EEG coupling features: Towards mental workload measurement based on wearables

Automated mental workload measurement is particularly important in safety-critical settings, such as in nuclear plants, aviation, air traffic control, shipping, and transportation, to name a few. As an example, recent statistics have suggested that 90% of the accidents in the transport industry are due to human factors. In this paper, we explore the potential of off-the-shelf wearable technologies in monitoring mental workload in real-time, thus potentially reducing the number of accidents due to human errors. Wearable technologies, while providing the user with ease-of-use, comfort, and portability, have several limitations, such as lower quality signal readings (e.g., due to dry electrodes) and smaller number of recording sites. Such limitations place a burden on the accuracy of existing mental workload models. To overcome this limitation, we propose the use of phase-amplitude and amplitude-amplitude coupling features computed from a portable commercial electroencephalography (EEG) device. Experiments with three different tasks, namely N-back, mental rotation and visual search, show the proposed features being significantly correlated with multiple dimensions of the widely-used NASA task load index test and providing complementary information to other conventional features.