EEG and fNIRS Analysis Using Machine Learning to Determine Stress Levels

Abstract

Researchers are constantly striving to determine effective ways to detect and diagnose stress in patients as early as possible to prevent them from experiencing serious health consequences and complications. This study analyzed the subject's stress levels using EEG and fNIRS while they played a computer game that tested their ability to make accurate yet quick decisions. Trails were conducted to create a machine learning model to determine the varying levels of stress experienced by each subject. Blood oxygen levels, heart rate, and body temperature were also monitored and recorded. The EEG and fNIRS data was processed, tested, and verified using MATLAB to create the machine learning model. The data indicate that stress levels increased while the subject's quick decision-making skills were tested, and amplified as the difficulty of the computer game increased. The model accurately predicted and classified the level of stress an individual was under during each trial.