## 4 Results
### Region-Based Analysis  
We also conducted a region-based channel analysis on the five regions (frontal, temporal, central, parietal, and occipital). Figure 4 shows the F1 scores for the applied classification algorithms. When evaluating the performance results between 1 s and 2 s segmentations, the 2 s segmentation showed slightly better results. We also found that the wavelet features performed better with the region-based classification. Because of numerous performance results from our study, three region-based classification results (accuracy, precision, and AUC) are listed in Appendix A. Notably, we observed high accuracy and precision scores for the regions utilizing the wavelet features from the region-based classification analysis on the EC- and EO-data. We also found that the wavelet features showed better AUC scores. Among the classification algorithms, both KNN and RF showed better classification performance results. More importantly, we determined that RF demonstrated high classification performance results with the wavelet features (see the red glyphs in Figure 4B). High F1 scores were identified in the frontal region with ML techniques. The wavelet features also showed high F1 scores with SVM, RF, and LR in all regions except KNN. When using KNN, minor differences were observed between the wavelet and EEG frequency band features in the frontal brain region (see Figure 4C). The EEG frequency band features showed comparable performance with the wavelet features in the frontal region. Except for the frontal region, all other regions performed better with the wavelet features.  
Additionally, we performed a comparison of the classification results employing different wavelets (\(w\)) and decomposition levels (\(l\)). Table 3 presents the F1 scores for the region-based classification with the 2 s segmentation using two wavelets (\(w=\) 'coif3' and 'db4') and two decomposition levels (\(l=6\) and 7). We observed similar F1 scores between the two wavelets, with minor differences depending on the decomposition levels and the specific classification algorithms. Overall, the frontal region showed higher performance results across the regions. RF and KNN showed higher performance results with the EC- and EO-data. Specifically, the occipital and frontal regions showed enhanced performance results with \(l=6\) and \(w=\) 'coif3', while the central region exhibited better performance with \(l=7\) and \(w=\) 'coif3'. However, considering the frequency of achieving higher performance by each classification algorithm, 'db4' was selected for \(l=6\) (55%) with the  
Figure 3: Average classification performance comparisons of four extracted features (i.e., EEG frequency band, PSD, raw, and wavelet) for all channels in the EC-data. The classification of the four extracted features using the 1 s and 2 s segmentations was evaluated.  
EO-data to be SVM (0.885), RF (0.924), KNN (0.980), and LR (0.673), and for the EC-data, SVM (0.903), RF (0.953), KNN (0.980), and LR (0.629). Overall, KNN showed better results when comparing the AUC scores. By evaluating the average classification scores for all classification algorithms, we observed that the AUC scores with \(l=6\) and \(w=\) 'db4' were higher than with \(l=6\) and \(w=\) 'cofi3' except for the central region in the EC-data. With the EO data, \(l=6\) and \(w=\) 'db4' was higher in the frontal, occipital, and parietal regions. With the level seven decomposition (\(l=7\)), the 'db4' performed better for the regions except for the frontal region with the EC data. With the EO data, the 'db4' was superior for all regions except the frontal and occipital regions.