## 4 Discussion  
Transfer learning is highly effective in terms of increasing classification performance across individual target domains utilizing features acquired from different source domains, and it has been demonstrated to yield superior CA for a two-class cross-subject classification problem [44], while for a multi-class problem, it has been demonstrated to be more effective for within-subject classification [9; 45]. However, sessional diversity and subject-to-subject variations demonstrated that non-related domains pose a significant challenge across target domains as a result of NT, which in turn deteriorates the prediction rate across individual target domains [46; 47]. In this paper, we proposed a multi-class MMFT approach to investigate the effect of multi-class cross-session and cross-subject classification on TL performance. The proposed approach was evaluated based on a three-class and a four-class problem and compared with MEKT and two classical ML algorithms under similar transfer learning conditions. A comparative performance analysis demonstrated that the proposed multi-class MMFT could significantly enhance the classification performance of both three-class and four-class cross-session and cross-subject classification problems. Consequently, a superior classification performance of 88.7% was recorded across all individual TDs for three-class cross-session, while a highest prediction rate of 75.3% was recorded for three-class cross-subject classification problems when multi-class MMFT is employed. Moreover, compared to multi-class MMFT, the effect of NT and overfitting proved to pose severe implications on CA when both MEKT and ML algorithms are employed, respectively, as shown in Figure 5. Furthermore, a mean CA of 54% was noted when multi-class MMFT is employed, while a mean CA of 51.6% was observed when MEKT is employed for three-class cross-session classification. The effect of overfitting as a result of significant EEG variations was demonstrated to have an impact on low prediction rates when ML algorithms were employed. Hence, mean CAs of 42.1% and 32.7% were recorded for three-class cross-session classification when LDA and RegTree were employed, respectively, as shown in Table 5.  
Moreover, utilizing samples from different sources to train and test ML algorithms further demonstrated to constitute overfitting, which resulted in lower accuracies obtained by the LDA and RegTree, with mean CAs of 40.9% and 36% recorded for three-class cross-subject classification, respectively. However, the proposed multi-class MMFT showed traits of improvements with the best mean classification performance of 64.8% achieved, while MEKT achieved a mean CA of 61.4% for three-class cross-subject classification, the highest accuracy as depicted in Table 5.  
A similar decline in accuracy was observed for both four-class cross-session and four-class cross-subject classification problem when ML algorithms were employed [48]. The multi-class MMFT was demonstrated to be effective in minimizing the impact of inter-subject and sessional variations for both four-class cross-session and cross-subject classification problem, as depicted in Figures 9 and 11. Although multi-class MMFT obtained satisfactory classification performance for both three-class and four-class cross-session and cross-subject classification problems, the impact \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Computation Time**} \\ \hline
**Methods** & **Three-Class** & **Three-Class** & **Four-Class** & **Four-Class** \\ \hline  & **(Sessions)** & **(Subjects)** & **(Sessions)** & **(Subjects)** \\ \hline LDA & 231.71 s & 229.7 s & 266.96 s & 233.19 s \\ \hline RegTree & 4439.61 s & 4681.23 s & 5371.8 s & 5194.59 s \\ \hline MEKT & 7.564 s & 8.59 s & 10.36 s & 13.95 s \\ \hline Multi-class MMFT & 6.63 s & 8.47 s & 10.17 s & 12.58 s \\ \hline Enhanced Multi-class MMFT & 3314.09 s & 3972.49 s & 6648.37 s & 6105.18 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computation Complexity.