In this section, we evaluate and compare the classification performances of ROD, DTE, EMC-MMFT, and LSA-MMFT across individual TDs when low-beneficial sources are discarded and high beneficial sources are selected for transfer mapping under two-class, three-class, and four-class inter-session and inter-subject classification scenarios. In this case, ROD, DTE, and LSA-MMFT iterate through the domains and remove a single low-beneficial domain in each iteration until only two domains remain, respectively, mainly to minimize the effect of NT. However, whenever a session/subject is assigned the role of TD, a single low-beneficial domain is removed in each iteration, and the remaining sources are assigned the roles of the SDs utilized for TL [
36]. EMC-MMFT first performs source selection, whereby less-beneficial domains are discarded, while the best combination of closely related high beneficial sources are selected as the SDs utilized when TL is performed.
Selected Combination of High Beneficial Source Domains Using EMC-MMFT
Table 1 depicts the optimal selected combination of closely related beneficial sources with a high transferability, with X-Se and X-S1 representing the SSMVEP inter-session and inter-subject, respectively. X-S2 represents the MI inter-subject (our own recorded MI dataset), and X-S3 also represents the MI inter-subject (BCI competition IV-a dataset), whereby the selected sources are utilized as SDs to enhance the prediction rate across individual TDs when TL is employed. In this case, the EMC-MMFT algorithm select only the best combination of closely related high beneficial sources as the SDs from a pool of SDs and then assign a single session/subject to be the TD while each of the nine sessions/subjects takes a turn being the TD, respectively. Notably, EMC-MMFT maintains superior classification performances across each of the nine TDs for all two-class inter-subject and inter-session classification scenarios, with the highest CA being recorded when EMC-MMFT is employed and selects Se1, Se5, and Se8 as the optimal combination of highly beneficial SDs for the SSMVEP sessions, while the highest CA is observed when S3 and S8 are optimally selected as being high beneficial SDs for a two-class inter-subject classification problem using the MI subjects acquired from the BCI competition IV-a dataset. Moreover, selecting S2, S4, S7, and S9 and S3, S4, S5, S6, and S8 as high beneficial SDs yields superior CAs for both of the two-class inter-subject classification problems, i.e., when using our own recorded SSMVEP and the MI dataset, respectively.
A similar trend in classification performance is observed for a three-class problem, when EMC-MMFT is employed to select only the best combination of high beneficial sources as SDs. In this case, the highest CA is observed when Se4, Se5, Se6, Se7, and Se8 are optimally selected as the best combination of high beneficial SDs for three-class inter-session classification, while the highest CA is observed when S2 and S7 are selected as the optimal combination of SDs for inter-subject classification, utilizing the MI subjects acquired from the BCI competition IV-a dataset. In a similar manner, selecting S7 and S4 as high beneficial SDs yields superior CAs for both of the three-class inter-subject classification problems, using our own recorded SSMVEP and the MI dataset, respectively.
Furthermore, the EMC-MMFT framework yields superior CAs across the individual TDs for a more complex classification problem—in this instance, a four-class problem. Notably, the highest CA is recorded when Se7 and Se8 are selected as high beneficial SDs for four-class inter-session classification, while the highest CA is recorded when S3 and S7 are selected as the optimal combination of beneficial SDs for four-class inter-subject classification using the MI subjects acquired from the BCI competition IV-a dataset. Notably, superior CAs for both four-class inter-subject classification problems are recorded when S2, S4, S7, and S9 and S3, S4, S5, S6, and S8 are selected as high beneficial SDs, using our own recorded SSMVEP and the MI dataset, respectively. Hence, discarding low-beneficial sources and selecting only a combination of high-beneficial sources is proven to significantly enhance the prediction rate and yields superior classification performances when the EMC-MMFT algorithm is employed.
Number of Removed Low-Beneficial Source Domains Using ROD
Table 2 depicts the number of discarded low-beneficial source domains across each individual target domain when ROD is employed for two-class, three-class, and four-class inter-session and inter-subject classification scenarios. In this case, the highest CA is recorded when the ROD algorithm assigns Se1 to be the TD and three less-beneficial SDs are removed, while the highest CA is observed when S3 is assigned the role of TD and two low-beneficial SDs are removed for the two-class inter-session scenario using the SSMVEP sessions and for the inter-subject classification problem using the MI subjects acquired from the BCI competition IV-a dataset, respectively. Moreover, for the two-class inter-subject classification problem using our own recorded SSMVEP subjects, the highest CA is observed when the ROD algorithm assigns S7 to be the TD and a single low-beneficial SD is removed, while the highest CA is observed when S2 is assigned the role of TD and two low-beneficial SDs are removed for two-class inter-subject classification, using our own recorded MI subjects.
