Date Published: April 17, 2017
Publisher: Public Library of Science
Author(s): Winnie K. Y. So, Savio W. H. Wong, Joseph N. Mak, Rosa H. M. Chan, Emmanuel Manalo.
Using a wireless single channel EEG device, we investigated the feasibility of using short-term frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65%–75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.
The construct of mental workload can be understood as the level of cognitive engagement which has a direct impact on the effectiveness and quality of a learning process . While an optimal level of mental workload facilitates efficient learning, mental overload could negatively affect task performance and result in more errors . An overloaded individual may even exhibit psychological symptoms, such as frustration, stress and depression . Yet, there lacks a real-time measure of mental workload which can help an individual identify the optimal level of mental workload and hence enhance one’s learning performance.
This study aims to develop an EEG-based mental workload-detection application by building a generalized model for four different cognitive and motor tasks. Our findings showed that the frontal theta activity is a common feature across these tasks. This result is consistent with previous studies that theta activities increase with the level of mental effort [17, 18]. Meanwhile, the correlation of mental workload level and other frequency bands is task-dependent [3, 36].