# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 5 Conclusions  
By combining EEG and eye tracking methods within an experimental context, along with the NASA-TLX scale and performance-based approaches, this study presented an innovative research design. The data were collected during n-back tasks. Through the simultaneous recording of EEG and eye data, this study successfully developed mental workload prediction models with the help of machine learning algorithms. The main contributions of this study are: (i) two distinct physiological techniques were employed concurrently alongside the n-back memory task; (ii) unlike other studies that focused on two- or three-class problems, we approached the prediction of mental workload level as a four-class problem; (iii) we successfully implemented machine learning algorithms that were developed at a later stage, and these particular algorithms demonstrated the highest classification performance; (iv) we have made a valuable contribution to the existing literature by exploring the correlation between EEG and eye-related variables in relation to task difficulty level and subjective workload assessment; and (v) we presented an application example to the literature by conducting the EEGLAB STUDY, which processed raw EEG data and analyzed the change in band power in different parts of the brain, under different task conditions. In future studies, the scope of the model can be expanded to encompass the classification of more demanding tasks associated with mental workload. Furthermore, the models can be evaluated by testing them with a diverse group of participants or by using data collected under varying task conditions. Despite the fact that the number of participants used in this study is enough to ensure the reliability of the analyses, it is important to consider increasing the number of participants to obtain more reliable data for the analysis. To deal with a large dataset, deep learning algorithms can be used to identify highly complex patterns in large datasets. Using the concepts and techniques covered in this paper, a real-time state monitoring system that takes into account participant emotions, weariness, stress, and other factors in addition to mental workload may be developed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors, Year** & **Number of** & **Number of Classes** & **Measurement** & **Method** & **Accuracy** \\ \hline Subasi [21] & 30 & 3 & EEG & ANN & 87–98\% \\ Grimes et al. [12] & 8 & 2–4 & EEG & NB & 88–99\% \\ Sassaroli et al. [19] & 3 & 3 & fNIRS & KNN (k = 3) & 44–72\% \\ Wang et al. [17] & 8 & 3 & EEG & ANN, NB & 30–84\% \\ Borys et al. [23] & 13 & 2-3 & EEG + eye tracking & DT, LDA, LR, SVM, KNN & 73–90\% \\ Liu et al. [13] & 21 & 3 & EEG + fNIRS & LDA, NB & 65\% \\ Yin and Zhang [15] & 7 & 2 & EEG & DL & 85.7\% \\ Le et al. [30] & 5 & 3 & fNIRS & DT, LDA, LR, SVM, KNN & 81.3–95.4\% \\ Lim et al. [26] & 48 & 3 & EEG & SVM & 69\% \\ Jussa and Samuvel [14] & 9 & 4 & EEG & LDA & 64\% \\ Plechowska-Wojcik et al. [29] & 11 & 3 & EEG & SVM, DT KNN, RF & 70.4–91.5\% \\ Wu et al. [20] & 39 & 2 & Eye tracking & ANN & 97\% \\ Kaczrowska et al. [24] & 26 & 2 & Eye tracking & SVM, KNN, RF & 97\% \\ Qu et al. [25] & 10 & 3 & EEG & SVM & 79.8\% \\ Kaczrowska et al. [18] & 29 & 3 & Eye tracking & LR, RF & 97\% \\ Pei et al. [16] & 7 & 3 & EEG & RF & 75.9–84.3\% \\ Zhou et al. [28] & 45 & 7 & EEG & KNN, SVM, LDA, ANN & 56\% \\ Li et al. [22] & 28 & 2-3 & EEG + Eye tracking & SVM, RF, ANN & 542–82.7\% \\ Ssmaz et al. [27] & 45 & 3 & EEG & SVM, RF, LDA, ANN & 83.4\% \\ Current study & 15 & 2–3–4 & EEG + Eye tracking & KNN, SVM, ANN, RF, GBM, & 76-90\% \\ \hline \hline \end{tabular} EEG: electroencephalography; fNIRS: functional near-infrared spectroscopy; KNN: k-nearest neighbors; NB: naive bytes DT: decision trees; SVM: support vector machines; RF: random forest; ANN: artificial neural network; DL: deep learning; LDA: linear discriminant analysis; LR: logistic regression.  
\end{table} 
Table 2: Performance comparison with previous studies. **Author Contributions:** Conceptualization, S.H.A., E.C.