Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data

Abstract

The main contribution of this study was the concurrent application of EEG and eye tracking techniques during n-back tasks as part of the methodology for addressing the problem of mental workload classification through machine learning algorithms. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The results indicating the relationship between EEG and eye tracking measures and mental workload are consistent with previous research. Regarding the four-class classification task, mental workload level could be predicted with 76.59% accuracy using 34 selected features. This study makes a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.