## 3 Results
### Three-Class Problems
#### 3.2.3 Multi-Class MMFT for Three-Class Cross-Subject Classification  
The impact of a three-class cross-subject classification problem on TL performance is further investigated using nine EEG subjects, mainly to further validate the classification performance of multi-class MMFT on a three-class cross-subject classification problem [39, 40]. In a similar manner as the previous experiment, when multi-class MMFT is employed, eight subjects are considered as source domains, while a single subject is a target domain, and all nine subjects take a turn as a target domain.  
\begin{table}
\begin{tabular}{c c} \hline
**Target Domain** & **Selected Source Domain(s)** \\ \hline
1 & 4, 5, 6, 7, 8 \\ \hline
2 & 3, 4, 6, 7, 8 \\ \hline
3 & 5 \\ \hline
4 & 5, 7, 8, 9 \\ \hline
5 & 3, 6 \\ \hline
6 & 1, 2, 5, 7, 9 \\ \hline
7 & 2, 4, 6, 8 \\ \hline
8 & 1, 2, 5 \\ \hline
9 & 1, 4 \\ \hline \end{tabular}
\end{table}
Table 1: Selected source domain for a three-class cross-session classification.  
Figure 6: Multi-Class MMFT performance evaluation before and after domain selection based on a three-class cross-session classification.  
From Figure 7, one finds that TL algorithms can significantly enhance the classification performance of individual TDs for a three-class cross-subject classification as compared to ML algorithms. Hence, emulating the same TL conditions for ML algorithms by utilizing samples from eight different subjects to train and samples from a single different subject to test the classifiers proved to have a negative effect on classification performance. Consequently, LDA achieved a highest CA of 48% when S5 is used to test and (S1-S4 and S6-S9) are used to train the classifier, while RegTree achieved a highest accuracy of 42% when S6 was used to test and (S1-S5 and S7-S9) were used to train the classifier [17; 34; 41]. However, multi-class MMFT demonstrated to effectively improve performance, with a highest CA of 75.3% recorded across S3 as a target domain and (S1-S2 and S4-S9) are source domains. An inferior CA was also recorded when MEKT was employed and compared with multi-class MMFT, with a highest CA of 70% observed across S4 as a TD and (S1-S3 and S5-S9) are SDs.  
Related source domains proved to be beneficial for multi-class MMFT. However, non-related sources constituted NT, which resulted in poor classification performance across individual TDs. The effect of NT can be attributed to a significant decline in accuracy which is observed across S8 as a TD and (S1-S7 and S9) are SDs, whereby S8 achieved an accuracy of 34% when multi-class MMFT is employed. Samples from non-related subjects were also demonstrated to be an obstacle for both ML classifiers. Hence, a significant decrease in accuracy is noted for ML classifiers, with LDA achieving a lowest accuracy of 30% when S8 was used to test and (S1-S7 and S9) were used to train the classifier, while RegTree achieved a lowest accuracy of 32% when S1 is used to test and (S2-S9) are used to train the classifier.  
From these results, one finds that subject-to-subject variations can significantly affect the performance of TL for a three-class cross-subject classification. However, related domains illustrated that multi-class MMFT could significantly enhance classification performance, while non-related domains deteriorate the classification performance of individual target domains, as depicted in Figure 7.