# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 1 Introduction
The dominance dimension ranges from submissive/without control to dominant/in control and empowered [12; 13]. The dimensional emotion model is preferred due to its international identification and acceptance [11]. The 3D Emotion Space Model can be seen in Figure 1, which shows a three-dimensional (valence, arousal, and dominance) space that occurs using the data in [14], taking the pleasure plane as valence. [PERSON] and [PERSON] [12] proposed a 3D emotion model on VAD space for describing various emotions, including fun, joy, cheerfulness, melancholy, love, mellow, shock, etc. In general, 3D space is more sufficient and accurate for representing all emotions than 2D space. Many studies on emotion analysis appear in the literature; however, these studies generally focus on 2D valence and arousal space. Little work focuses on 3D valence, arousal, and dominance space. In the present study, we especially wanted to study 3D space to classify emotions and to compare emotions, segmentations, and classes. EEG is used in this study to conduct space classifications and comparisons. [PERSON] and [PERSON] [12] obtained a classification accuracy (%) of 63.47 (valence), 69.62 (arousal), and 63.57 (dominance) for a tree class classification (low, medium, and high) with the DEAP database besides their own experiment. [PERSON] et al. [15] used a feature-based wavelet and R-ELM (extreme learning machine) classifier (with physiological signals in the DEAP database). They obtained classification rates of 73.43% (V), 72.65% (A), and 69.3% (D) for the two classes. They obtained the best average classification accuracy of 53% for four class classifications in the VA quadrant. [PERSON] et al. [16] studied multivariate synchro squeezing transform on EEG signals from the DEAP database for feature extraction to classify emotions. They used a 2D VA model for four emotional states and obtained the best accuracy of 79.1%. They used a 3D VAD model for eight emotional states with an SVM classifier and obtained the highest accuracy of 93%. Accuracies are higher for eight emotions than in the four emotion model. The classification and comparison of emotions could contribute to many areas, such as intelligent health technologies, brain-computer interfaces (BCIs), psychology, patient robots, humanoid robot technologies, virtual reality, holograms, simulation systems, game technologies, interactive education systems, smart homes, workplaces, driver systems, etc. This study contributes to the literature with its comparisons and comprehensive analytical approach implemented with two sub-studies: first, a segmentation pre-study, and second, an emotion comparison and evaluation study, including comparisons of selected segments, class separation levels, and data balancing. Our study's general results show that accuracies obtained from balanced datasets mostly seem to outperform those obtained from unbalanced datasets. The accuracies of unbalanced data in the figures of the second part of this study generally seem to increase in the order of A&G, D&F, B&H, and C&E comparisons. We have obtained high accuracy results that can be seen directly in the spider web diagrams in the second part of this study. 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), and 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 which has a dominance dimension besides valence and arousal. This study is divided into two sub-studies. In the first part of this study, the comparison of the VAD emotions via various segmentations of EEG signals is objected to. Segments I-VIII are obtained by applying the non-overlapping windowing method. In the second part of this study, Segments III, IV, and V are chosen, and the two class cross-comparisons of the VAD emotions, segments, and class levels are objected to.