Evaluation of Academic Stress Employing Network and Time Series Analysis on EEG Data

Abstract

Stress management during examinations is an issue perpetually concerning students at all levels of education. This research endeavor focuses not merely on the plethora of factors associated with stress during examinations but rather focuses primarily on specific emotions developed during written and oral exams, introducing an original method, based on electroencephalograph (EEG) data. In an attempt to address the role of emotions in stress management, we organized an experiment on a sample of 30 postgraduate students, administering the Trier research protocol. The validated Emotiv Epoc+ device was used, while acquired the data were processed using a combined methodology of network and time series analysis applied in three (3) steps. Initially a descriptive analysis was performed based on the extracted frequencies, while in the second phase a network analysis was conducted to understand the extend of coordination between measured emotions (stress, engagement, interest, relaxation, excitement, and focus), using the tools of centrality. Finally, time series analysis was employed to indicate the level of auto- and cross-correlation between stress and other emotions. Our findings show that (eu)stress plays an imperative role in examination success, as long as it normally fluctuates within middle-range values (on a scale 0–100), and is being restrained by other feelings, such as interest, excitement, and focus