## 4 Results
### Experimental Results
#### 4.2.2 Domain Selection Based on a Three-Class Problem
Notably, a 50% increase in CA is observed across S8 when EMC-MMFT is applied and S5 and S9 are selected as highly beneficial SDs; in this instance, a CA of 74.16% is recorded. In this section, a three-class inter-subject classification scenario is adopted to further evaluate the impact of less-beneficial and highly beneficial domains on transfer mapping, mainly to determine the impact of NT on performance. As such, nine of our own recorded MI subjects are considered for experimentation, whereby the EMC-MMFT framework is compared with the DTE, ROD, and LSA-MMFT algorithms. In this case, ROD outperforms both DTE and LSA-MMFT, with the highest CA of 63.3% being recorded across S9, while CAs of 58.3% and 61.66% are recorded across S9 when both DTE and LSA-MMFT are  
Figure 10: EMC-MMFT performance evaluation based on a three-class inter-subject scenario (our own SSMVEP subjects). employed, respectively. However, a 4.3% increase is observed across S9, with the highest CA of 67.6% being recorded when EMC-MMFT is applied. Notably, EMC-MMFT further demonstrates its superiority when optimal combinations of highly beneficial domains are selected, whereby superior classification performances are recorded across all the individual TDs. Hence, the highest CA of 74.1% is recorded across S5 when S1, S4, S8, and S9 are selected as the best combination of highly beneficial SDs, as illustrated in Figure 11. In this instance, an inferior CA of 28.3% is recorded across S5 when both DTE and LSA-MMFT are employed, respectively. However, a 45.8% increase in CA is observed across S5 when EMC-MMFT is applied. From the comparison results, it can be noted that selecting only the best combination of highly beneficial SDs can effectively minimize the existing challenge of NT and, in turn, increase the CA across the TD, while removing a single less-beneficial SD in each iteration can massively deteriorate the CA. Hence, a 33.75% increase in CA can be observed across S8 when EMC-MMFT is compared with ROD, whereby a CA of 80% is recorded when EMC-MMFT is applied, while a CA of 46.25% is recorded when ROD is applied. In a similar manner, it can also be noted that removing only a single less-beneficial domain in each iteration can significantly decrease the CA, whereby LSA-MMFT records the lowest CA of 26.66% across S3 when seven less-beneficial SDs are removed. However, a 29.2% increase in CA is noted across S3 when EMC-MMFT is employed, with the highest CA of 55.8% being recorded at this time. In this experiment, the impact of NT on the transfer learning performance across TDs is further investigated, whereby four domain selection algorithms are employed to facilitate this investigation by evaluating the effect of less- and highly beneficial source domains on the transfer learning performance. As such, EMC-MMFT is compared with three domain selection algorithms (ROD, DTE, and LSA-MMFT), using a BCI competition IV-a dataset consisting of nine MI subjects. In this case, ROD outperforms both DTE and LSA-MMFT across S3, with a high CA of 80.66% being recorded when ROD is employed, while CAs of 76.66% and 74.66% are noted when both LSA-MMFT and DTE are employed, respectively. EMC-MMFT, on the other hand, outperforms ROD, with the highest CA of  
Figure 11: EMC-MMFT performance evaluation based on a three-class inter-subject scenario (our own MI subjects). 84.66% being recorded across S3 when EMC-MMFT is employed. Moreover, discarding a single less-beneficial domain is proven to contribute to NT, with an inferior CA of 36% being recorded across S8 when ROD is employed. However, a 54% increase in CA is observed across S8, whereby a superior CA of 90% is recorded across S8 when EMC-MMFT is employed, as illustrated in Figure 12.  
From these results, it is worth noting that the number of source domains utilized for transfer mapping has no impact on the classification performance.