Correlation Between Physiological and Performance-Based Metrics to Estimate Pilots' Cognitive Workload

Abstract

This paper discusses the utilization of pilots' physiological indications such as electroencephalographic (EEG) signals, ocular parameters, and pilot performance-based quantitative metrics to estimate cognitive workload. The study aims to derive a non-invasive technique to estimate pilot's cognitive workload and study their correlation with standard physiological parameters. Initially, we conducted a set of user trials using well-established psychometric tests for evaluating the effectiveness of pupil and gaze-based ocular metrics for estimating cognitive workload at different levels of task difficulty and lighting conditions. Later, we conducted user trials with the NALSim flight simulator using a business class Learjet aircraft model. We analyzed participants' ocular parameters, power levels of different EEG frequency bands, and flight parameters for estimating variations in cognitive workload. Results indicate that introduction of secondary task increases pilot's cognitive workload significantly. The beta frequency band of EEG, nearest neighborhood index specifying distribution of gaze fixation, L1 Norm of power spectral density of pupil diameter, and the duty cycle metric indicated variations in cognitive workload.

Comparing Presence, Workload, and Performance in Desktop and Virtual Reality Flight Simulations

“…Our findings regarding longitudinal stick data are in line with previous findings from low-fidelity simulator studies [12], [21], in which increased task difficulty (i.e., flying within the virtual boundaries of a given flight path of decreasing sizes, and adding secondary tasks to the initial flight maneuvers) increased Duty Cycle and Aggressiveness. As Gray [22] explains, when pilots are flying, they do not constantly change their control inputs.…”

“…In simulated flights, increase in workload has been associated with increased deviations from targeted flight parameters [11], [12]. Behavioral measures that target workload are of importance in the understanding of learning [13], and adaptive human-controlled systems [14].…”

“…The stick control is generally described using two measures: Duty Cycle and Aggressiveness [15], [19]. Duty Cycle refers to the amount of time the controller changes the input on the stick (in percentage), and Aggressiveness indicates the rate of change of the stick movements [12], [19]. Using these two measures allows for estimating Pilot Inceptor Workload (PIW): a twodimensional measure of pilot gain, i.e., the effort put into aircraft control [19]- [22].…”

Should All Minimal Access Surgery Be Robot-Assisted? A Systematic Review into the Musculoskeletal and Cognitive Demands of Laparoscopic and Robot-Assisted Laparoscopic Surgery

“…Whilst three studies using subjective measures of assessment reported no difference in cognitive load between LS and RALS, 35 , 37 , 40 these findings are likely limited by the insensitivity of the methods employed. 50 Collectively, data suggest that cognitive demand is greater in LS.…”

“…Further research is required to quantify cognitive fatigue in surgeons using objective measures such as EEG, which are less prone to the limitations of subjective assessment. 50 , 53 , 54 This will provide a direct objective measurement of brain function, unlike indirect objective measurements based on heart rate or skin conductance. If changes in fatigue during surgery can be determined objectively, then the relationship to musculoskeletal fatigue should be investigated in tandem to determine if cognitive fatigue is causative of musculoskeletal fatigue, caused by a reduction in central drive.…”