## 4 Results
### Experimental Results
#### 4.2.2 Domain Selection Based on a Three-Class Problem  
In this section, a three-class inter-session classification scenario is adopted to further investigate the impact of NT on the classification performance of TL. Therefore, to determine the impact of NT on performance, nine of our own recorded SSMVEP sessions are utilized to evaluate the effect of less- and highly beneficial domains on classification performance across individual TDs. Moreover, to facilitate this investigation, EMC-MMFT is compared with DTE, ROD, and LSA-MMFT under the same TL conditions. In this case, discarding a single less-beneficial SD in each iteration from a pool of domains consisting of significant individual differences is demonstrated to severely affect the CA. Hence, an inferior CA of 23.3% is observed across Se8 when ROD is employed. However, a 31.4% increase in CA is observed across Se8 when EMC-MMFT is employed, with the highest CA of 54.7% being recorded. Furthermore, inferior CAs of 36.66% and 36.67% are observed across Se9 and Se7 when both DTE and LSA-MMFT are applied, respectively. Notably, a 53.34% increase is observed across Se8, while a 55.33% increase in CA is observed across Se7 when EMC-MMFT is employed, with Cas of 90% and 92% being recorded across Se8 and Se7, respectively. From this comparison, it can be noted that selecting only a combination of highly beneficial SDs can minimize the effect of NT. Hence, the EMC-MMFT framework maintains superior classification performances across each individual TD compared to ROD, DTE, and LSA-MMFT. Moreover, EMC-MMFT further demonstrates its superiority when Se1 is assigned the role of TD and Se4, Se5, Se6, Se7, and Se8 are optimally selected as highly beneficial SDs, with the highest CA of 98% being recorded, as depicted in Figure 9.  
Based on these results, it is worth noting that the complexity of a three-class problem, including the effect of NT, can significantly deteriorate classification performance across individual TDs when DTE, ROD, and LSA-MMFT iterate through domains, removing a single less-beneficial domain at a time. However, the complexity of a three-class problem  
Figure 9: EMC-MMFT performance evaluation based on a three-class cross-session scenario (our own SSMVEP sessions). and the effect of NT demonstrate to pose a minimal impact on classification performance when EMC-MMFT is employed. Hence, first selecting only relevant or highly beneficial sessions can significantly enhance the prediction rate when transfer mapping is applied. In this section, the impacts of less- and highly beneficial domains on transfer mapping are investigated to determine the impact of NT on classification performance across each TD. As such, a three-class inter-subject classification scenario is adopted to evaluate and compare the four domain selection algorithms using nine of our own SSMVEP subjects. Therefore, the EMC-MMFT framework is compared with the DTE, ROD, and LSA-MMFT algorithm mainly to evaluate the effect of less- and highly beneficial domains on TL performance. Consequently, discarding a single less-beneficial SSMVEP subject in each iteration is demonstrated to have a minimal effect on reducing the impact of NT. Hence, the highest CA of 60.8% is recorded across S6 when LSA-MMFT is employed, while ROD and DTE achieve the highest CAs of 57.5% and 56.6% across S6, respectively. However, discarding all the less-beneficial domains and selecting only the highly beneficial domains when EMC-MMFT is applied are demonstrated to significantly minimize the effect of NT. Hence, a 25% increase in CA is observed across S6 when S7 is optimally selected as a highly beneficial SD, with a superior CA of 85.8% being recorded when EMC-MMFT is applied, as depicted in Figure 10. Moreover, a significant decline in CA is observed across S8 when LSA-MMFT is compared with ROD and DTE, with the lowest CA of 24.16% being recorded when LSA-MMFT is employed.