# 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. EEG; eye tracking; mental workload; machine learning; neuroergonomics; prediction; NASA-TLX; N-back +
Footnote †: journal: Medical  
setup, precise and sufficient data collection, and the ability to accommodate slight head movements. Specifically, studies have demonstrated a correlation between pupil response, blink movement, and visual as well as auditory tasks [6; 7; 8; 9; 10]. Recent studies have focused on predicting mental workload, primarily approaching it as a classification problem with the objective of estimating the level of mental workload categorically. These studies differ in terms of the task type employed in the experiments, the techniques utilized to obtain input variables for the model, the number of predicted classes, and the algorithms employed to address the problem. Despite these efforts, developing a model that can accurately predict mental workload remains an unresolved issue in the existing literature. One of the primary objectives of this study is to investigate the correlation between physiological variables and mental workload, and compare it with the existing literature. Another crucial objective is to expand the scope of the problem, which is typically categorized into two or three classes in previous studies, by considering four classes. This will provide a more detailed understanding of workload levels and enable the development of a classification model with the highest possible accuracy. Furthermore, this study stands out due to its unique approach of simultaneously collecting eye tracking and EEG data from participants. Although synchronizing the data from both devices presents challenges during the experiment and analysis process, previous findings have shown that combining physiological methods, which are known to be influenced by fluctuations in mental workload, enhances the performance of the model. Hence, EEG and eye tracking methods were employed together in this study, which is a novel contribution to the literature as no previous studies have tested both n-back tasks while utilizing EEG and eye tracking methods simultaneously. In this study, we employed EEG and eye tracking as physiological techniques, which were further complemented by the subjective evaluation scale NASA-TLX [11]. Additionally, the raw EEG data were processed using the MATLAB software (version R2021b)-based EEGLAB tool, which not only facilitates the visualization and interpretation of statistical analysis results, but also serves as an exemplary application of this tool in the literature. In summary, this study has three main objectives:  
1. Analyzing the relationship between EEG and eye-related variables with task difficulty level and subjective workload evaluation, and comparing them with similar studies in the literature. 2. Predicting task difficulty level, which is evaluated as the mental workload, using EEG and eye tracking data. 3. Incorporating machine learning algorithms and utilizing the EEGLAB tool to enhance the analysis and interpretation of the results, thereby contributing to the existing literature.