Investigating the Utility of fNIRS to Assess Mental Workload in a Simulated Helicopter Environment


Functional near-infrared spectroscopy (fNIRS) has been used with moderate success in many passive brain-computer interface applications. Much of this recent work has been focused on differentiating between various states shortly following discrete stimuli. We aim to extend these results to the to the assessment of an operator’s mental state during the complex environment encountered by helicopter pilots. This work presents initial efforts made in this direction. Stepping though phases of increasing complexity, fNIRS data from the pre-frontal cortex were collected and analyzed from four participants as they completed n-back tests, discrete flight simulator tasks, and during abbreviated simulated medevac mission scenarios. Data collected during the n-back tests and discrete simulator tasks were found not to be significantly clustered in the feature space considered. A support vector machine (SVM) classifier was trained on the n-back data to differentiate between workload levels and applied to the discrete simulator task data achieving an average 3-class classification accuracy of 57% and an average 2-class classification accuracy of 68%. Finally, this classifier was applied to the data collected during the simulated mission and the result was found to be only weakly correlated with the participant’s subjectively-Assessed workload. Due to these results, it is not yet clear how an n-back-Trained classifier could be utilized to augment an adaptive crew support system. We suggest that the levels of ‘workload’ measured by an n-back test should not be expected to ‘map onto’ other, more complex, subjective evaluations of ‘workload.’ Strong hemodynamic responses observed during mission execution however, suggest fNIRS may contain data relevant for the augmentation of an adaptive assistant system.

Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020