Furthermore, when the ROD algorithm is employed for a three-class problem, the highest CA is recorded when Se6 is selected to be the TD and two low-beneficial SDs are removed, while the highest CA is observed when S3 is assigned the role of TD and five low-beneficial SDs are removed for both of the inter-session classification problem using the SSMVEP sessions and the inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a database, respectively. In a similar manner, removing six low-beneficial SDs and assigning S6 to be the TD yields the highest CA across all the TDs when ROD is employed, based on a three-class inter-subject classification problem utilizing SSMVEP subjects, while the highest CA is recorded when S9 is selected to be the TD and six less-beneficial SDs are removed for the three-class inter-subject classification problem using our own recorded MI subjects.
Employing ROD for source selection based on a more complex classification problem results in a significant decline in CA across the individual TDs. Hence, the highest CA is observed when Se4 is selected to be the TD and two low-beneficial SDs are removed for the inter-session classification scenario utilizing the SSMVEP sessions; meanwhile, removing five low-beneficial SDs and assigning S6 to be the TD yields a superior performance compared to the other individual TDs for the inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a dataset. Furthermore, assigning S7 the role of TD and discarding six low-beneficial SDs yields the highest CA across all the TDs when ROD is applied on SSMVEP subjects for a four-class inter-subject classification scenario, while the highest CA is recorded when S9 is selected to be the TD and six low-beneficial SDs are removed for a four-class inter-subject scenario using our own recorded MI subjects. Significant variations in the classification performances across individual TDs further validate that the number of domains causes a minimal impact on the classification performance; however, discarding a single low-beneficial source domain in each iteration demonstrates to significantly affect the prediction rate, resulting in massive variations in the CAs across individual TDs.
Number of Removed Low-Beneficial Source Domains Using DTE
Table 3 shows the number of removed low-beneficial SDs across each individual target domain when DTE is employed to perform source selection based on two-class, three-class, and four-class inter-session and inter-subject classification scenarios. As such, removing four low-beneficial SDs and assigning Se1 the role of TD yields a remarkable CA; however, a decline in CA is observed across individual TDs when DTE is employed, based on the two-class inter-session classification scenario, while a remarkable CA is observed when S3 is assigned the role of TD and three low-beneficial SDs are removed for the two-class inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a database. Moreover, removing three low-beneficial SDs and selecting S6 as the TD yields the highest CA across all the TDs when DTE is employed, based on a two-class inter-subject scenario utilizing SSMVEP subjects, while the highest CA is recorded when S2 is assigned the role of TD and four low-beneficial SDs are removed for the two-class inter-subject scenario using our own recorded MI subjects.
Furthermore, when the DTE algorithm is employed for a three-class problem, the highest CA is recorded when Se1 is assigned the role of TD and a single less-beneficial SD is removed, while the highest CA is observed when S6 is assigned the role of TD and seven low-beneficial SDs are removed for both the inter-session scenario using the SSMVEP sessions and the inter-subject classification scenario using our own recorded MI subjects, respectively. In a similar manner, for a three-class inter-subject scenario using our own recorded SSMVEP subjects, the highest CA is observed when the DTE algorithm assigns S6 to be the TD and five low-beneficial SDs are removed, while the highest CA is observed when S9 is assigned the role of TD and three low-beneficial SDs are removed for the three-class inter-subject scenario using our own recorded MI subjects.
Furthermore, a significant decline in CA across the individual TDs is observed for a more complex four-class classification problem when DTE is employed. In this case, assigning Se2 the role of TD and removing six low-beneficial SDs in the scenario utilizing the SSMVEP sessions yields a remarkable CA, while removing seven low-beneficial SDs and assigning S6 the role of TD for the scenario using our own recorded MI subjects acquired from the BCI competition IV-a database also yields a remarkable CA for both the inter-session and inter-subject classification scenarios, respectively.
Notably, employing DTE based on a four-class inter-subject problem making use of SSMVEP subjects yields the highest CA across all the TDs when a single less-beneficial SD is removed and S7 is assigned the role of TD, while the highest CA is recorded when S2 is selected as the TD and seven less-beneficial SDs are removed for the four-class inter-subject scenario using our own recorded MI subjects. However, a decrease in the classification performances is observed across the individual TDs whenever a single less-beneficial SD is removed in each iteration, as a result of individual variations across the domains, since different domains respond differently to other domains.
