## 5 Discussion
However, a significant decline in CA across individual TDs was observed when LSA-MMFT, DTE, and ROD were applied, while EMC-MMFT maintained superior classification performances across all the individual TDs, as depicted in Figure 5. Similar classification performances were observed for the two-class inter-subject classification scenario using the BCI competition IV-a dataset, whereby the highest CAs of 96% and 97% were recorded when LSA-MMFT, DTE, and ROD were applied, respectively, while EMC-MMFT achieved a superior CA of 98%. In this case, EMC-MMFT outperformed all the other three algorithms and maintained superior performances across all the individual TDs, as illustrated in Figure 8. Moreover, EMC-MMFT outperformed all three algorithms for the two-class inter-subject classification scenarios using our own recorded SSMVEP and MI subjects, with superior CAs of 95% and 92.5% being recorded when optimal combinations of high beneficial SDs were selected, as illustrated in Figures 6 and 7. Notably, the highest mean CA of 85.1% was recorded when DTE was employed, compared to ROD and LSA-MMFT; however, a superior mean CA of 94.2% was observed when EMC-MMFT was employed, based on the two-class inter-session scenario, while a superior mean CA of 93.9% was achieved for the two-class inter-subject classification scenario using the BCI competition IV-a dataset. EMC-MMFT further outperformed the other three domain selection algorithms, with superior CAs of 84.5% and 78.4% being recorded for both of the inter-subject scenarios utilizing our own recorded SSMVEP and MI subjects, respectively as depicted in Table 5.  
between domains also demonstrated to be less efficient in enhancing performance, with a mean CA of 64.1% being recorded. Therefore, selecting only a combination of high beneficial domains when EMC-MMFT was employed yielded superior performances across the individual TDs that translated to a mean CA of 84.1% for the three-class inter-session classification scenario, while a superior mean CA of 80% was noted for the three-class inter-subject classification scenario utilizing MI subjects acquired from the BCI competition IV-a dataset. Moreover, EMC-MMFT achieved the highest mean CAs of 72.8% and 64.3% for the three-class inter-subject classification scenarios using our own recorded SSMVEP and MI subjects, respectively. The complexity of the four-class inter-subject EEG classification problem including the existence of NT proved to significantly affect classification performance when DTE, ROD, and LSA-MMFT were employed. Hence, inferior mean CAs of 57%, 61.2%, and 58.27% were recorded when DTE, ROD, and LSA-MMFT were employed to address the effect of NT based on the four-class problem. Notably, selecting only the optimal combination of highly beneficial SDs yielded remarkable performances regardless of the complexity of the four-class problem, with the highest mean CA of 75.06% being recorded when EMC-MMFT was employed, based on the four-class inter-session scenario, while a superior mean CA of 67.4% was achieved for the four-class inter-subject classification scenario using the BCI competition IV-a dataset, as depicted in Tables 5 and 6, respectively. EMC-MMFT further demonstrated its superiority and outperformed the other three domain selection algorithms, with superior mean CAs of 59.8% and 62.9% being recorded for the four-class inter-subject classification scenarios using our own recorded SSMVEP and MI subjects. These results further validate that selecting only the best combination of closely related high beneficial sources can significantly minimize the effect of NT and, in turn, enhance the prediction rate across individual TDs. However, iterating through the domains and removing a single low-beneficial source domain at a time can still pose severe implications for the CA in the next iteration, since different domains respond differently to other domains.