Classifying mental workload using EEG data: A machine learning approach

Open Access
Article
Conference Proceedings
Authors: Şeniz Harputlu AksuErman Çakıt

Abstract: Mental workload is related to the difference between the available mental resource capacity of the operator and the mental resource required by the job. To decide the number of tasks assigned to operator and the difficulty levels of those tasks, it is important to know the operator's mental workload. An overload occurs if the amount of resources required by the task exceeds the available capacity of the person. Mental workload analysis helps to recognize the mental fatigue, evaluate the human performance of different level tasks and adjust cognitive sources for safe and efficient human-machine interactions. Excessive levels of mental workload can lead to errors or delays in information processing. Monitoring brain activity has been verified to be sensitive and consistent reflector of mental workload changes. Classification, regression, clustering, anomaly detection, dimensionality reduction, and reward maximization are common machine learning models. Classification of mental workload has critical importance in the domain of human factors and ergonomics. In recent years, with the need to analyze continuous and large-scale data obtained by physiological methods, the use of machine learning algorithms has become widespread in estimating and classifying mental workload. The objectives of the current study were two-fold: (1) to investigate the relationship among EEG features, task difficulty levels and subjective self-assessment (NASA-TLX) scores and (2) to develop machine learning algorithms for classifying mental workload using EEG features. N-back tasks have been commonly used in the literature. In this study, N-back memory tests were performed at four different difficulty levels. As the number of n increases, so does the difficulty of the task. Four participants performed the tests. Seventy EEG features (5 frequency band power for 14 channels) were selected as independent variables. One output variable reflecting the difficulty level of N-Back memory was classified. The machine learning algorithms used in our study were K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) algorithms. As the task difficulty increased, theta activity in prefrontal and frontal regions increased. Especially frontal theta power, parietal and occipital gamma power were significantly correlated to perceived workload scores obtained via NASA-TLX. Prefrontal beta-high activity had a significant negative relationship with self-assessment workload ratings. Prefrontal and frontal theta, prefrontal beta-high, occipital, parietal and temporal gamma and occipital alpha activities were found to be the most effective parameters. The results obtained for the four classes of classification problem reached the accuracy of 68% with EEG features as input and the Random Forest algorithm. In addition, the results obtained for the two classes of classification problem reached the accuracy of 87% with EEG features as input and the GBM algorithm. The results from the analysis indicate that EEG signals play an important role in the classification of mental workload. Another remarkable result was high classification performance of GBM, LightGBM and XGBoost algorithms that have been developed in the recent past and therefore not frequently used in studies on this subject in the literature.

Keywords: Machine learning, mental workload, modeling

DOI: 10.54941/ahfe1001820

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