Abstract
Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model.
Highlights
Cognitive workload is defined as a quantitative measure of the amount of mental effort needed to perform a task [1]
Analyzed data subjected to statistical analysis had 285 features (19 electrodes, 5 frequencies, 3 cognitive workload levels)
The main focus of the present research was put on estimation of cognitive workload level in accordance with subject-independent approach applying classical machine learning methods combined with feature selection techniques
Summary
Cognitive workload is defined as a quantitative measure of the amount of mental effort needed to perform a task [1]. We distinguish the terms “cognitive workload” and “mental fatigue”, whereas mental fatigue is defined as a psychological state related to the loss of work capacity [2]. Assessment of human mental effort is an important, but not trivial task. Research on cognitive workload gives an opportunity to understand the process of mental fatigue, including analyzing the influence of different complexity tasks on mental effort and concentration level. Estimation of mental effort may be helpful in adjusting learning techniques and cognitive sources, as well as in understanding the human performance of different level tasks and information processing capabilities
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