Using near infrared spectroscopy and heart rate variability to detect mental overload

Mental workload is a key factor influencing the occurrence of human error, especially during piloting and remotely operated vehicle (ROV) operations, where safety depends on the ability of pilots to act appropriately. In particular, excessively high or low mental workload can lead operators to neglect critical information. The objective of the present study is to investigate the potential of functional near infrared spectroscopy (fNIRS) - a non-invasive method of measuring prefrontal cortex activity - in combination with measurements of heart rate variability (HRV), to predict mental workload during a simulated piloting task, with particular regard to task engagement and disengagement. Twelve volunteers performed a computer-based piloting task in which they were asked to follow a dynamic target with their aircraft, a task designed to replicate key cognitive demands associated with real life ROV operating tasks. In order to cover a wide range of mental workload levels, task difficulty was manipulated in terms of processing load and difficulty of control - two critical sources of workload associated with piloting and remotely operating a vehicle. Results show that both fNIRS and HRV are sensitive to different levels of mental workload; notably, lower prefrontal activation as well as a lower LF/HF ratio at the highest level of difficulty, suggest that these measures are suitable for mental overload detection. Moreover, these latter measurements point toward the existence of a quadratic model of mental workload.