*3.5. The E*ff*ect of Number of Channels on the Feature Sets across Windowing Parameters*

Finally, we investigated the influence of the number of electrode channels on limb movement intent classification across window length (150 ms~300 ms) with a window increment of 100 ms for all the feature sets using sEMG recordings from 2, 4, and 6 channels, and the obtained results are presented in Figure 8a–f for both the able-bodied and amputee subjects. Figure 8a,b represent the classification performances of all the features using two channels. Therefore, the CE of all the feature sets (except PKF) decreased with increasing window length and this trend was consistent with the other results obtained when sEMG recordings from 4 and 6 channels were utilized (Figure 8c–f) for both able-bodied and amputee subjects. In other word, the CE reduces with increasing window length and number of channels regardless of the kind of feature set employed.

In like manner, the classification performances of the feature extraction methods were again observed to be better for the able-bodied subjects compared to the amputee subjects (Figure 8a–f) when sEMG recordings from the same number of electrode channels were utilized. In other words, it could be seen that the PKF (able-bodied: 65.65 ± 1.58%, amputee: 72.90 ± 0.72%) and MDF (able-bodied: 52.65 ± 3.02%, amputee: 56.21 ± 3.21%) features recorded the highest CE values for 2-channel sEMG recordings regardless of the type of participants, while the NTDF (able-bodied: 4.05 ± 0.83%, amputee: 6.15 ± 1.54%) and TD-PSD (able-bodied: 16.54 ± 1.78%, amputee: 22.81 ± 2.55%) features achieved the least CE values.

In similar trend, the same phenomenon was observed for 4-channels and 6-channels sEMG recording though with slight decrease in CE values. In general, utilizing sEMG recordings from 6-channels achieved the lowest CE for all the feature sets. Hence, such variability indicate that the number channels utilized may influence the classification performance of EMG-PR classifiers. It is important to note that the computation time of all the features across varying window length increases with increasing number of electrode channels, indicating a trade-off between number of electrode channels and computation time.

*Symmetry* **2020**, *12*, x FOR PEER REVIEW 15 of 20

**Figure 8.** Mean classification error across varying window length at window increment of 100 ms for (**a**) 2-channel recording of able-bodied subjects; (**b**) 2-channel recording of amputee subjects (**c**) 4 channel recording of able-bodied subjects; (**d**) 4-channel recording of amputee subjects (**e**) 6-channel recording of able-bodied subjects (**f**) 6-channel recording of amputee subjects. **Figure 8.** Mean classification error across varying window length at window increment of 100 ms for (**a**) 2-channel recording of able-bodied subjects; (**b**) 2-channel recording of amputee subjects (**c**) 4-channel recording of able-bodied subjects; (**d**) 4-channel recording of amputee subjects (**e**) 6-channel recording of able-bodied subjects (**f**) 6-channel recording of amputee subjects.

## **4. Discussion 4. Discussion**

A detailed analysis of the experimental results obtained from this study revealed that multiple factors, including windowing parameters, choice of feature sets, and number of electrode channels would influence the overall performance of myoelectric pattern recognition system that adopts linear A detailed analysis of the experimental results obtained from this study revealed that multiple factors, including windowing parameters, choice of feature sets, and number of electrode channels would influence the overall performance of myoelectric pattern recognition system that adopts linear

discriminant analysis classifier. A few previous works recognized the need for such study and they had attempted to investigate the effect of window length and increment on myoelectric controller delay though in the context of limited factors [12–14,16,38]. Remarkably, this study considered additional critical factors by investigating the effect and interactions amongst windowing parameters and feature sets with respect to classification error, computational time, robustness to noise and number of electrode channels on the overall performance of pattern recognition system. Investigation on the interaction of these factors towards realizing a consistently stable and accurate EMG-PR scheme for multi-class movement intent decoding has rarely been considered till date. It is worth noting that this investigation may be adopted in other fields of study [39,40].

