## 4 Discussion and Future Directions
### Answering Research Question Two  
\"What existing major research gaps are yet to be addressed and what are their associated research problems\"? The following major research gaps were revealed that had not yet been addressed. Research gap 01--Bias-Variance Dilemma in existing confusion emotion models: The bias-variance dilemma is a conflict between model bias and variance where model variance  
Figure 5: Averaged prediction accuracies of popular machine learning algorithms used in previous literature. Figure 6: Averaged prediction accuracies of popular deep learning algorithms used in previous literature. increases when bias is being reduced, and vice versa [49; 50]. This conflict prevents the model's ability to learn beyond the training dataset, thus reducing the reliability and prediction accuracy of the model [51]. This dilemma causes the model either to overfit (provides accurate predictions for training data but not for new data) or underfit (generates erroneous predictions for both new and unseen data). The authors observed that works by [5; 20; 21; 22; 26] developed models that had a prediction accuracy within the range of 50% and [20; 24; 27] had models that had prediction accuracies between 90% and 100%. The high variation in the reported ML prediction accuracies concerned the author as to why it might happen. It was assumed that this could be due to the bias-variance dilemma. The assumption was supported by the concluding remarks of [27], which stated that achieving 100% accuracy levels might be due to overfitting. Another solid support for the above assumption was provided by [21]. Their initial model developed using XGBoost had an accuracy level of 52%. In order to boost performance, they optimized the model using Tree Parzan Estimator (TPE) and boosted the performance to 58%. However, the authors did not mention the bias-variance dilemma. However, the theory on the bias-variance dilemma states that model optimization using TPE or hyperparameter tuning algorithms reduces this dilemma resulting in a boost in the prediction accuracy of the model if underfitted [50]. A critical root cause for the bias-variance dilemma could be the quality of the dataset [47]. If the dataset consists of noise and is not preprocessed sufficiently, this would lead to the creation of this dilemma [48]. The above reason could be the reason for the dilemma experienced by the models in [52]. Both studies utilized the dataset of [PERSON] et al. [5]. It did not describe feature preprocessing in a detailed manner. Since EEG recordings are highly susceptible to noises and artifacts, a proper preprocessing pipeline is required for better accuracy of the EEG recordings. Hence, the quality of the EEG data samples in the dataset is questionable, and it is suspected that this lower quality led to the creation of the bias-variance dilemma. An associated research problem for this could be that by not including an examination of the bias-variance dilemma in the machine learning development pipeline, the developed models would become affected by the dilemma, therefore causing the model to either overfit or underfit, and thus making the predictions for new and unseen data unreliable. The authors only focused on the bias-variance dilemma in machine learning algorithms. Deep learning algorithms were given less focus as the authors intended to bridge the research gaps that were in machine learning algorithms in order to achieve the aim of the study. Research gap 02--Limited research on the development of cost-sensitive confusion emotion models: It was revealed that models developed with DL algorithms had better accuracy than ML algorithms (Tables 7 and 9). However, DL models are much more expensive than ML models as they are computationally expensive [53] and heavily dependent on computing power, requiring powerful hardware for processing Ga. Although the performance of DL models is higher, their operational costs mean that it is unaffordable for end users to utilize DL models in their day-to-day activities [54].