Providing user-focused, objective, and quantified metrics for prosthesis usability may help reduce the high (up to 50%) abandonment rates and accelerate the clinical adoption and cost reimbursement for new and improved prosthetic systems. We comparatively evaluated several physiological, behavioral, and subjective cognitive workload measures applied to upper-limb neuroprosthesis use. Users controlled a virtual prosthetic arm via surface electromyography (sEMG) and completed a virtual target control task at easy and hard levels of difficulty (with large and small targets, respectively). As indices of cognitive workload, we took behavioral (Detection Response Task; DRT) and electroencephalographic (EEG; parietal alpha and frontal theta power, and the P3 event-related potential) measures for one group (n = 1 amputee participant, n = 10 non-amputee participants), and electrocardiographic (ECG; low/high frequency heart-rate variability ratio) and pupillometric (task-evoked pupillary response) measures for another group (n = 1 amputee participant, n = 10 non-amputee participants), because all measures could not reasonably be recorded simultaneously. Participants of both groups also completed the subjective NASA Task-Load Index (TLX) survey. Ease of use, setup, piloting, and analysis complexity varied among measures. The DRT required minimal piloting, was simple to set up, and used straightforward analyses. ECG measures required moderate piloting, were simple to set up, and had somewhat complex analyses. Pupillometric measures required extensive piloting but were simple to set up and relatively simple to analyze. EEG measures required extensive piloting, extensive setup and equipment, careful monitoring, and moderately complex analyses. Across subjects, the DRT, low/high frequency heart-rate variability ratio, task-evoked pupillary response, and NASA TLX significantly differentiated between the easy and hard tasks, whereas EEG measures (alpha power, theta power, and P3 event-related potential) did not. Aside from the NASA TLX, the DRT was the easiest to use and most sensitive to cognitive load across and within subjects. Among physiological measures, we recommend ECG, pupillometry, and EEG/ERPs, in that order. This study provides the first evaluation of multiple objective and quantified cognitive workload measures during the same task with prosthesis use. User-focused cognitive workload assessments may increase our understanding of human interactions with advanced upper-limb neuroprostheses and facilitate their improvements and translation to real-world use.

JMC owns Red Scientific, which develops human factors research tools, including the DRT. TSD, CCD, and GAC are inventors on a patent for decoding EMG motor signals. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

This work was sponsored by: the Hand Proprioception and Touch Interfaces (HAPTIX) program administered by the Biological Technologies Office (BTO) of the Defense Advanced Research Projects Agency (DARPA) through the Space and Naval Warfare Systems Center, Contract No. N66001-15-C-4017; the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number ULTR002538 and TL1TR002540; and the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Ruth L. Kirchstein National Research Service Award Number 1F31NS118938.

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IRB of the University of Utah gave ethical approval for this work.

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All data produced in the present study are available upon reasonable request to the authors