The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS?

The ability to identify reliable and sensitive physiological signatures of psychological dimensions is key to developing intelligent adaptive systems that may in turn help to mitigate human error in complex operations. The challenge of this endeavor lies with diagnosticity. Despite different underlying causes, the physiological correlates of workload and acute psychological stress manifest in rather similar ways and can be easily confounded. The current work aimed to build a diagnostic model of mental state through the simultaneous classification of mental workload (varied through three levels of the n-back task) and acute stress (the presence/absence of aversive sounds) with machine learning. Using functional near infrared spectroscopy (fNIRS) and electrocardiography (ECG), the model's classifiers was above-chance to disentangle variations of mental workload from variations of acute stress. Both ECG and fNIRS could predict mental workload level, the best accuracy resulted from the two measures in combination. Stress level could not be accurately diagnosed through ECG alone, only with fNIRS or ECG and fNIRS combined. Individual calibration may be important since stress classification was more accurate for those with higher subjective state anxiety, perhaps due to a greater sensitivity to stress. Mental workload and stress were both better classified with activity in lateral prefrontal regions of the cortex than the medial areas, and the HbO2 signal generally lead to better classification than HHB. The current model represents a step forward to finely discriminate different mental states despite their rather analog physiological correlates.