifier. Despite the complexity of a four-class cross-subject classification problem, closely related subjects with similar EEG characteristics were demonstrated to effectively enhance the prediction rate in the target domain. Figure 11 depicts a comparison of classification performance between our proposed multi-class MMFT with MEKT and two ML algorithms for a four-class cross-subject classification problem. The effect of closely related sources on a four-class cross-subject classification problem can be attributed to a high classification performance across individual TDs. Hence, the best performance of 59% is noted across S3 as a TD and (S1-S2 and S4-S9) as SDs when multi-class MMFT is employed. However, the complexity of a four-class cross-subject classification problem proved to be a challenge for TL algorithms.