## 4 Results
### Single-Channel Analysis  
Figure 2 shows examples of the four features extracted from the channels (O2, C4, Fp1, and T7) using the EC-data with the 'db4' wavelet and level six decomposition. The raw features exhibited subtle distinctions with minimal overlaps among the various disabilities. Specifically, the T7 channel presented slight differences in patterns between OCD, dyslexia, and Parkinson's (see Figure 2M). In both the O2 and T7 channels, OCD was distinguishable, presenting an isolated pattern (high values) compared to others (see Figure 2A,M). Using the PSD features, we found distinct patterns between the two disabilities (dyslexia and ADHD) and the healthy condition, particularly in the FP1 and O2 channels. Notably, the C4 and T7 channels exhibited nearly identical patterns between dyslexia and ADHD. When analyzing EEG frequency band features, a clear separation among dyslexia, Parkinson's disease, and MDD was determined. When employing wavelet features, the FP1 channel emerged as effective in differentiating between dyslexia, ADHD, Parkinson's disease, chronic pain, and the healthy condition. For the O2 channel, we observed differences, especially when comparing features of OCD, ADHD, and Parkinson's disease with others. Results for the disabilities (burnout and MDD) were almost identical across most channels. Additionally, chronic pain and Parkinson's disease showed almost similar patterns in the C4 channel. We also found a high similarity between MDD and Parkinson's disease in the FP1 channel. But, we noticed completely different patterns between them in some of the channels (O2, T7, and C4). MDD and burnout displayed high similarity across most channels, except for the T7 channel. Among the four features, the wavelet features were determined as superior in distinguishing the disabilities from healthy. Specifically, we identified that the wavelet features in the FP1 channel could be a good indicator for differentiating Parkinson's disease, OCD, ADHD, dyslexia, and healthy  
Figure 2: The comparisons of the channels (O2, C4, FP1, and T7) with the EC-data using the selected features with the segmentation length (=1 s). The ‘db4’ wavelet and the level six decomposition were used. The \(x\)-axis indicates each feature, and the \(y\)-axis denotes the mean (\(\mu\)) \(\pm\) SEM. The plots are arranged by channels (O2: (**A–D**); C4: (**E–H**); FP1: (**I–L**); and T7: (**M–P**)), and by feature type (Raw: (**A,E,I,M**); PSD: (**B,F,J,N**); EEGband: (**C,G,K,O**); Wavelet: (**D,H,L,P**)). because of the clear separation between them. With the 2 s segmentation, we observed the wavelet features in the C4 channel showed three distinctively separable groups: dyslexia and ADHD, chronic pain, and the rest of the disabilities. Utilizing the level six decomposition (\(l=6\)), we observed nearly identical patterns between ADHD and dyslexia in the O2 channel. However, a clear differentiation among the disabilities within the same channel became evident when employing the level seven decomposition (\(l=7\)). Furthermore, with the level seven decomposition, we identified a distinct separation between MDD and the healthy condition, which was not apparent with the level six decomposition. Distinctions between [PERSON]'s disease and burnout were highlighted through features extracted from level four and six detail coefficients and approximate coefficients in the T7 channel. We observed a clear separation among burnout, healthy, and other disabilities with the level seven decomposition, particularly when utilizing the features from the approximate coefficient corresponding to 1\(\sim\)4 Hz. We also found that the features with detail coefficient levels five, six, and seven presented distinctive separations across the disabilities: dyslexia and ADHD, and other disabilities. In the Fp1 channel, chronic pain showed a clearly distinguishable pattern from other disabilities, similar to the results obtained using the level six decomposition.