SEED: A Framework for Stress Estimation Using Emotiv Devices

Abstract

Nowadays, industrialization and urbanization have led to increasing stress levels in many countries. Primarily, it starts having an impact on vulnerable populations, e.g., college students and physicians. While previous works have reported an alarmingly high rate of stress in students, little is known about whether there are stress-related brain activity changes. This study aims to examine the effects of a realistic stressor (examination) on students’ stress levels and brain activities. We selected a cohort of 16 college students to participate in this study. Each subject was invited to the laboratory twice (pre-exam and in-exam) for completing a package of questionnaires. At the same time, we measure their brain physiological activity by using a portable EEG recorder. Our data reveal that examination- induced stress is associated with changes in the activity of the occipital and parietal-temporal areas in the brain. We construct a machine learning framework to estimate each student’s stress level using features extracted from brain activities and a classification method. Finally, our study underlines the necessity of applying stress intervention to create an optimal learning environment for students.