## 3 Experimental Results and Discussion
### Results of the Feature-Based Evaluation Procedure
Table 3 shows that the average classification accuracy obtained using only the \(TF_{7}\), \(TF_{9}\), and \(TF_{12}\) features, which is equal to 89.24%, is higher that the average classification accuracy obtained using all the CWD-based time-frequency features, which is equal to 84.6%, as shown in Figure 4. In addition, the results presented in Table 3 suggest that the best classification accuracy can be obtained by eliminating all the features except \(TF_{7}\), \(TF_{9}\), and \(TF_{12}\). This can be attributed to two factors: (1) The physical interpretation of the quantities measured by each of these three features. In particular, the feature \(TF_{7}\), which is the root mean square of the CWD, measures the strength of the signal energy in the time-frequency domain [6,7,17,23]. The feature \(TF_{9}\), which is the time-frequency flatness of the CWD, measures the rate of change of the signal energy in the time-frequency domain [6,7,17,23]. Finally, the feature \(TF_{12}\), which is the time-frequency energy of the CWD, measures the concentration of the signal energy in the time-frequency domain [6,7,17,23]. In fact, further analysis is needed to explore the correlation between the energy characteristics of the EEG signals and the capability of detecting pain. (2) Recently, researchers have shown that during pain there are several neural oscillations that are associated with different frequency bands and are generated across various regions of the brain [10,41]. Therefore, using the computed values of the aforementioned three features enables capturing the time-varying spectral oscillations encapsulated within EEG segments, where such time-varying spectral oscillations can contribute to the task of differentiating between the no-pain and pain classes. It is worth noting that the use of the time-frequency features \(TF_{9}\) and \(TF_{12}\) in different EEG-based classification problems, such as decoding human emotions [23] and motor imagery tasks [6], has yielded a higher classification accuracies compared with the other time-frequency features. This implies the capability of these time-frequency features to capture the nonstationary and nonlinear \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{3}{*}{\
\begin{tabular}{c} **Iterations of the** \\ **Feature Reduction** \\ **Procedure** \\ \end{tabular} }} & \multicolumn{10}{c}{**Eliminating Time-Frequency Feature**} \\ \cline{3-14}  & \(TF_{7}\) & \(TF_{9}\) & \(TF_{12}\) & \(TF_{1}\) & \(TF_{10}\) & \(TF_{11}\) & \(TF_{6}\) & \(TF_{2}\) & \(TF_{5}\) & \(TF_{4}\) & \(TF_{3}\) & \(TF_{8}\) \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Average classification \\ accuracy(\%) \\ \end{tabular} } & Iteration 1 & 84.36 & 82.21 & 84.41 & 84.53 & 84.54 & 85.05 & 84.61 & 84.68 & 83.59 & 85.67 & 85.39 & 86.63 \\ \cline{2-14}  & Iteration 2 & 86.04 & 84.45 & 86.22 & 86.23 & 86.44 & 86.73 & 86.23 & 86.45 & 85.93 & 87.35 & 87.68 \\ \cline{2-14}  & Iteration 3 & 87.24 & 85.79 & 87.25 & 87.44 & 87.63 & 87.95 & 87.45 & 87.61 & 86.62 & 88.62 & \\ \cline{2-14}  & Iteration 4 & 88.49 & 87.05 & 88.55 & 88.69 & 88.64 & 88.04 & 88.66 & 88.71 & 88.81 & \\ \cline{2-14}  & Iteration 5 & 87.87 & 83.98 & 87.94 & 88.31 & 88.25 & 88.29 & 88.25 & 88.39 & \\ \cline{2-14}  & Iteration 6 & 88.25 & 84.65 & 87.94 & 88.31 & 88.25 & 88.29 & 88.39 & \\ \cline{2-14}  & Iteration 7 & 87.91 & 84.25 & 87.73 & 88.3 & 88.26 & 88.5 & & & & \\ \cline{2-14}  & Iteration 8 & 88.47 & 86.37 & 88.62 & 88.72 & 88.88 & & & & & \\ \cline{2-14}  & Iteration 9 & 88.85 & 86.66 & 88.73 & 89.24 & & & & & & & \\ \cline{2-14}  & Iteration 10 & 88.51 & 86.83 & 89.02 & & & & & & & & \\ \cline{2-14}  & Iteration 11 & 87.01 & 87.06 & & & & & & & & & \\ \cline{2-14}  & Iteration 12 & 87.06 & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mean values of the average classification accuracies computed for each iteration of the feature reduction procedure over all subjects.