# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 4 Discussion  
In the literature, we generally see studies comparing emotions with binary classifications, such as high and low levels of valence and high and low levels of arousal, which divide the VA space into two pieces. Some studies also consider two-dimensional VA space, which divides the space into four pieces. There are many studies on emotion analysis in the literature. However, these studies generally consider VA space. Little work seems to focus on the 3D space of valence, arousal, and dominance, which divides the VAD space into eight pieces weighing the dominance  
Figure 14: CCI, MCC values, and F1 scores (%) for the comparisons of VAD space (data balanced with SMOTE) (Segment V, 4–6 classification). Figure 13: CCI, MCC values, and F1 scores (%) for comparisons of VAD space (Segment V, 4–6 classification). besides valence and arousal dimensions. Therefore, in this study, we especially wanted to focus on 3D VAD space to classify, compare, and evaluate emotions. Similar to these comparisons of segmentations for emotion analysis using handcrafted feature sets that the researcher manually considers, we also see valence and arousal dimensions in the literature. In this context, we also wanted to focus on 3D VAD space to study the segmentations of EEG signals for emotion analysis. There are few studies for emotion analysis using the SMOTE method. Because of the unbalanced classes for our comparisons, we also wanted to use this filter (oversampling of the minority class) to perform data balancing besides the unfiltered data. This study is divided into two parts: a segmentation pre-study and cross-comparisons of 3D VAD emotions, segments, and classes. We used all valence, arousal, and dominance coordinates together for two class cross-comparisons in both studies. In the first part of this study, the 30, 20, 15, 10, 5, 3, 2, and 1 s non-overlapping windowing method is used for the segmentation of EEG signals, which correspond to Segments I, II, III, IV, V, VI, VII, and VIII, respectively (Table 2). The % CCI values are calculated for VAD space binary emotion comparisons (A&G, B&H, C&E, and D&F) for evaluating the segmentation effect. Eight segments (Segments I, II, III, IV, V, VI, VII, and VIII) are given in Figures 6 and 7 (data-balanced version). Only three classifiers (SMO, IBk, and Random Forest) are used for the classification in this part of this study. Similarly, this part of this study considers only the 4-6 class separation level. [PERSON] et al. [49] studied a novel method (InvBase) for emotion classification after baseline removal. The results show that baseline removal gave higher accuracy with simple features, requiring less computation time. They also concluded in their study that the emotion features were dominant in a range of 1 s to 12 s time slots. Similarly to that study, we have subtracted the first three seconds of the preprocessed EEG data from the signal, and the remaining 60 s signal is used in this study. From Figure 6, it can be seen that segmentation increases accuracy. The results show that accuracies generally grow to some extent as the segmentation increases; however, after some increase, they give similar results or even begin to decrease at the end parts for every three classifiers (SMO, IBk, and Random Forest). Segments III, IV, and V, which correspond to 15, 10, and 5 s windowing, are chosen to use in the second sub-study, which has more complex comparisons with the class separation levels, classifiers, and data balancing. In the second part of this study, spider web diagrams compare the results for the binary cross-comparisons of 3D VAD emotions, segments, class separations, classifiers, and data balancing. Lines are drawn to ensure a more accessible and precise understanding of the results and the comparisons. Data points on the lines indicate the classifiers' results (% accuracies). This part of this study uses SMO, IBk, FURTIA, Random Forest, Optimized Forest, Bagging, Random Committee, and Random Subspace classifiers.