## 4 Results
### Single-Channel Analysis
The wavelet features derived from the level seven decomposition revealed two to three divisible groups among the disabilities, whereas the level six decomposition showed individual differences. Figure 3 illustrates the comprehensive results of single-channel classification for the EC-data with both 1 s and 2 s segmentations. We found that the wavelet features showed better classification performance results than those using other features. Among the classification algorithms, RF showed better classification performance than the others. While the 2 s segmentation showed slightly better classification results than the 1 s segmentation, variations in classification performance were noted across the channels depending on the segmentation (either 1 s or 2 s segmentation). Specifically, the channels F3, F4, F8, and CP4 showed enhanced performance results with the 1 s segmentation. From the evaluation of the classification performance results, we found an improvement in AUC with increased segmentation length. For the single-channel classification evaluation, we used the wavelet ('db4'). Alternatively, different wavelets can be utilized to extract wavelet features. Based on our evaluation with another wavelet ('coif'), we found slightly different classification performance results. For instance, the F1 classification scores for analyzing the EC-data were determined as RF (\(0.85\pm 0.0002\)), SVM (\(0.79\pm 0.0001\)), KNN (\(0.83\pm 0.0003\)), and LR (\(0.80\pm 0.0001\)). When analyzing the EO-data, the F1 scores were identified as RF (\(0.85\pm 0.0002\)), SVM (\(0.80\pm 0.0002\)), KNN (\(0.83\pm 0.0002\)), and LR (\(0.80\pm 0.0002\))). We found the AUC scores to be RF (\(0.60\pm 0.0005\)), SVM (\(0.51\pm 0.0003\)), KNN (\(0.59\pm 0.0004\)), and LR (\(0.51\pm 0.0003\)). Although slightly different classification results can be observed depending on the applied wavelet, we determined that the wavelet features produced better classification performance results than other features. By evaluating the classification performance results for the features with the 2 s segmentation using the level six and seven decompositions (\(l=6\) and 7), we found higher F1 scores with \(l=6\). But similar AUC scores were observed between them. When using the wavelet features extracted from the EC-data with 'db4', we found the overall average F1 scores for all channels to be RF (\(0.880\pm 0.0002\)), SVM (\(0.860\pm 0.0001\)), KNN (\(0.856\pm 0.0003\)), and LR (\(0.860\pm 0.0001\)). For evaluating the wavelet features from the EO-data, the F1 scores were determined as RF (\(0.851\pm 0.0014\)), SVM (\(0.800\pm 0.0011\)), KNN (\(0.831\pm 0.0012\)), and LR (\(0.801\pm 0.0008\)). The overall average AUC of all channels from the EC-data was measured as RF (\(0.601\pm 0.0001\)), SVM (\(0.510\pm 0.0002\)), KNN (\(0.574\pm 0.005\)), and LR (\(0.513\pm 0.0012\)). For the EO-data, the performance results were RF (\(0.610\pm 0.0001\)), SVM (\(0.510\pm 0.0001\)), KNN (\(0.582\pm 0.0003\)), and LR (\(0.510\pm 0.0002\)). Overall, we found slightly better performance results with \(l=6\) when comparing the F1 and AUC scores with 2 s segmentation. We also found slightly better performance in multiple channels (Fp1, Fp2, F3, F4, and FcZ) with \(l=7\).