## 6 Conclusions  
In this paper, the effect of NT on the classification performance of transfer learning across individual target domains was investigated, whereby four domain selection algorithms were introduced to evaluate the effect of low- and high beneficial source domains on TL performance under three inter-session and inter-subject classification scenarios (two-class, three-class, and four-class problems). In this case, the EMC-MMFT framework was compared with three domain selection algorithms (LSA-MMFT, ROD, and DTE), mainly to determine the impact of NT on classification performance across individual TDs. Hence, the performance of each domain selection algorithm was evaluated based on the selection of high beneficial domains and the rejection of low-beneficial domains to determine the effect of NT on the performance. Therefore, discarding a single low-beneficial domain in each iteration from a pool of domains consisting of significant individual differences demonstrated to pose severe implications for classification performance. Hence, inferior classification performances were recorded across individual TDs when ROD, DTE, and LSA-MMFT were employed under the two-class and three-class problems. Moreover, the complexity of the four-class problem including the presence of low-beneficial domains in each iteration when ROD, DTE, and LSA-MMFT were employed to discard only a single low-beneficial domain per iteration demonstrated to significantly deteriorate the prediction rate across individual TDs. However, discarding all the low-beneficial SDs at once and selecting only the best combination of high beneficial SDs demonstrated to minimize the effect of NT and, at the same time, significantly enhance the prediction rate. Hence, superior classification performances were recorded across all nine individual TDs when EMC-MMFT was employed under the two-class and three-class problems. Moreover, the complexity of the four-class problem was demonstrated to cause a minimal impact on the classification performance when EMC-MMFT was employed to select only the optimal combination of high beneficial SDs, with superior performances being recorded across each individual TD compared with the results obtained with the ROD, DTE,and LSA-MMFT algorithms. This proves the study's hypothesis and indicates that selecting only high beneficial domains can significantly minimize the effect of NT and, in turn, enhance the performance of TL algorithms.  
Conceptualization, R.C.M.; methodology, R.C.M.; software, R.C.M. and S.D.; formal analysis, R.C.M.; investigation, R.C.M.; resources, R.C.M. and S.D.; data curation, R.C.M.; writing--original draft preparation, R.C.M.; writing--review and editing, C.T. and S.D.; visualization, R.C.M.; supervision, C.T., P.A.O. and S.D.; project administration, C.T. and P.A.O.; funding acquisition, C.T. and P.A.O. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Research Foundation of South Africa (grant numbers 145975 and 138429).  
Not applicable.  
The datasets used/or analyzed during the current study are publicly available online and from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.