# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 2 Literature  
Human stress research is one of the growing areas of research in the domain of neural engineering and biomedical science. Stress assessment includes studying different methodologies and patterns to analyze human stress, such as mental, physical, and emotional [7]. The stressor may vary from person to person due to the different trigger points of individuals. When human beings confront a potentially threatening situation, their physiological changes occur in the brain and nervous system. This potentially threatening situation acts like a stimulus that induces a cascade of processes in the body to adapt to the incident [34]. There is a rapid increase in internet-related applications and social media sites. Consequently, human beings' exposure to multimedia content is rapidly increasing [19]. So, it has become more crucial to assess stress in the response to multimedia content, i.e., videos. Various methods are proposed to make an automatic distinction between stress and relaxation using facial expression, body language, skin conductance, and brain activity. Stress assessment through heart rate is obtained via functional near-infrared spectroscopy. Emotions are affected by external influences along with internal influences. They are evoked by stimuli such as images and videos. EEG-based emotion recognition studies have been focused on the valence arousal model [1; 35] by analyzing the facial expression and heart rate [15]. In research literature, two perspectives are usually used to understand emotions. The first is a discrete model involving the basic evolutionary features such as happiness, joy, sadness, fear, disgust, and surprise. The other perspective for understanding emotions is known as a dimensional model. Recent studies showed that emotions could be determined from emotional models such as [PERSON]'s continuous emotional model, which includes the valence and arousal values; [PERSON]'s emotional power model, which uses arousal and pleasure; and the Mehrabian pleasure, arousal, and dominance emotional model [1]. Valence and arousal values range between \(1\) and \(-1\). Stress and anxiety can be predicted by using valence and arousal values [18]. It was found that invoking different emotions can affect stress [36]. A study uses arousal-based electrodermal activity to determine emotional stress by using the genetic algorithm on the DEAP dataset [20]. In this study, subjects undergo relaxation, arousal, and a stressful environment. The results showed that the mental state is affected by arousal and a stressful environment. Some studies used passive methods for stress induction, and some used cognitive tasks, psychosocial stress, physical discomfort, or naturalistic stressors. In [37] the DEAP dataset [38], EEG signals are recorded while watching the videos. Stress and relaxed conditions are determined from the valence and arousal values. Arousal and valence values range from 0 to 9. These values are mapped in the equations to determine stress and relaxed conditions for the participants. Figure 1 depicts the circumplex model of effect. It shows the categorization of emotions based on their pleasantness and activation levels. From this, we can see how stress is mapped to the unpleasant and arousal quadrant of the model. The Geneva Affective PicturE Database (GAPED) is a relatively large collection of images used to evoke various emotions [39; 40]. Each image in the collection was scored for valence and arousal. Negative images with high arousal ratings, such as violence against humans and animals, may trigger stress. This kind of stress is identified by fluctuations in heart rate, physiological stress markers, EEG, and subjective rating [41]. In [42], a preliminary study is conducted to classify arousal by comparing the efficiency of machine learning classifiers. DEAP dataset was used to determine participants' stress and relaxed conditions.