More precisely, the results presented in Figure 4 demonstrate the effect of windowing parameters (window length and window increment) on the sixteen feature sets and their resultant influence on the classification performance of the EMG-PR classifier across movement classes and subjects. In general, for all the considered feature sets, the classification error reduces as the window length increases, however we found that window increment do not have direct effect on the classification performance which corroborate the findings from a previous study [14]. Additionally, the multi-feature sets of NTDFS, TD-PSD, and TD5AR6 achieved the minimum average classification errors and deviations of 1.07 ± 0.36%, 3.52 ± 0.83%, and 4.70 ± 1.07%, across subjects compared to the single features selected from the four EMG feature functional groups presented in Table 2. One possible reason for the improved performance observed for the multiple feature sets of NTDFS, TD-PSD, and TD5AR6 should be because they integrate neuromuscular information from multiple dimensions, thereby aggregating rich set of information for the movement intent decoding tasks compared to the other single features. From the perspective of the single feature set, AR6 and MNP were observed to have achieved better performance than other single features by recording classification error as low as 5.70 ± 1.45%, and 6.52 ± 1.33%, respectively. One core benefit of this findings is that the classification performance of EMG-PR system can be improved by using a combination of features from the four EMG feature functional groups presented in Table 2, rather than considering single feature set. Therefore, this findings corroborates the report of a previous study [39].

Generally, the least classification error across features was achieved at window length of 300 ms and increment of 100 ms and it could be seen that window increment do not have direct influence on the classification performance. Most features recorded the least classification error at window increment of 100 ms hence it was adopted for subsequent results presented in this study. Analysis of computation time of the feature sets across varying window length and increments was also reported in Figure 5. Here, we observed from the result that the multi-features attracts high computation time, compared to the single features. From the angle of computational complexity, the multiple NTDFS feature set that recorded the lowest movement intent decoding error was observed to have had the highest average computation time (385.1 ms) followed by the TD5AR6 multiple feature (226.9 ms). In addition, increasing the analysis window length resulted to corresponding increase in computation time, indicating a trade-off between classification performance and computation time. Therefore, this further provided us with the insight that it may be beneficial to consider features with relatively lower classification error and slightly higher computation time if the goal is to achieve a classifier with high performance in terms of accuracy that could also output its decision within a reasonable time-frame.

The performances of the feature sets were examined across varying window length of 150 ms–300 ms at a window increment 100 ms based on the feature data points in the feature space using F1-score metric, and the outcome was presented in Tables 3 and 4 for both able-bodied and amputee subjects. From detailed analysis based on EMG recordings from both categories of subjects (amputees and able-bodied individuals), it was observed that the NTDFS and TD-PSD (multiple features) recorded the highest accuracies as against the PKF and MDF (single features) that recorded the lowest accuracies. Also, Tables 3 and 4 showed that there is no significant difference in the classification accuracy across the varying window length, while the F1-score results were consistent with the results presented in Figure 1, thereby further supporting our findings.

Furthermore, we investigated the performances of the feature sets in the presence of a disturbance, by introducing random noise into the EMG recording signal and the features' performances were evaluated using the stability index metric across subjects and window lengths (150 ms–300 ms) as shown in Figures 6 and 7. In this investigation, we found that the multi features, such as NTDFS, TD-PSD, and TD5AR6 recorded the least classification error, while PKF, MDF, and WL features recorded the highest classification error for both able-bodied and amputee subjects. Interestingly, this result further proves that multi features would be more robust to external interferences (noise) compared to the single features irrespective of the windowing parameters considered. Also, we observed that the effect of the introduced noise was much obvious on the sEMG recordings of the amputees compared to the able-bodied, which could be attributed to the fact that the residual arm muscles of the amputees may produce less-rich information than those of the able-bodied subjects in an ideal situation. Therefore, considering the fact that amputees are the end-users of the myoelectric device, there is need to employ a robust feature sets that will help to enhance classification performance of EMG-PR control system regardless of the windowing parameters adopted.

Lastly, we examined the effect of the number of channels on the extracted features across varying window parameters and we found that the classification error of the features reduces with increasing number of channels for both able-bodied and amputee subjects. (Figure 8a–f). In similar trends with other results, the multi-features outperformed the single features when 2-channels, 4-channels and 6-channels were considered. Finally, by critically analyzing of our results, we discovered that when classification error, computation time, and number of electrodes were considered together, most feature sets achieved good classification performance with optimal windowing parameters of 250 ms/100 ms. Also, discoveries from this study through the systematic approach adopted can facilitate positive development in other areas where optimal features and machine learning driven approaches are required [41–50]. Last, one limitation of the current work is that the EMG pattern recognition system for movement intent decoding was analyzed in an off-line mode, and we hope to conduct online and real-time analysis in our future work. By doing so, it would further broaden the applicability of the current study in real-life applications.
