# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 4 Discussion
For the B&H comparison and the class separation level 3-7, an accuracy rate of 96.03% (CCI) is obtained with the RC classifier and Seg V according to the unbalanced data results. CCI, MCC values, and F1 scores (%) for the comparisons of VAD space for Segment V and the 4-6 classification are given in Figures 13 and 14 (data-balanced version) for Random Forest, SMO, IBk, FURIA, and Random Committee classifiers. CCI (%) values generally gave close results to F1 scores (%); however, MCC values (%) gave relatively lower results, which can be seen in Figures 13 and 14 and in Tables 3 and 4. Studies in the literature on emotion evaluation show that emotional states can be evaluated with discrete or dimensional emotion models. Dimensional models include Valence Arousal (VA) or Valence Arousal and Dominance (VAD) spaces. A comparison of our study with some studies that have a dominance dimension is given in Table 5. We can see that most of the studies in the literature use only VA due to the ease of differentiation; however, very few studies use VAD space like ours. Similarly, glancing over the studies in the literature for the segmentation of emotion analysis with handcrafted features, we also see that VA space is used. Considering those two issues, we especially wanted to focus on VAD space to compare and evaluate the emotional states and segmentations of EEG signals and segment comparisons in terms of emotion analysis. Based on this, we think this study will contribute to the literature. In the literature, studies generally do not use so many comparison factors. In this context, the present study is critical because it offers an overall comprehensive and analytical approach with several different steps and complete comparisons applied for various factors for comparisons, such as binary cross-comparisons and evaluations of eight emotional states; comparisons of selected segments; comparisons of class separation levels (5, 4-6, and 3-7); and comparisons of unbalanced and balanced data with the SMOTE method. Giving detailed comparison results with the same or similar conditions is crucial. For instance, for these segmentation studies and binary comparisons of eight emotional states as well as other comparison factors, all the conditions are the same, including the same dataset, the same preprocessed EEG signal, the same channels, the same feature sets, the same class separations, the same classifiers, etc. As such, the results could be considered to show the differences in a more convenient and reliable way. In our study, we have obtained high-accuracy results that can be seen directly in the spider web diagrams in the second part of this study and in Tables 3 and 4. The best accuracy result in this study for unbalanced data is for the C&E comparison (Seg III and 4.5-5.5 class separation), for which an accuracy rate of 98.94% CCI (F1: 98.9% and MCC: 97.9%) is obtained with the IBR classifier, which is greater than the compared studies from the literature that have a dominance dimension besides valence and arousal, as shown in Table 5.  
The overall analysis results conducted with VAD space show that accuracy values obtained from C&E comparisons were generally higher than all the other cross-comparisons for unbalanced data. From all the results in Figures 6 and 7 (data-balanced version) in the first part of this study, accuracy rates generally seem to decrease gradually in the order of C&E, D&F, B&H, and A&G comparisons, respectively, for 4-6 class separations. Besides these results, if we look at all the results and Figure 9a, Figure 10a, Figure 11a, and Figure 12a in the second part of this study, the accuracy rates generally seem to gradually decrease in the order of C&E, B&H, D&F, and A&G comparisons, respectively. Here, C&E and B&H seem similar at a glance; however, we should remember that C&E comparisons do not include the 3-7 class separation. As such, a middle-sized class separation (4-6) in B&H and other comparisons correspond to a thick-sized class separation (4-6) in C&E, as seen in Figure 12a,b.