Discrimination of mental workload levels in human subjects with functional near-infrared spectroscopy

We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involv-ing 3 mental workload levels based on to working memory tasks. To quantify the poten-tial of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin (HbO2) and deoxy-hemoglobin (HbR) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 44%–72%, which are signifi-cantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application