Number of Removed Low-Beneficial Source Domains Using LSA-MMFT
Table 4 depicts the number of low-beneficial SDs discarded in each iteration whenever an individual target domain is assigned when the LSA-MMFT algorithm is employed to perform source selection based on two-class, three-class, and four-class inter-session and inter-subject classification scenarios. In this case, employing LSA-MMFT to select high beneficial domains by evaluating label similarities across the domains and discard a single low-beneficial domain in each iteration yields remarkable CAs. However, significant variations in the CAs across individual TDs is observed with LSA-MMFT performing poorly across several individual TDs. As such, assigning Se1 the role of TD and discarding a single low-beneficial SD yields a remarkable CA; however, significant variations in neural dynamics result in a decline in the CAs across several individual TDs when LSA-MMFT is employed, based on the two-class inter-session classification scenario using the SSMVEP sessions, while a remarkable CA is also observed when S3 is selected as the TD and six low-beneficial SDs are removed in the two-class inter-subject classification scenario using the MI subjects acquired from the BCI competition IV-a database.
Moreover, employing LSA-MMFT based on a two-class inter-subject scenario making use of SSMVEP subjects yields the highest CA across all the TDs when a seven low-beneficial SD is removed and S7 is assigned the role of TD, while the highest CA is recorded when S2 is assigned the role of TD and seven low-beneficial SDs are removed in the two-class inter-subject scenario using our own recorded MI subjects.
When the LSA-MMFT framework is employed, based on a three-class problem using SSMVEP sessions, the highest CA is recorded when Se6 is assigned the role of TD and a single low-beneficial SD is removed, while the highest prediction rate is recorded when S3 is assigned the role of TD and seven low-beneficial SDs are discarded in the scenario using our own MI subjects obtained from the BCI competition IV-a database. Selecting S6 as the TD and removing seven low-beneficial SDs yield the highest CA across all the TDs when LSA-MMFT is employed for the scenario using SSMVEP subjects, while the highest CA is recorded when S9 is assigned the role of TD and a single low-beneficial SD is removed in the three-class inter-subject scenario using our own recorded MI subjects.
Notably, the four-class classification problem demonstrates to pose severe implications for the classification performance when LSA-MMFT is employed. Hence, assigning Se4 the role of TD and removing five low-beneficial SDs yield a remarkable CA, while selecting S6 as the TD and removing seven low-beneficial SDs also yield remarkable CAs for both the inter-session and inter-subject classification scenarios using the BCI competition IV-a dataset, respectively. Furthermore, for the four-class inter-subject scenario using our own recorded SSMVEP subjects, the highest CA is observed when S7 is assigned the role of TD and six low-beneficial SDs are removed, while the highest CA is observed when S1 is selected as the TD and two low-beneficial SDs are removed in the scenario using our own recorded MI subjects. However, removing a single low-beneficial SD in each iteration from a pool of domains consisting of both low- and high beneficial domains results in a decrease in the CA across individual TDs, mainly as a result of individual variations across the domains.
Table 5 depicts the mean prediction rate for a comparative performance evaluation across all four domain selection algorithms under two-class, three-class, and four-class inter-session classification scenarios, whereby X-Sessions represents the inter-session classification scenario using our own recorded SSMVEP sessions. As such, DTE outperforms both ROD and LSA-MMFT, with a mean CA of 85.1% being recorded when DTE is employed for a two-class problem. However, a 9.1% increase in CA is observed, with a superior mean CA of 94.2% being recorded when EMC-MMFT is applied in a two-class inter-session classification scenario. Moreover, for a three-class inter-session scenario, DTE yields a remarkable mean CA compared to both ROD and LSA-MMFT, with a mean CA of 68.9% being recorded when DTE is employed. However, a superior mean CA of 84.1% is recorded when EMC-MMFT is applied, whereby a 15.2% increase in CA is observed compared to DTE.
Furthermore, performance evaluation has also been carried out for a more complex classification problem—in this instance, a four-class problem. Superior performances across all inter-session classification scenarios under
Table 5 is denoted by bold texts. As such, DTE yields a superior performance compared to ROD and LSA-MMFT, with the highest mean CA of 61.2% being recorded when DTE is employed in a four-class inter-session scenario. However, a 13.85% increase in the mean CA is observed when EMC-MMFT is employed and compared with DTE, with a superior mean CA of 75.05% being recorded when EMC-MMFT is employed. In this case, the superior performances recorded when EMC-MMFT is employed can be attributed to the minimal effect of NT, a result of the best selected combination of closely related highly beneficial SDs, which, in turn, significant increases the CA across individual TDs when TL is applied. However, a decline in CA when ROD, DTE, and LSA-MMFT are employed can be attributed to removing only a single low-beneficial SD in each iteration, since the remaining low-beneficial SDs still pose severe implications for the CA in the next iteration as a result of each domain responding differently to another domain.
