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The purpose of this study was to demonstrate how the inclusion of a tactical task goal awareness measure complemented mental workload and performance measures in a simulated air combat mission. It was hypothesized that the evaluation of the tactical task goal awareness could provide additional information concerning the cognitive demands a pilot is exposed to during a complex air combat task. A test setting was developed to test this hypothesis in a virtual flight training device. To highlight the impact of task complexity, high performance aircraft pilots’ heart rate (HR), interbeat-interval (IBI), and performance in two simple flying tasks were first compared. Then, a similar comparison, complemented with the tactical task goal awareness measure, was made with two complex flying tasks. It was found that when the pilot's awareness of the tactical goals was low, a combination of low performance and low mental workload occurred. It was concluded that when the pilots’ performance is evaluated on a complex air combat task, the awareness of the tactical goals, performance, and mental workload should be studied together as the pilot's awareness can explain some of HR/IBI responses that could be otherwise misinterpreted. More generally, mental workload, performance, and task goal awareness should all be considered when the operator's performance in any complex human–machine system is assessed.

Optical-neuro-imaging based functional Near-Infrared Spectroscopy (fNIRS) has been in use for several years in the fields of brain research to measure the functional response of brain activity and apply it in fields such as Neuro-rehabilitation, Brain-Computer Interface (BCI) and Neuro-ergonomics. In this paper we have enhanced the classification accuracy of a Mental workload task using a novel Fixed-Value Modified Beer-Lambert law (FV-MBLL) method. The hemodynamic changes corresponding to mental workload are measured from the Prefrontal Cortex (PFC) using fNIRS. The concentration changes of oxygenated and deoxygenated hemoglobin (ΔcHbO (t) and ΔcHbR (t)) of 20 participants are recorded for mental workload and rest. The statistical analysis shows that data obtained from fNIRS is statistically significant with p 1.97 at confidence level of 0.95. The Support Vector Machine (SVM) classifier is used to discriminate mental math (coding) task from rest. Four features, namely mean, peak, slope and variance, are calculated on data processed through two different variants of Beer-lambert Law i.e., MBLL and FVMBLL for tissue blood flow. The optimal combination of the mean and peak values classified by SVM yielded the highest accuracy, 75%. This accuracy is further enhanced using the same feature combination, to 94% when those features are calculated using the novel algorithm FV-MBLL (with its optical density modelled form the first 4 sec stimulus data). The proposed technique can be effectively used with greater accuracies in the application of fNIRS for functional brain imaging and Brain-Machine Interface.

Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier—support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.