Using Functional Near Infrared Spectroscopy to Assess Cognitive Workload

Quantification of mental workload is a significant aspect of monitoring and adaptive aiding systems that are intended to improve the efficiency and safety of human–machine systems. Functional near Infrared (fNIR) spectroscopy is a field-deployable brain monitoring device that provides a measures of cerebral hemodynamic within the prefrontal cortex. The purpose of this study was to assess the cognitive load by using Performance (reaction time), Behavioral metrics (NASA TLX) and Neuro-Cognitive Measures (Hemodynamic response). To observe the activation in prefrontal cortex, we employed Functional Near Infrared (fNIR) Spectroscopy with a Standard Stroop task. A total of 25 healthy participants (N 18 Male and N 07 Female, M Age 25.5 SD 7.6), participated in the study. For statistical analysis, a repeated measure t-test was computed to compare the Oxy (Δ[HbO2]) and De-Oxy (Δ[hHb]) changes under Congruent and In-Congruent task conditions. For Classification, Binary logistic regression model applied to identify how accurately classifying the varied workload conditions. The finding shows that fNIR measures had adequate predictive power for estimating task performance in workload conditions. In this paper, we have found evidence that fNIR can be used as indicator of cognitive load which is important for optimal human performance

Cognitive Workload Analysis of Fighter Aircraft Pilots in Flight Simulator Environment

Maintaining and balancing an optimal level of workload is essential for completing the task productively. Fighter aircraft is one such example, where the pilot is loaded heavily both physically (due to G manoeuvering) and cognitively (handling multiple sensors, perceiving, processing and multi-tasking including communications and handling weapons) to fulfill the combat mission requirements. This cognitive demand needs to be analysed to understand the workload of fighter pilot. Objective of this study is to analyse dynamic workload of fighter pilots in a realistic high-fidelity flight simulator environment during different flying workload conditions. The various workload conditions are (a) normal visibility, (b) low visibility, (c) normal visibility with secondary task, and (d) low visibility with secondary task. Though, pilot’s flying performance score was good, the physiological measure like heart rate variability (HRV) features and subjective assessment (NASA-TLX) components are found to be statistically significant (p<0.05) between tasks. HRV features such as SD2, SDNN, VLF and total power are found to be significant at all task load conditions. The features LFnu and HFnu are able to differentiate the effect of low visibility and secondary cognitive task, which was imposed as increased task in this study. This result benefits to understand the pilot’s task and performance at each flying phase and their cognitive demands during dynamic workload using HRV, which could assist pilot’s training schedule in optimal way on simulators as well as in actual flight conditions