Comparative analysis of cognitive tasks for modeling mental workload with electroencephalogram

Previous electroencephalogram (EEG) studies have shown that cognitive workload can be estimated by using several types of cognitive tasks. In this study, we attempted to characterize cognitive tasks that have been used to manipulate workload for generating classification models. We carried out a comparative analysis between two representative types of working memory tasks: the n-back task and the mental arithmetic task. Based on experiments with 7 healthy subjects using Emotiv EPOC, we compared the consistency, robustness, and efficiency of each task in determining cognitive workload in a short training session. The mental arithmetic task seems consistent and robust in manipulating clearly separable high and low levels of cognitive workload with less training. In addition, the mental arithmetic task shows consistency despite repeated usage over time and without notable task adaptation in users. The current study successfully quantifies the quality and efficiency of cognitive workload modeling depending on the type and configuration of training tasks.