## 5 Discussion  
By evaluating the classification performance results, we found that the segmentation length (2 s) is suitable for analyzing the disabilities. We also identified that RF showed better performance results than the other algorithms. When evaluating the difference using the decomposition levels (\(l=6\) or 7), we found a clear distinction between chronic pain and healthy with \(l=6\). With the level \(l=6\), we also observed slightly better performance results in most channels except the four channels (F3, Fp1, F4, Fcz, and P4). The four channels showed opposite results with the level \(l=7\). These results indicated that a higher decomposition level should be applied for analyzing certain disabilities. However, this statement requires an extensive analysis of identifying the relationship between decomposition levels and EEG channels in producing classification performance results for analyzing disabilities. Since this analysis is not a primary consideration of this study, we leave this as our future work. Comparing the two wavelets ('db4' and 'coif3'), we found similar classification performance results, even if different decomposition levels were applied. For the single-channel-based classification, we found a higher performance result with RF (1% difference) when using either wavelet. However, slightly better performance results with the 2 s segmentation and 'coif3' were determined when analyzing the EO-data with \(l=6\). Furthermore, for the region-based classification, 'coif3' performed better than 'db4.' Since DWT is suitable for detecting local events with different frequency scales, combining multiple wavelet features from different channels could synergistically improve the overall performance of identifying disabilities. As discussed above, the most influencing features were determined with the ANOVA test to distinguish the disabilities in the channels. The cardinality of determining the subset of the features as significant features varied depending on the channel, segmentation length, and feature approach. For example, four EEG frequency band features were selected in the O2 channel with the 2 s segmentation, producing the recall performance as SVM (0.787), RF (0.807), KNN (0.780), and LR (0.784) for analyzing the EO-data. For the EC-data, nine features were selected, producing the performance results of SVM (0.859), RF (0.857), KNN (0.843), and LR (0.858). When using the wavelet features, the same cardinality of features was selected in the O2 channel, even when using different segmentation lengths. Instead, the C4 channel showed the different cardinality as important features between the two segmentation lengths. The recall scores with the 2 s segmentation were determined as the EC-data of SVM (0.883), RF (0.898), KNN (0.871), and LR (0.879), and the EO-data of SVM (0.852), RF (0.868), KNN (0.844), and LR (0.852). The same metric score performance  
Figure 6: Parallel coordinates with angular histograms of the (**A**) EEG frequency band, (**B**) PSD, (**C**) raw, and (**D**) wavelet features from the occipital region using the window size (1 s). Significant differences between normal and abnormal groups are emphasized with arrows. Zoomed representations of the highlighted regions (indicated with rectangular outlines) are arranged on the right side of the paralleled coordinates. Arrows indicate distinctive patterns compared to other data distributions. In detail, high-density regions (colored red in angular histograms) have appeared in multiple locations in the histograms. with the 1 s segmentation was SVM (0.813), RF (0.859), KNN (0.844), and LR (0.814) with the EC-data and SVM (0.714), RF (0.806), KNN (0.782), and LR (0.725) with the EC-data. Overall, the EO-data with the 2 s segmentation provide better performance results. We found that the higher the cardinality, the better the performance presented.