# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 2 Related Work
[18], a group of 29 individuals participated in a number symbol matching test, which consisted of three levels of difficulty. The logistic regression and random forest algorithms were employed to determine the accuracy of the 20-variable model, which was constructed using eye tracking data. The resulting accuracy was found to be 95%. In the research conducted by Sassaroli et al. [19], the fNIRS technique was utilized on three individuals to differentiate various levels of mental workload by analyzing the hemodynamic changes in the prefrontal cortex. To determine the mental workload levels, the K nearest neighbor algorithm was employed,considering the amplitude of oxyhemoglobin and deoxyhemoglobin (k = 3). The study revealed classification success rates ranging from 44% to 72%. Grimes et al. [12] conducted an early study that classified mental workload through n-back tasks, achieving a commendable level of accuracy. By employing the Naive Bayes algorithm, they determined that the two-difficulty level model had an accuracy rate of 99%, while the four-difficulty level model achieved an accuracy rate of 88%. These measurements were obtained using a 32-channel EEG device and involved a sample of eight individuals. Wu et al. [20] conducted a study on prediction using the eye tracking method. The study involved 32 non-experts and seven experts who were tasked with performing operational tasks at two varying difficulty levels. The researchers used pupil diameter, blink rate, focusing speed, and saccade speed as inputs in their model, while the NASA-TLX score served as the output. The results showed that the accuracy of the model, obtained through the use of ANN, was 97% when tested with the data. A study conducted on the cognitive burden of automobile operators demonstrated that a feed-forward neural network classifier achieved a prediction accuracy of over 90% in determining levels of alertness, drowsiness, and sleep states [21]. In another study, a group of 28 individuals engaged in a computer-based flight mission. During this study, the brain signals of the participants were recorded using a 32-channel EEG device, while eye data were collected using an eye-tracking device operating at a frequency of 256 Hz [22]. The study employed various algorithms such as linear regression, support vector machines, random forests, and artificial neural networks. Among these algorithms, the model that incorporated both eye tracking and EEG variables yielded the highest accuracy rate of 54.2% in the three-category classification task. SVM has been utilized in various studies within the literature to obtain mental workload classification results using eye tracking or EEG data [23; 24; 25; 26; 27; 28; 29; 30]. Upon reviewing the pertinent literature, it became evident that both EEG and eye tracking techniques have significant implications in the analysis of mental workload. Nevertheless, it was observed that there exists incongruity in the findings regarding the correlation between the variables in both methods and mental workload. Notably, certain studies have demonstrated an augmentation of eye-related variables with increased mental workload, while others have shown a decline. The current study distinguishes itself from other studies by incorporating machine learning algorithms that have been developed in the past decade (extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM)), but have not been previously utilized in studies aiming to estimate mental workload using EEG and eye tracking techniques.