Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features

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 Naïve 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.