# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 2 Related Work  
The majority of methods used to measure mental workload are based on theories and follow a deductive approach. This approach begins with a hypothesis or a set of hypotheses which are derived from existing knowledge and theories. The next step is to measure and quantify the factors that are believed to influence mental workload. These factors are then aggregated and used to make a final inference, typically in the form of an index or score. However, it is also possible to use inductive research methodologies in the field of mental workload. This involves starting with the measurement and quantification of the factors that are believed to influence mental workload, and then using data-driven techniques to create computational models that can fit human performance. Machine learning is essential in measuring mental workload due to the complex nature of mental workload itself, the presence of ambiguous and uncertain non-linear factors that influence this concept, and the challenges in aggregating these factors and developing computational models. However, there has been limited progress in applying machine learning techniques to automatically construct mental workload models that incorporate subjective measures. In contrast to other neuroimaging techniques, EEG exhibits a relatively high temporal resolution. It offers a real-time and unbiased assessment, and its ease of application is attributed to its portable device. Moreover, EEG is considered a safe method for long-term and repeated use due to its non-invasive nature. Notably, it does not necessitate the presence of a skilled operator or involve any medical procedures [12]. EEG indices have demonstrated a remarkable sensitivity to fluctuations in brain activity. Several studies have applied machine learning algorithms using eye tracking and EEG data. In the research conducted by Liu et al. [13], individuals were instructed to perform an n-back memory task in order to assess the workload levels of 21 participants across three distinct scenarios. Simultaneously, the participants' EEG; fNIRS data; and additional physiological measurements, including heart rate, respiratory rate, and heart rate variability, were recorded. The findings of the study unveiled that the combination of EEG and fNIRS techniques yielded more precise outcomes compared to their individual usage, with an accuracy rate of 65%. Jusas and Samuvel [14] conducted a study involving nine individuals, where they employed four distinct motor imagery tasks. These tasks required participants to engage in mental processes where they practiced or simulated specific actions. The study solely utilized EEG physiological data, and the highest classification performance achieved was 64% through the implementation of linear discriminant analysis. In their research on seven individuals, Yin and Zhang [15] performed a prediction model for mental workload using binary classification. The study concluded with a comparison of the model's estimator accuracy performance, which achieved a remarkable 85.7%, against traditional classifiers such as support vector machines and nearest neighbors. The findings demonstrated the superiority of this method over static classifiers. Pei et al. [16] conducted a study involving seven individuals, where they achieved an accuracy performance of 84.3% using the RF algorithm. They established a model by utilizing various EEG attributes obtained from a 64-channel EEG device during a simulation flight mission. Furthermore, the classification performance of the three-level model, which solely utilized band power variables, reached 75.9%. In a study conducted by Wang et al. [17], the hierarchical bayes model's cross-subject classifier was trained and tested using EEG data from a cohort of eight participants. The findings of the research indicate that the performance of the cross-subject classifier is similar to that of individual-specific classifiers. In the study conducted by Kaczorowska et al.