## 4 Results
### Experimental Results
#### 4.2.3 Domain Selection Based on a Four-Class Problem
Therefore, the highest CA of 38.7% is recorded across S7 when LSA-MMFT is applied, while CAs of 35.6% and 36.25% are recorded across S7 when ROD and DTE are applied, respectively. However, a superior CA of 68.75% is noted across S7 when EMC-MMFT is employed, while a 30.1% increase in CA is observed across S7 when EMC-MMFT is compared with LSA-MMFT. From the comparison results, it can be noted that, despite the complexity of a four-class problem, employing EMC-MMFT to select the best combination of highly beneficial SDs can significantly enhance the CA and, at the same time, minimize the effect of NT. Hence, superior classification performances are observed across all the individual TDs when EMC-MMFT is employed compared to the three domain selection algorithms tested, with a superior CA of 76.2% being recorded across S3 when S2 and S4 are selected as the best combination of highly beneficial SDs when EMC-MMFT is applied, as illustrated in Figure 14.  
In this section, nine of our own recorded four-class MI subjects are further utilized to carry out a comparative performance analysis, mainly to evaluate the effect of less-beneficial and highly beneficial domains on transfer mapping performance. In this case, to determine the impact of NT on transfer mapping, EMC-MMFT is compared with the DTE, ROD, and LSA-MMFT algorithms under a four-class inter-subject classification scenario. Consequently, the highest CA of 52.6% is observed across S1 when LSA-MMFT is employed and two less-beneficial SDs are removed, compared to both DTE and ROD, with the highest CAs of 51.87% and 52.5% being recorded across S2 and S1, respectively. Therefore, employing EMC-MMFT is demonstrated to yield superior classification performances across all the individual TDs, with a 25% increase in CA being observed when compared  
Figure 14: EMC-MMFT performance evaluation based on a four-class inter-subject scenario (our own SSMVEP subjects). with all the three domain selection algorithms tested, whereby a superior CA of 77.6% is recorded across S4 when S3 is selected as a highly beneficial SD using EMC-MMFT, as illustrated in Figure 15. Notably, a significant increase of 50.1% in CA is observed across S4 when EMC-MMFT is applied, compared to ROD, which instead achieves a CA of 27.5% across S4. Furthermore, discarding a single less-beneficial domain in each iteration whilst including the complexity of a four-class problem is proven to significantly affect the prediction rate, with the lowest CA of 17.5% being recorded across S2 when ROD is applied; meanwhile, selecting the combination of highly beneficial domains is proven to minimize the effect of NT and the complexity of a four-class problem. Hence, a 37.1% increase in CA is observed across S2, while a CA of 54.6% is recorded when EMC-MMFT is applied. A four-class inter-subject classification scenario is adopted when a comparative performance analysis is carried out to further investigate the impact of NT on the classification performance of TL. In this case, to examine the impact of NT on the performance of individual TDs, four domain selection algorithms are employed to evaluate the effect of less- and highly beneficial domains on TL. As such, EMC-MMFT is compared with DTE, ROD, and LSA-MMFT, whereby a BCI competition IV-a dataset consisting of nine MI subjects is utilized to facilitate this investigation. The complexity of a four-class problem including the presence of less-beneficial SDs in each iteration is demonstrated to significantly contribute to NT, denoted by poor classification performances across the individual TDs when the DTE, ROD, and LSA-MMFT algorithms are applied [35]. Notably, the lowest CA of 29% is observed across S9 when ROD is employed, while CAs of 33.5% and 31.5% are recorded across S9 when DTE and LSA-MMFT are employed, respectively. However, employing EMC-MMFT to select highly beneficial SDs for transfer mapping is demonstrated to minimize the effect of NT and, in turn, significantly enhance the CA.