ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task

Abstract

Electroencephalograph (EEG) has been increasingly studied to identify distinct mental factors when persons perform cognitively demanding tasks. However, most of these studies examined EEG correlates at channel domain, which suffers the limitation that EEG signals are the mixture of multiple underlying neuronal sources due to the volume conduction effect. Moreover, few studies have been conducted in real-world tasks. To precisely probe EEG correlates with specific neural substrates to mental factors in real-world tasks, the present study examined EEG correlates to three mental factors, i.e., mental fatigue [also known as time-on-task (TOT) effect], workload and effort, in EEG component signals, which were obtained using an independent component analysis (ICA) on high-density EEG data. EEG data were recorded when subjects performed a realistically simulated air traffic control (ATC) task for 2 h. Five EEG independent component (IC) signals that were associated with specific neural substrates (i.e., the frontal, central medial, motor, parietal, occipital areas) were identified. Their spectral powers at their corresponding dominant bands, i.e., the theta power of the frontal IC and the alpha power of the other four ICs, were detected to be correlated to mental workload and effort levels, measured by behavioral metrics. Meanwhile, a linear regression analysis indicated that spectral powers at five ICs significantly increased with TOT. These findings indicated that different levels of mental factors can be sensitively reflected in EEG signals associated with various brain functions, including visual perception, cognitive processing, and motor outputs, in real-world tasks. These results can potentially aid in the development of efficient operational interfaces to ensure productivity and safety in ATC and beyond.

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

“…On the other hand, a progressive suppression of alpha waves has been linked to increasing levels of task difficulty (Mazher et al, 2017). Cortical areas that have been associated with alpha band changes are parietal and occipital areas (Dasari et al, 2017;Puma et al, 2018).…”

“…In many domains, the ability to process information, to react to different environments, and to make accurate decisions is vital. For instance, air traffic controllers (ATCs) generally perform in a highly cognitively-demanding environment, working for long periods of time, and under stress (Dasari et al, 2017). This scenario can lead to depletion of cognitive resources and thus degradation of performance.…”

Indexing Mental Workload During Simulated Air Traffic Control Tasks by Means of Dual Frequency Head Maps

“…Experiments with ATM simulations and a task-load grading proved to be advantageous although the majority of simulated ATM examinations were limited to two task-load levels (easy and difficult). Relevant studies on workload determination methods for simulated or real air traffic control were conducted by Brookings et al (1996), Russell (2003b, 2007), Shou et al (2012), Abbass et al (2014b,c), Borghini et al (2014Borghini et al ( , 2017, Aricò et al (2015Aricò et al ( , 2016, Aricò et al (2018), Di Flumeri et al (2015), Dasari et al (2017), and Dehais et al (2018). Wilson and Russell (2003a) investigated the classification of the mental state of seven air traffic controllers in simulated air traffic monitoring.…”

Human-based dynamics of mental workload in complicated systems

“…On the other hand, the balance in the workload reduces the human error and increases the task performance of operators (Xie and Salvendy, 2000[ 36 ]; Yu et al, 2016[ 40 ]; Zhao et al, 2016[ 41 ]). Therefore, the concept of mental workload and mechanism of its effect on task performance in different human-machine systems is considered by practitioners and researchers in a variety of cognitive activities, such as conventional driving (Allahyari et al, 2014[ 1 ]; Hassanzadeh-Rangi et al, 2014[ 18 ]; Yan et al, 2019[ 37 ]), automated driving (Ko and Ji, 2018[ 22 ]), train driving (Balfe et al, 2017[ 6 ]), nuclear power plants (Choi et al, 2018[ 12 ]), advanced surgery programs (Cavuoto et al, 2017[ 9 ]), air traffic monitoring (Dasari et al, 2017[ 14 ]), control rooms (Melo et al, 2017[ 24 ]), workplace activities (Chen et al, 2017[ 11 ]), information technologies (Buettner, 2017[ 7 ]) and other complex human-machine systems (Xiao et al, 2015[ 35 ]). Few conceptual frameworks are available for understanding mental workload mechanism based on the static relationship extracted from traditional statistics (Xie and Salvendy, 2000[ 36 ]).…”