## 4 Discussion and Future Directions
### Answering Research Question One  
\"What is the present context of EEG-based confusion emotion recognition systems?\"  
It was observed that this research domain is still limited and growing at a slow pace due to the smaller number of publications (only seventeen) that were identified till the present. Only five research works [5, 16, 19, 24, 25] were dedicated to creating datasets and of the five, only one dataset [5] is still available publicly. Other publications comprised research works that built confusion emotion classifiers using [PERSON] et al. 's [5] dataset (Figures 3 and 4). A crucial question lies concerning the slow pace of this research domain. Although deriving a specific and conclusive answer cannot be reached, it was assumed that this is occurring due to the following reasons:  
1. [label=()]
2. Limited creation of datasets that collect EEG recordings by conducting human experiments. This is due to the following reasons: (i) difficulty in stimulus design for inducing confusion within an educational setting; (ii) unavailability of standardized psychological procedures for deliberate induction of confusion through learning activities; and (iii) practical problems and challenges relating to EEG acquisition remain, such as the need for the necessary technological infrastructure, legal approvals, time, and financial commitment for the acquisition of a significant number of EEG recordings from participants [46]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Gap** & **Associated Research Problem** & **Suggested Research Works for Future** \\ \hline Bias-Variance Dilemma in existing Confusion & Model having hindered performance. The model’s prediction performance is unreliable for new and unseen data. & Metadata such as demographics play an important role in human emotional states [48]. Hence, they can be considered as inputs for model training. Use advanced EEG feature preprocessing techniques to remove noise in EEG data. Use advanced feature selection techniques to identify the optimal EEG features that can predict confusion emotion. & Metadata such as demographics play an important role in human emotional states [48]. Hence, they can be considered as inputs for model training. Use advanced EEG feature preprocessing techniques to remove noise in EEG data. Use advanced feature selection techniques to identify the optimal EEG features that can predict confusion emotion. & Metadata such as demographics play an important role in human emotional states [48]. Hence, they can be considered as inputs for model training. Use advanced EEG feature preprocessing techniques to remove noise in EEG data. Use advanced feature selection techniques to identify the optimal EEG features that can predict consistent emotion. \\ \hline Limited research on the development of cost-sensitive confusion emotion models & Good-performing models are unaffordable for end users. & Research on constructing cost-sensitive models having a tradeoff between cost and model performance. \\ \hline Limited datasets & Unable to test developed models on independent datasets for their prediction reliability on new and unseen data. & Conduct human experiments to collect EEG recordings and create datasets. \\ \hline Low Generalizability in existing confusion emotion models & Models would not perform well if new data having different levels of complexities were provided. & Collect EEG recordings among a broader demographic population of learners. Test the models on EEG recordings from real-world populations rather than recordings obtained from a laboratory experiment setup. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the identified research gaps and the suggested recommendations. 2. Limited publicly accessible datasets: datasets that are publicly accessible help overcome obstacles related to EEG acquisition. The unavailability of datasets may demotivate those researchers who are interested in conducting research in this domain yet cannot overcome the above obstacles.