## 5 Discussion
For understanding the neurological disabilities depending on their age groups, multiple parallel coordinates visualizations were generated as shown in Figure 7. With the traditional parallel coordinates visualization (i.e., representing each instance as polyline), we could not determine the difference between the age groups when analyzing the data. However, we identified the difference between the groups by evaluating the angular histograms (bin size = 25) that present the distribution of each variable. For instance, the variable ('Cz07') represents fat-tailed distributions (i.e., skewed distributions). But distinctive distributions were observed depending on the age groups. In detail, the high peak appeared near the centerline of the angular histograms in the age 20s. But, the high peak existed at the bottom edge of the angular histograms in the age 70s. The differences, depending on the age groups, were more clearly visible in the variable 'C417'. Specifically, the distribution in the age 40s showed skew normal distribution, having normal distribution but leaning toward one side of the distribution. Although identifying the primary cause of such distinctive distributions depending on the age groups cannot be determined from the visualization, it highlights the need to conduct extensive analysis to understand the effectiveness of each attribute in differentiating the distributions in the age groups. We performed [PERSON]'s Honest Significant Difference (HSD) test to examine the differences between the age groups. We found most variables showed significant differences (\(p<0.05\)) between the age groups. For instance, the variable ('Cz07') in the Cz channel showed significant differences (\(p<0.05\)) except for the age groups between 0 and 50 (\(p=0.9\)), 0 and 70 (\(p=0.0584\)), 20 and 70 (\(p=0.894\)), and 30 and 60 (\(p=0.925\)). The variable ('C417') in the C4 channel presented significant differences (\(p<0.05\)) except for the age groups between 20 and 30 (\(p=0.8348\)), and 40 and 50 (\(p=0.9\)). Figure 7: Analyzing patterns by age groups with parallel coordinates visualizations. Statistically significant wavelet features from the central region (window size = 1 s and bin size = 25) are used to generate the visualization. Each horizontally positioned parallel coordinates visualization represents an individual age group (i.e., 0: 0\(\sim\)9, 10: 10\(\sim\)19, \(\ldots\), 70: 70\(\sim\)79). Various background color attributes are used to represent data patterns in different age groups. We conducted an additional analysis to understand the difference between the disorders by generating multiple parallel coordinates visualizations depending on the conditions (seven neurological disorders and one healthy condition). Figure 8 shows eight parallel coordinates visualizations with angular histograms (bin size = 25) arranged horizontally. From the figure, we determined distinctive patterns depending on the conditions. For instance, the healthy condition showed similar frequency distributions compared to the disorders (i.e., Parkinson's disease and MDD) in the variables ('C300'\(\sim\)'Cz06'--from left to right in the visualization). But the rest of the other variables represented distinctive distributions between them. For example, the variables ('Cz10' and 'Cz11') in the healthy condition showed highly skewed distributions (i.e., asymmetric distributions) (see Figure 8A). Although the disorders MDD and Parkinson's disease also represented asymmetric distributions, their distributions were different (see Figure 8B and 8C, respectively). More interestingly, Parkinson's disease shows peaks at the bottom edge of the distributions (see Figure 8C). These findings can be used to extract more critical features from data to differentiate the disabilities. The disorders chronic pain and dyslexia showed similar frequency distributions in the variables ('C300'\(\sim\)'Cz06'--from left to right in the visualization).