# Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features
## 4 Results
### Clustering Analyses
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**EEG Features**} & \multirow{2}{*}{**Subtopics**} & \multicolumn{4}{c}{**k-Means**} & \multicolumn{4}{c}{**DBSCAN**} \\ \cline{3-8}  & & **Low SI** & **Medium SI** & **High SI** & **Low SI** & **Medium SI** & **High SI** \\ \hline \multirow{4}{*}{Mean \(\delta\) power at F3} & D & 7 & 17 & 5 & 4 & 5 & 20 \\  & F & 5 & 12 & 12 & 12 & 8 & 9 \\  & H & 7 & 15 & 7 & 6 & 3 & 20 \\  & I & 7 & 13 & 9 & 13 & 9 & 7 \\ \hline \multirow{4}{*}{Mean \(\delta\) power at F4} & D & 1 & 16 & 12 & 3 & 9 & 17 \\  & F & 1 & 8 & 20 & 3 & 6 & 20 \\  & H & 3 & 14 & 12 & 3 & 15 & 11 \\  & I & 3 & 8 & 18 & 12 & 8 & 9 \\ \hline \multirow{4}{*}{Mean \(\delta\) power at F3 and F4} & D & 1 & 7 & 21 & 9 & 14 & 6 \\  & F & 5 & 13 & 11 & 7 & 10 & 12 \\  & H & 7 & 8 & 14 & 9 & 3 & 17 \\  & I & 3 & 11 & 15 & 11 & 7 & 11 \\ \hline \multirow{4}{*}{Mean \(\alpha\) power at F3} & D & 3 & 9 & 17 & 6 & 7 & 16 \\  & F & 2 & 9 & 17 & 3 & 8 & 18 \\  & H & 2 & 10 & 17 & 9 & 4 & 16 \\  & I & 1 & 13 & 15 & 8 & 9 & 12 \\ \hline \multirow{4}{*}{Mean \(\alpha\) power at F4} & D & 1 & 8 & 20 & 5 & 6 & 18 \\  & F & 1 & 5 & 13 & 8 & 8 & 13 \\  & H & 1 & 13 & 15 & 8 & 13 & 8 \\  & I & 1 & 7 & 21 & 3 & 5 & 11 \\ \hline \multirow{4}{*}{Mean \(\alpha\) power at F3 and F4} & D & 1 & 13 & 15 & 6 & 6 & 17 \\  & F & 2 & 12 & 15 & 5 & 10 & 14 \\  & H & 2 & 5 & 22 & 8 & 9 & 12 \\  & I & 3 & 7 & 19 & 4 & 6 & 19 \\ \hline \multirow{4}{*}{FAA at FP1-FP2} & D & 3 & 21 & 5 & 10 & 14 & 5 \\  & F & 4 & 7 & 18 & 15 & 9 & 5 \\ \cline{1-1}  & H & 16 & 11 & 2 & 9 & 7 & 13 \\ \cline{1-1}  & I & 18 & 3 & 8 & 6 & 4 & 19 \\ \hline \multirow{4}{*}{FAA at F3-F4} & D & 4 & 12 & 13 & 8 & 14 & 7 \\ \cline{1-1}  & F & 5 & 12 & 12 & 8 & 12 & 9 \\ \cline{1-1}  & H & 4 & 11 & 14 & 10 & 8 & 11 \\ \cline{1-1}  & I & 14 & 2 & 13 & 21 & 4 & 4 \\ \hline \end{tabular}
\end{table} 
Table 4: A summarization of the clustering results for each EEG feature used in every subtopic. From this clustering result, the SI level for each student was monitored by comparing SI levels classified from two different approaches. Figure 7a shows that the k-means and DBSCAN were classified differently when a single electrode was used; however, both algorithms showed an agreement in the combination of electrodes, as shown in Figure 7b. To verify that the proposed framework was simple and of low complexity, the processing times of the applied clustering was calculated. All the runtime experiments were checked on a PC with an AMD\({}^{\text{TM}}\) Ryzen 7 processor with 2.90 GHz and 8 GB RAM. Table 5 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**EEG Features**} & \multirow{2}{*}{**Subtopics**} & \multicolumn{4}{c}{**k-Means**} & \multicolumn{4}{c}{**DBSCAN**} \\ \cline{3-8}  & & **Low SI** & **Medium SI** & **High SI** & **Low SI** & **Medium SI** & **High SI** \\ \hline \multirow{4}{*}{FAA at Combined Pairs} & D & 7 & 9 & 13 & 12 & 10 & 8 \\  & F & 8 & 15 & 6 & 21 & 4 & 4 \\ \cline{1-1}  & H & 17 & 9 & 3 & 20 & 5 & 4 \\ \cline{1-1}  & I & 12 & 16 & 1 & 20 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 6: Example of DBSCAN clustering results for FAA metrics at the following electrode: (**a**) FP1-FP2, (**b**) F3-F4, and (**c**) combination of channel prefrontal and frontal. shows the average speed of this clustering for nine EEG features selected across subtopics. The average time taken for k-means algorithm was about 1.24 CPU sec for the entire dataset. For the DBSCAN, the average test was about 1.73 CPU sec. Therefore, the proposed framework allowed SI monitoring throughout the learning session easily and quickly, which was not feasible with the questionnaire method. Besides, the data could be processed from multiple electrode channels.