# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
###### Abstract  
The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis using Physiological Signals (DEAP). EEG signals are preprocessed with resampling and a median filter. We extracted Power Spectral Density (PSD) features from the alpha, beta, delta, and theta bands of the preprocessed EEG. Instances were labeled based on the valence and arousal values provided in the DEAP dataset in response to the presented videos. Four machine learning algorithms, namely Naive Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), and Sequential Minimal Optimization (SMO) classifiers, were employed to differentiate between stressed and relaxed states using a 10-fold cross-validation technique. The SMO classifier achieved the highest accuracy of 95.65%. Additionally, statistically significant variations in the alpha band using \(t\)-tests suggest that the DEAP dataset video clips can effectively induce stress and relaxation conditions in participants. electroencephalography; stress assessment; valence arousal model; classification; alpha activity +
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adults are highly engaged with social networks such as WhatsApp, Facebook, Twitter, YouTube, Instagram, LinkedIn, and Snapchat, which are significant multimedia content sources [9; 10]. Surveys indicate that young adults are more associated with Internet use [11]. Previous studies have identified multimedia use as a critical risk factor in modern society [12; 13]. It poses significant risks to mental health by inducing emotions that can disrupt mental equilibrium [14]. Multimedia content can elicit emotions that positively or negatively impact brain activity, thus affecting their lifestyle directly or indirectly [15]. Persistent feelings of fear, distress, and nervousness can negatively affect brain activity and induce stress [16]. Functional neuroimaging research indicates that addiction to multimedia damages the frontal-basal pathways, leading to an increased frequency of unwanted actions such as stress, anxiety, and suicidal tendencies [17]. Various studies have shown that stress can be measured using brain signals [7; 18; 19; 20; 21; 22]. Brain-Computer Interface (BCI) is a technique that captures the brain signals responding to a provided stimulus. After examining the acquired signals, their features are extracted. Finally, classification is performed using machine learning or deep learning classifiers. Stress significantly impacts the health, lifestyle, and overall well-being of an individual [23]. Neuroscience reveals that the brain is the primary target of stress. For a long time, EEG has been studied to distinguish between different emotions. Observing human responses to problems through physiological signals enhances our understanding of their emotions [24]. Human emotions are the most important and complex feature. They are the source of information about people's experiences. Emotional states are described by the valence-arousal model, which is a foundational concept in psychology [1]. Valence refers to the pleasantness or unpleasantness of an emotion. Positive valence emotions are considered enjoyable (e.g., joy, content), while negative valence emotions are unpleasant (e.g., anger, sadness), and arousal captures the level of intensity associated with emotion. High-arousal emotions are characterized by increased alertness and physiological responses (e.g., excitement, anxiety), while low-arousal emotions are associated with calmness and relaxation. This model proves valuable in understanding human stress. Stressful situations typically evoke emotions with negative valence and high arousal. Emotion consists of three components: subjective experience, physiological response, and expressive response [25]. Strong emotions like anger and fear, as well as mental stress, are linked to abnormal heart rhythms [26].