Table 6 displays the mean classification result for a comparative performance evaluation across all four domain selection algorithms under two-class, three-class, and four-class inter-subject classification scenarios. In this case, X-S1 represent an inter-subject classification scenario using MI subjects acquired from the BCI competition IV-a dataset, while X-S2 represent an inter-subject classification scenario using our own recorded SSMVEP subjects, and X-S3 represent an inter-subject classification scenario using our own recorded MI subjects. Moreover, Superior performances across all inter-session classification scenarios under
Table 6 is denoted by bold texts. From the comparison, one finds that the complexity of a classification problem including the effect of low-beneficial sources has severe implications for the classification performance when a single low-beneficial domain is removed from a pool of domains in each iteration. Consequently, ROD outperforms both DTE and LSA-MMFT, with a mean CA of 85.1% being recorded when ROD is employed. However, an 8.5% increase is observed when EMC-MMFT is employed in X-S1, with a superior mean CA of 93.9% being recorded. Moreover, a 20.5% increase and a superior mean CA of 84.5% are observed when EMC-MMFT is applied and compared with DTE. In this instance, DTE outperforms both ROD and LSA-MMFT, with a mean CA of 64% being recorded when DTE is employed in X-S2 for a two-class problem. A superior mean CA of 78.4% is recorded when EMC-MMFT is employed in X-S3 for a two-class problem, and a 10.8% increase is observed as compared to the performance of ROD, which achieves a mean CA of 67.6% and outperforms both DTE and LSA-MMFT.
Moreover, for the three-class inter-subject scenario, ROD yields a remarkable mean CA compared to both DTE and LSA-MMFT, with a mean CA of 69.4% being recorded when ROD is employed in X-S1. However, a superior mean CA of 80% is recorded when EMC-MMFT is applied, while a 10.6% increase in CA is observed compared to ROD’s. For the three-class inter-subject classification scenario based on X-S2, a superior mean CA of 72.8% is recorded when EMC-MMFT is applied, while a 30.5% increase in CA is observed when it is compared to DTE, whose highest mean CA of 42.3% is recorded when it is applied and compared to both ROD and LSA-MMFT. A superior mean CA of 64.3% is achieved when EMC-MMFT is implemented based on X-S3 for a three-class problem, with a 16.7% increase in CA being observed when compared to ROD’s, which achieves a mean CA of 47.6% and, at the same time, outperforms both DTE and LSA-MMFT.
Furthermore, in our study, performance evaluation has also been carried out for a more complex classification problem—in this instance, a four-class problem. DTE yields a superior performance compared to ROD and LSA-MMFT, with the highest mean CA of 56.9% being recorded when DTE is employed based on X-S1. However, a 10.5% increase in the mean CA is observed when EMC-MMFT is employed and compared with DTE, with a superior mean CA of 67.4% being recorded when EMC-MMFT is employed.
Moreover, a 27.6% increase in the mean CA is observed when EMC-MMFT is employed and compared with DTE, with a superior mean CA of 59.8% being recorded when EMC-MMFT is employed based on X-S2, while DTE achieves the highest mean CA of 59.8% compared to both ROD and LSA-MMFT. An inferior mean CA is observed when ROD, DTE, and LSA-MMFT are employed based on X-S3 for a four-class problem. However, a 26.5% increase in CA is observed when EMC-MMFT is applied and compared to DTE, which achieves a mean CA of 36.5% and outperforms both ROD and LSA-MMFT. The superior classification performances achieved in this instance are attributed to the selected optimal combination of high beneficial domains after low-beneficial domains have been discarded at once, when EMC-MMFT is employed. In this case, high beneficial SDs are mainly selected to minimize the effect of NT and utilized to enhance the prediction rate across individual TDs. Hence, superior classification performances are observed when EMC-MMFT is employed, while inferior performances are attributed to the remaining non-related low-beneficial domains when a single low-beneficial domain is removed at a time, when ROD, DTE, and LSA-MMFT are employed.
These results further validate the fact that removing a single low-beneficial domain in each iteration poses severe implications for the classification performance across individual TDs, while selecting only the best combination of closely related high beneficial sources, denoted by superior classification performances, can significantly enhance the prediction rate across individual TDs.