## 5 Discussion  
The classification performance when TL is employed is dependent on the transferability of features in the source domains, utilized to enhance the prediction rate in the target \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Two-Class**} & \multicolumn{3}{c}{**Three-Class**} & \multicolumn{3}{c}{**Four-Class**} \\ \cline{2-9}  & **X-S1** & **X-S2** & **X-S3** & **X-S1** & **X-S2** & **X-S3** & **X-S1** & **X-S2** & **X-S3** \\ \hline LSA-MMFT & 83.3 (6.09) & 60.4 (15.5) & 65.1 (17.5) & 67.1 (5.9) & 40.9 (13.2) & 41.8 (13.8) & 56.6 (2.7) & 30.7 (7.9) & 35.2 (11.1) \\ \hline DTE & 83.7 (14.8) & 64 (14.2) & 60.3 (18.3) & 67.1 (11.2) & 42.3 (10.6) & 44.9 (12.1) & 56.9 (9.4) & 32.2 (2.7) & 36.5 (11.1) \\ \hline ROD & 85.1 (14.9) & 58.2 (8.5) & 67.6 (10.5) & 69.4 (13.2) & 42.1 (7.5) & 47.6 (6.9) & 56.8 (11.6) & 30.2 (5.2) & 27.8 (10) \\ \hline EMC-MMFT & **93.9 (6.09)** & **84.5 (8.7)** & **78.4 (12.4)** & **80 (5.9)** & **72.8 (10.7)** & **64.3 (7.6)** & **67.4 (2.7)** & **59.8 (12.6)** & **62.9 (10)** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean (%) and standard deviation (in parenthesis) for domain selection algorithms based on cross-subject classification. domain [37]. However, negative transfer has demonstrated to pose severe implications for the classification performance across individual target domains when TL is employed [12]. As such, sessional and inter-subject variations have proven to significantly contribute to the effect of NT, emerging as a result of the transfer of features from low-beneficial sources in the target domain [38]. However, a combination of both low-beneficial and high beneficial domains can positively and negatively affect classification performance, mainly as a result of different sources responding differently to other individual sources. In this case, low-beneficial sources can drastically deteriorate the prediction rate, while related high beneficial sources improve the prediction rate across individual target domains [39]. Hence, domain selection algorithms have been implemented to identify high beneficial sources with a high transferability, mainly to minimize the effect of NT and enhance the prediction rate across individual TDs when TL is employed after source selection. In this paper, we investigated the impact of low-beneficial and high beneficial domains on TL performance, at the same time addressing the challenge of NT, significantly contributing to poor classification performances across individual TDs. To facilitate the investigation, four novel domain selection algorithms were introduced and compared to evaluate the performance of each algorithm when beneficial sources ware selected for transfer mapping. In this case, all the domain selection techniques (EMC-MMFT, ROD, DTE, and LSA-MMFT) adapted a two-class inter-session and inter-subject scenario to a multi-class inter-subject and inter-session scenario. The performances of EMC-MMFT, DTE, ROD, and LSA-MMFT were evaluated and compared under three inter-session and inter-subject EEG classification problems (namely, two-class, three-class, and four-class problems), mainly to validate the performance of each algorithm across individual TDs [11; 12]. Consequently, when the EMC-MMFT framework was employed, only a combination of high beneficial domains was selected for transfer mapping, while low-beneficial domains were discarded to minimize the effect of NT, which can drastically deteriorate the prediction rate across individual TDs. A comparative performance evaluation illustrated that EMC-MMFT could significantly improve the classification performance across individual TDs compared to DTE, ROD, and LSA-MMFT. Consequently, all four algorithms achieved the highest CA of 100% for the two-class inter-session classification scenario.