## 2 Methodology
### Generalization of the SVM-ML Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Delta**} & **Theta** & **Theta** & **Beta** & **Gamma** \\ \cline{2-6}  & Average Acc & 33.5\% \(\pm\) 0.05 & 37.8\% \(\pm\) 0.05 & 35.8\% \(\pm\) 0.03 & 38.2\% \(\pm\) 0.04 & 44.7\% \(\pm\) 0.11 \\ \cline{2-6}
62 & Peak Acc & 38.5\% & 51.8\% & 40.0\% & 47.6\% & 67.4\% \\ \cline{2-6}  & Peak Loc (\(\gamma\), C) & (\(2^{-3},2^{5}\)) & (\(2^{3},2^{1}\)) & (\(2^{-5},2^{1}\)) & (\(2^{-7},2^{7}\)) & (\(2^{3},2^{9}\)) \\ \hline \multirow{4}{*}{21} & Average Acc & 39.1\% \(\pm\) 0.06 & 35.6\% \(\pm\) 0.04 & 33.2\% \(\pm\) 0.06 & 41.3\% \(\pm\) 0.07 & 49.2\% \(\pm\) 0.16 \\ \cline{2-6}  & Peak Acc & 57.3\% & 49.3\% & 38.5\% & 57.8\% & 76.8\% \\ \cline{2-6}  & Peak Loc (\(\gamma\), C) & (\(2^{-3},2^{11}\)) & (\(2^{3},2^{13}\)) & (\(2^{-13},2^{10}\)) & (\(2^{-5},2^{13}\)) & (\(2^{1},2^{3}\)) \\ \hline \hline \end{tabular} Note: Average Acc: average classification accuracy of the SVM-ML model with all the RBF kernel hyperparameter pairs; Peak Acc: highest classification accuracy of the SVM-ML model in the predefined range of (\(\gamma\), C); Peak Loc: location (\(\gamma\), C) corresponding to the highest classification accuracy of the SVM-ML model.  
\end{table} 
Table 2: Accuracies of the SVM-ML model for classifying the three upper-limb groups with the RBF kernel hyperparameter pairs in different frequency bands. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CV**} & \multicolumn{5}{c}{**Accuracy**} \\ \cline{2-6}  & **Delta** & **Theta** & **Alpha** & **Beta** & **Gamma** \\ \hline
2-fold & 49.6\% \(\pm\) 0.07 & 38.1\% \(\pm\) 0.06 & 27.6\% \(\pm\) 0.06 & 50.7\% \(\pm\) 0.07 & 73.8\% \(\pm\) 0.05 \\ \hline
3-fold & 49.1\% \(\pm\) 0.06 & 33.3\% \(\pm\) 0.07 & 26.0\% \(\pm\) 0.05 & 51.0\% \(\pm\) 0.05 & 74.5\% \(\pm\) 0.04 \\ \hline
4-fold & 49.7\% \(\pm\) 0.06 & 34.7\% \(\pm\) 0.06 & 23.9\% \(\pm\) 0.05 & 50.1\% \(\pm\) 0.05 & 74.8\% \(\pm\) 0.04 \\ \hline
5-fold & 49.7\% \(\pm\) 0.06 & 32.4\% \(\pm\) 0.05 & 26.1\% \(\pm\) 0.05 & 51.2\% \(\pm\) 0.04 & 75.0\% \(\pm\) 0.03 \\ \hline
6-fold & 53.2\% \(\pm\) 0.05 & 35.0\% \(\pm\) 0.06 & 22.2\% \(\pm\) 0.05 & 50.8\% \(\pm\) 0.05 & 75.4\% \(\pm\) 0.04 \\ \hline
7-fold & 46.7\% \(\pm\) 0.06 & 32.6\% \(\pm\) 0.05 & 32.9\% \(\pm\) 0.05 & 48.2\% \(\pm\) 0.06 & 72.6\% \(\pm\) 0.04 \\ \hline
8-fold & 47.0\% \(\pm\) 0.07 & 31.3\% \(\pm\) 0.06 & 28.9\% \(\pm\) 0.06 & 47.2\% \(\pm\) 0.06 & 74.8\% \(\pm\) 0.04 \\ \hline
9-fold & 49.5\% \(\pm\) 0.07 & 31.4\% \(\pm\) 0.06 & 27.8\% \(\pm\) 0.06 & 49.1\% \(\pm\) 0.06 & 74.7\% \(\pm\) 0.05 \\ \hline
10-fold & 51.7\% \(\pm\) 0.07 & 33.0\% \(\pm\) 0.06 & 25.9\% \(\pm\) 0.06 & 50.0\% \(\pm\) 0.05 & 74.8\% \(\pm\) 0.05 \\ \hline LOO & 51.3\% & 28.2\% & 12.8\% & 53.8\% & 74.4\% \\ \hline \hline \end{tabular} Note: Data are given as mean \(\pm\) SD. CV: cross validation; LOO: leave-one-out.  
\end{table} 
Table 3: Accuracies of the SVM-ML model for classifying the three upper-limb groups with different k-fold CV strategies in the different frequency bands. The generalized performance of the model was also evaluated by considering the arm differences during stimulations with different fabrics (Table 5). The classification accuracies of each upper-limb group during stimulations with different fabrics are not normally distributed (\(p<0.5\), Shapiro-Wilk test). Significant differences in the accuracies (\(p<0.001\), Kruskal-Wallis test) with respect to fabric stimulations were observed in each band, except for the SU group in the gamma band (\(p>0.05\), Kruskal-Wallis test). The highest classification accuracy for each upper-limb group was achieved in the gamma band.