## 4 Results
### Experimental Results
#### 4.2.3 Domain Selection Based on a Four-Class Problem  
A more complex multi-class inter-session classification scenario is explored to evaluate the effect less- and highly beneficial source domains on the TL prediction rate across individual TDs. A comparative performance analysis is carried out under a four-class inter-session classification problem to further investigate the impact of NT on classification performance. As such, nine of our own four-class SSMVEP sessions are utilized to facilitate this investigation, whereby EMC-MMFT is compared with the DTE, ROD, and LSA-MMFT algorithms mainly to determine the effect of source selection on TL performance. Therefore, selecting only highly beneficial SDs with a high transferability to minimize the effect of NT  
Figure 12: EMC-MMFT performance evaluation based on a three-class cross-subject scenario (BCI competition IV-a). demonstrates to significantly enhance the prediction rate across the individual TDs for a four-class problem. Hence, superior classification performances are observed across all the individual TDs when EMC-MMFT is applied compared to DTE, ROD, and LSA-MMFT. As such, a superior CA of 87% is recorded across Se4 when Se7 and Se8 are selected as the best combination of highly beneficial SDs when EMC-MMFT is applied. In this case, EMC-MMFT outperforms all three domain selection algorithms, with the highest CA of 74% being recorded across Se4 when ROD is applied, while both DTE and LSA-MMFT achieve the highest CAs of 68.5%, and 79% across Se4, respectively. Notably, a significant decline in CA is observed across Se9, with the lowest CA of 25% being recorded when LSA-MMFT is applied. However, a 49.5% increase in CA is noted across Se9, whereby a CA of 74.5% is recorded when EMC-MMFT is employed, as illustrated in Figure 13.  
Based on these results, it is worth noting that the complexity of a four-class problem can significantly contribute to a poor performance when DTE, ROD, and LSA-MMFT iterate through the domains to discard a single less-beneficial domain from a pool of domains consisting of significant individual differences. In this case, the complexity of a four-class problem is proven to massively affect the performances of all the domain selection algorithms tested. Hence, inferior classification performances are observed across all the individual TDs. However, the EMC-MMFT framework outperforms all the algorithms and yields remarkable performances compared to ROD, DTE, and LSA-MMFT when the best combinations of highly beneficial domains are selected for TL. In this experiment, a four-class inter-subject classification scenario is adopted to further investigate the impact of NT on the classification performance of TL. Therefore, a comparative performance analysis is carried out to evaluate the effect of less- and highly beneficial domains on transfer mapping, utilizing nine of our own recorded SSMVEP sessions. As such, the EMC-MMFT framework is compared with DTE, ROD, and LSA-MMFT under a four-class inter-subject classification scenario, mainly to determine the impact of NT on performance. In this case, the complexity of a four-class problem including the effect of NT is proven to pose severe implications for the prediction rate when DTE,  
Figure 13: EMC-MMFT performance evaluation based on a four-class inter-session scenario (our own sessions). ROD, and LSA-MMFT are applied. Such implications are the result of significant individual differences across domains, mainly because different sources respond differently to other domains. Therefore, iterating through subjects and discarding a single less-beneficial subject, while utilizing the remaining combination of both highly and less-beneficial subjects as SDs, can drastically deteriorate the classification performance. Hence, inferior performances are observed across individual TDs when DTE, ROD, and LSA-MMFT are employed.