# Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features
## 5 Discussion  
### Presence of Interest  
In this section, the cluster performed by the k-means and DBSCAN algorithm will be discussed in detail. We have proposed the mean delta and alpha power at the frontal lobe as an attention reaction predictor for SI detection. Figure 4 indicates that the k-means was able to cluster the students into three groups completely when delta power was served as the input. Based on the results obtained, the students who had a lower mean delta power were clustered as high SI, while students with a higher mean delta power were clustered as medium, and the highest mean delta power were clustered as the low SI group. The results highlighted the scientific finding of an increase in delta power mainly in frontal leads in different tasks during cognitive processing [51]. These findings were corroborated with findings from the original database authors, where the grouping of the SI was finished using a questionnaire [38].  
Meanwhile, when the same features were used as the input for the DBSCAN clustering algorithm, they were misclassed in the student's SI groups detected (see Figure 5b). Based on the finding from [PERSON] [27], the high SI student should have the lower delta power at the frontal region when compared to the low SI student. However, two students that had a low delta power were misclassified as low SI group. For further performance evaluation, the class given by the clustering was retained as it was even though the class was mismatched.  
Both k-means and the DBSCAN algorithms could cluster the mean alpha power in three groups. The student with the highest alpha power represented the low SI group and vice versa. The suppression in alpha in the frontal lobe for high SI students indicated the tense state, compared to increases in alpha in the frontal lobe for low SI students who were more relaxed. These findings were consistent with an alpha inhibition hypothesis from [PERSON] [52] and [PERSON] [53], who proposed that an indicator of active neuronal processing regions was reflected by a small amplitude of alpha oscillation; however, large-amplitude alpha oscillations reflected the reduction and disconnection of task-irrelevant cortical areas.  
Aside from the mean delta and alpha power, we also proposed the FAA metrics as an effective reaction predictor for SI detection. The k-means clustering results using FAA metrics as input showed that the students could be clearly distinguished into three clusters. The k-means was working well in all electrode pairs. Students with more negative FAA metrics were clustered as having high SI, while students with more positive FAA metrics were clustered as having low SI. The results from DBSCAN clustering are shown in Figure 6. This figure indicated that the DBSCAN algorithm was unable to cluster the data properly. There were students with more negative asymmetry that were misclassified as low SI at channel pair of FP1-FP2 and F3-F4.  
The present study showed that changes in the frontal EEG alpha asymmetry depended on students' situational interest levels. Students with a low level of SI exhibited relatively high right-hemispheric activation (positive FAA metrics). By contrast, a high level of SI students showed relatively high left-hemispheric activation. Notably, students with high situational interest would have positive emotions, while low situational interest students had negative emotions. Realistically, when someone had a positive feeling on some topic, that they would show more interest to gather information about that topic. These statements were supported by a previous study from [PERSON] [54], who reported changes in FAA metrics recognized by EEG that could correspond with passionate or inspirational character attributes. Hence, the proposed method achieved a quantitative result, which was incorporating two important elements (i.e., attentional and effective reaction) in assessing situational interest.