EEG-Based Assessment of Stress Levels Using Time–Frequency Features and Machine Learning

Stress from everyday living and exam times can have a major impact on cognitive function. So, identifying the degree of stress can be crucial in choosing preventive measures. In order to categorize stress levels (binary and multi-class) using machine learning techniques, the Empirical Wavelet Transform was used in this study to decompose EEG signals into subbands. Accuracy, sensitivity, specificity, precision, and F-score measurements were used to evaluate the performance of machine learning algorithms. The findings demonstrated that binary classification yielded higher accuracy than other classification tasks, especially between low and high stress levels. After feature selection, the Random Forest classifier produced the best results, with an accuracy of 86.25%. The K-Nearest Neighbor algorithm produced the best accuracy of 66.67% in multi-class classification, demonstrating the greater challenge of differentiating stress levels. The most instructive features, as determined by statistical analysis, were associated with signal power, relative energy, and amplitude, which were mostly obtained from frontal and temporal EEG channels. This study demonstrates that stress levels can be effectively classified using a limited number of EEG channels and simple features, providing a practical approach for EEG-based stress assessment.