## 3 Experimental Results and Discussion
### Limitations and Future Work  
In spite of the promising results achieved using our proposed CWD-based approach in distinguishing between the no-pain and pain classes, we are planning to investigate several research directions that can enhance the accuracy and applicability of our proposed pain detection approach. Specifically, we are intending to explore four future research directions: (1) Recently, few researchers have successfully utilized deep learning techniques to analyze the time-frequency maps constructed for the EEG signals [42; 43]. In this regard, we intend to investigate the possibility of employing deep learning techniques, such as convolutional neural networks (CNN), to extract data-driven deep features from the constructed CWD-based TFR of the EEG signals that can enhance the accuracy of pain detection. (2) The subjective nature of pain, which introduces a significant intra- and intersubject variations in the responses of subjects to external painful stimuli, along with the nonstationary nature of the EEG signals increase the necessity to acquire a large-scale and balanced database that can be used to design a subject-independent pain detection system. In addition, the results obtained based on physiological signals, such as the EEG signals, that are acquired under well-controlled lab conditions may overestimate the results that can be achieved in real-world applications, in which minimally controlled acquisition conditions are maintained. In light of this, we plan to record a large-scale database under realistic recording conditions to assess the capability of our proposed CWD-based pain detection approach to support real-world applications. (3) We also plan to extend our work by utilizing the extracted time-frequency features to estimate the pain level that a patient is feeling. Such an extension can enhance the ability of the caregivers to characterize and range clinical pain for patients who are not capable to verbally express their feelings, such as infants. (4) We plan to extend  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Approach** & **Features** & **Accuracy(\%) \(\pm\) Standard Deviation (\%)** \\ \hline [PERSON] et al. [8] & Peak alpha frequency & \(81.1\pm 5.2\) \\ [PERSON] and [PERSON] [9] & Normalize PSD of the alpha and beta frequency bands & \(75.6\pm 8.7\) \\ This work & CWD-based time-frequency features & \(89.2\pm 3.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other approaches.  
Figure 7: The average \(F_{1}-\) measure accuracy values computed for the no-pain and pain classes per each subject over the ten train–test repetitions of the employed cross-validation procedure using the features \(TF_{7}\), \(TF_{9}\), and \(TF_{12}\). The black vertical lines represent the standard deviations of the classification accuracy and the red dashed line represents the random classification rate. The last column represents the mean value of the average \(F_{1}-\) measure values of the no-pain and pain classes computed over the 24 subjects.  
the analyses performed in the current study from subject-specific analyses to subject-independent analyses. The subject-independent analyses can measure the ability of our proposed CWD-based pain detection approach to detect pain for new subjects that were not included in the training set, which can be useful for several real-world applications.