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.

The NASA TLX is a survey-based method widely used to evaluate cognitive workload across six specific dimensions related to an employee’s tasks. While academic research recognizes that these dimensions may have unequal contributions to mental workload, they are often weighted using multi-criteria decision-making techniques. However, prior studies have not investigated possible relationships between the dimensions. The main objective of this paper is to explore the interconnections and dependencies among these dimensions. This study distinguishes itself by employing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to clarify these relationships and dependencies, and to examine how interactions between dimensions shift under different threshold conditions. The research includes three distinct impact diagrams tailored for individuals performing tasks with varying levels of cognitive workload. By considering the interdependencies among the NASA TLX dimensions, this study offers a significant advancement in the field, as these interrelationships could greatly influence the derived weights. This theoretical contribution has the potential to be groundbreaking in the realm of survey-based mental workload assessment techniques, such as the NASA TLX.

The objective of this study was to analyze the mental workload using EEG and eye tracking data and classify it using machine learning algorithms. The machine learning model was developed based on the simultaneous recording of eye tracking and EEG measurements during the experimental process. 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 findings revealed that as the task difficulty increased, there was an increase in the diameter of both the right and left pupils, the number of fixations, the number and duration of saccades, and the number and duration of blinks. Conversely, variables related to fixation duration decreased. The EEG results indicated that theta power in the prefrontal, frontal, and front central regions increased with task difficulty. Additionally, alpha power increased in the frontal regions but decreased in the temporal, parietal, and occipital regions as the task became more challenging. Furthermore, low beta power significantly decreased in almost all brain regions as the task difficulty increased. In terms of the four-class classification problem, the mental workload level can be predicted with an accuracy rate of 76.59% using 34 selected features. This study has made a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.

Purpose: The primary objective of this study was to develop machine learning algorithms for classifying mental workload using eye tracking data.Theory and Methods: This study proposed machine learning algorithms for classifying mental workload using eye tracking data. Four participants performed the N-Back memory task and National Aeronautics and Space Administration -task load index (NASA-TLX) to induce different levels of mental workload. In total, 792 samples were collected from four participants. Twenty-seven eye tracking metrics were selected as independent variables. One output variable reflecting the difficulty level of N-Back memory was classified.Results: As a result of these experiments, it was revealed that almost all eye tracking metrics considered in this study were significantly correlated to both weighted NASA-TLX total score and N-Back memory task difficulty level. As the task difficulty increased, pupil diameter, number of saccades, number of blinks, and blink duration increased, while fixation duration decreased. The results obtained for the two classes of classification problem reached the accuracy of 68% with 14 eye-tracking features due to problem complexity. The results obtained for the two classes of classification problem reached the accuracy of 84% with 27 eye-tracking features as input and the LightGBM algorithm. To determine the degree to which the input variables contribute to the determination of the output variable, a sensitivity analysis was conducted using the gradient boosting machines (GBM) algorithm. The left eye pupil diameter was found to be the most effective metric in the classification of the task difficulty level. The results from the analysis indicate that eye tracking metrics play an important role in the classification of mental workload.Conclusion: The results from the analysis indicate that eye tracking metrics play an important role in the classification of mental workload. Among the classification models developed with machine learning algorithms, the most successful results were obtained with tree-based algorithms. Algorithms such as GBM, LightGBM, XGBoost, which have been developed recently and therefore are not frequently used in the literature on this subject, have achieved better results compared to others.