## 4 Results
### Experimental Results  
#### 4.2.1 Domain Selection Based on a Two-Class Problem  
In this section, the impact of NT on transfer learning performance across individual TDs is investigated using nine of our recorded SSMVEP sessions. As such, to facilitate the investigation, four state-of-the-art domain selection algorithms are utilized to evaluate the effect of less-beneficial and highly beneficial domains on transfer mapping performance, mainly to determine the impact of NT on classification performance, depicted as low CAs across individual TDs [33]. Therefore, the EMC-MMFT framework is introduced and compared with three domain selection algorithms (DTE, ROD, and LSA-MMFT) under two-class inter-session classification scenarios. In this case, when EMC-MMFT is employed, a single domain (subject/session) is selected to be the TD, while the best combinations of highly beneficial sources are selected from eight domains and assigned the roles of SDs. In a similar manner, when DTE, ROD, and LSA-MMFT are employed, a single domain is selected to be the TD, while the remaining domains, after the discarding of a single less-beneficial domain in each iteration, are assigned the roles of SDs when transfer mapping is employed after source selection. Figure 5 presents the classification performance of the EMC-MMFT framework when compared with the three domain selection algorithms (DTE, ROD, and LSA-MMFT) under a two-class inter-session classification scenario. Performance evaluation in this instance is carried out to determine the effect of both less- and highly beneficial domains (which significantly contribute to NT) on the transfer learning prediction rate across individual target domains. From the evaluation, one finds that iterating through the domains and removing a single less-beneficial domain in each iteration proves to pose severe implications for performance across individual TDs, denoted by a low prediction rate across the TDs [34]. Consequently, all four domain selection algorithms yield remarkable classification performances for a less complex classification problem--in this case, a two-class problem. As such, the highest CA of 100% is observed when Se1 is selected to be the TD and a single less-beneficial SD is removed when LSA-MMFT is employed, while the highest CA of 100% is recorded when Se1 is assigned the role of TD and four less-beneficial SDs are removed when DTE is employed. Moreover, the highest CA of 100% is also observed when Se1 is selected to be the TD and three less-beneficial SDs are removed when ROD is employed, as depicted in Figure 5. Notably, a significant decline in CAs is recorded across Se4 and Se9 when DTE, ROD, and LSA-MMFT are employed, with a significant decline of 16% in the CA recorded across Se3 when LSA-MMFT is employed. However, the EMC-MMFT framework maintains superior CAs across all individual TDs, with the highest CA of 100% recorded across Se1, Se3, Se4, Se5, Se6, and Se7. Notably, an inferior CA of 66% is recorded across Se9, and Se1, Se3, and Se9 are assigned the roles of SDs when EMC-MMFT is employed, as depicted in Table 1. In this case, when EMC-MMFT is compared with ROD, DTE, and LSA-MMFT across Se9, EMC-MFFT still maintains a superior CA. Figure 5: EMC-MMFT performance evaluation based on a two-class inter-session scenario (our own SSMVEP sessions). In this section, the EMC-MMFT framework is compared with the DTE, ROD, and LSA-MMFT algorithms under a two-class inter-subject classification scenario. As such, nine of our own recorded SSMVEP subjects are further utilized to investigate the impact of less-beneficial and highly beneficial domains on transfer mapping performance. Therefore, the highest CA across all the individual TDs is observed across S6 when LSA-MMFT is employed, with an accuracy of 86.25% recorded, while the highest CAs of 68.75% and 85% are recorded across S6 when DTE and ROD are employed, respectively. However, EMC-MMFT maintains superior classification performances across all the individual TDs when compared with ROD, DTE, and LSA-MMFT.