## 4 Results
### Visual Analysis  
To understand the patterns and structures of the different features, PCA-based visualization is performed. Figure 5 shows examples of the PCA projections of different features. To generate the PCA projections, the first- and second-principal components are determined and used to show all instances in 2D scatterplots, mapped to the \(x\)- and \(y\)-axis, respectively. Although different visualizations were generated depending on the features, we could identify similarities among PCA projections. For instance, the visual representations with the EEG frequency band and wavelet features from the occipital region showed similar widely spread patterns (see Figure 5A,D). Triangular shapes were also observed when analyzing the EEG frequency band and wavelet features from the O2 channel (see Figure 5E,H). For the PSD features, we could not find any similarities compared to the visualizations with other features. But, we noticed completely different patterns when analyzing the visualizations with the raw features (see Figure 5C,G). This \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{\(I\)} & \multirow{2}{*}{\(\mathbf{w}\)} & \multicolumn{4}{c}{**EC**} & \multicolumn{4}{c}{**EO**} \\ \cline{3-10}  & & & **SVM** & **RF** & **KNN** & **LR** & **SVM** & **RF** & **KNN** & **LR** \\ \hline \multirow{4}{*}{Central} & \multirow{2}{*}{Six} & db4 & 0.880 & 0.937 & 0.943 & 0.816 & 0.885 & 0.945 & 0.916 & 0.757 \\ \cline{3-10}  & & coif3 & 0.901 & 0.943 & 0.946 & 0.749 & 0.882 & 0.939 & 0.953 & 0.753 \\ \cline{2-10}  & & \multirow{2}{*}{Seven} & db4 & 0.886 & 0.951 & 0.947 & 0.760 & 0.904 & 0.954 & 0.953 & 0.706 \\ \cline{3-10}  & & & coif3 & 0.878 & 0.931 & 0.936 & 0.845 & 0.885 & 0.940 & 0.932 & 0.846 \\ \hline \multirow{4}{*}{Frontal} & \multirow{2}{*}{Six} & db4 & 0.969 & 0.976 & 0.987 & 0.859 & 0.987 & 0.989 & 0.992 & 0.866 \\ \cline{3-10}  & & coif3 & 0.977 & 0.974 & 0.990 & 0.762 & 0.973 & 0.978 & 0.986 & 0.730 \\ \cline{2-10}  & & \multirow{2}{*}{Seven} & db4 & 0.963 & 0.979 & 0.990 & 0.707 & 0.959 & 0.985 & 0.992 & 0.695 \\ \cline{3-10}  & & & coif3 & 0.964 & 0.978 & 0.991 & 0.877 & 0.971 & 0.987 & 0.993 & 0.857 \\ \hline \multirow{4}{*}{Occipital} & \multirow{2}{*}{Six} & db4 & 0.865 & 0.927 & 0.939 & 0.815 & 0.919 & 0.960 & 0.938 & 0.697 \\ \cline{3-10}  & & & coif3 & 0.884 & 0.939 & 0.950 & 0.774 & 0.802 & 0.886 & 0.946 & 0.956 \\ \cline{2-10}  & & \multirow{2}{*}{Seven} & db4 & 0.884 & 0.941 & 0.929 & 0.738 & 0.841 & 0.876 & 0.944 & 0.943 \\ \cline{3-10}  & & & coif3 & 0.903 & 0.938 & 0.937 & 0.833 & 0.884 & 0.954 & 0.943 & 0.841 \\ \hline \multirow{4}{*}{Parietal} & \multirow{2}{*}{Six} & db4 & 0.855 & 0.935 & 0.942 & 0.819 & 0.898 & 0.955 & 0.937 & 0.762 \\ \cline{3-10}  & & & coif3 & 0.851 & 0.938 & 0.937 & 0.754 & 0.874 & 0.936 & 0.942 & 0.759 \\ \cline{2-10}  & & \multirow{2}{*}{Seven} & db4 & 0.896 & 0.939 & 0.939 & 0.750 & 0.892 & 0.957 & 0.950 & 0.709 \\ \cline{3-10}  & & & coif3 & 0.877 & 0.929 & 0.923 & 0.824 & 0.878 & 0.947 & 0.941 & 0.827 \\ \hline \multirow{4}{*}{Temporal} & \multirow{2}{*}{Six} & db4 & 0.817 & 0.899 & 0.883 & 0.815 & 0.797 & 0.913 & 0.858 & 0.760 \\ \cline{3-10}  & & & coif3 & 0.794 & 0.901 & 0.879 & 0.747 & 0.817 & 0.918 & 0.906 & 0.672 \\ \cline{1-1} \cline{2-10}  & & \multirow{2}{*}{Seven} & db4 & 0.807 & 0.909 & 0.883 & 0.699 & 0.825 & 0.916 & 0.888 & 0.765 \\ \cline{1-1} \cline{2-10}  & & & coif3 & 0.844 & 0.910 & 0.887 & 0.831 & 0.830 & 0.911 & 0.900 & 0.804 \\ \hline \hline \end{tabular} \(l\): decomposition level, \(w\): wavelet.  
\end{table} 
Table 3: F1 scores of using two wavelets and two decomposition levels on the EC- and EO-data. might happen because of the small number of attributes and their high similarities. The visualizations with the features from the O2 channel showed various possible outliers (see the arrow). The raw features showed a dense region in the bottom left corner. Two mostly extreme outliers, representing ADHD and Parkinson's instances, appeared in each corner of the PCA space.