A Psychometric Platform to Collect Somatosensory Sensations for Neuroprosthetic Use

Abstract

Somatosensory neuroprostheses exploit invasive and non-invasive feedback technologies to restore sensorimotor functions lost to disease or trauma. These devices use electrical stimulation to communicate sensory information to the brain. A sensation characterization procedure is thus necessary to determine the appropriate stimulation parameters and to establish a clear personalized map of the sensations that can be restored. Several questionnaires have been described in the literature to collect the quality, type, location, and intensity of the evoked sensations, but there is still no standard psychometric platform. Here, we propose a new psychometric system containing previously validated questionnaires on evoked sensations, which can be applied to any kind of somatosensory neuroprosthesis. The platform collects stimulation parameters used to elicit sensations and records subjects' percepts in terms of sensation location, type, quality, perceptual threshold, and intensity. It further collects data using standardized assessment questionnaires and scales, performs measurements over time, and collects phantom limb pain syndrome data. The psychometric platform is user-friendly and provides clinicians with all the information needed to assess the sensory feedback. The psychometric platform was validated with three trans-radial amputees. The platform was used to assess intraneural sensory feedback provided through implanted peripheral nerve interfaces. The proposed platform could act as a new standardized assessment toolbox to homogenize the reporting of results obtained with different technologies in the field of somatosensory neuroprosthetics.

Keywords: amputees; electrodes; neuroprosthesis; neurostimulation; platform; psychophysics; sensory feedback; somatosensations.

Figure 1

Neuroprosthetic applications. Neurotechnologies for restoring somatosensations have been developed for peripheral (PNS) or central nervous systems (CNS). The stimulation technique used to restore sensory feedback can be invasive (surgically implanted and in intimate contact with the nervous tissue) or non-invasive (applied on the skin surface). Delivering a stimulation to the brain or peripheral nerves provides benefits such as the control of robotics, smart prosthetics, or other assistive technologies.

Figure 2

Sensation characterization procedure. (1) Stimulation parameters are selected. The stimulation trains are delivered using the neurostimulator; the software also sends control commands to the Easy Quest app. (2) Patient perceives a stimulation-evoked sensation on the phantom hand thanks to the neural implant. (3) Easy Quest app in ODF mode is used to report the sensations. (4) Experimenters collect all sensation characterization outcomes and import them in MATLAB or Excel to plot the results.

Figure 3

Use cases. The three main features of the psychometric platform: the first two are implemented by the mobile app and the last by the whole system. (A) Defined as Local Fill-in (LF), where the users compile a questionnaire and the answers are stored in the device. (B) On demand fill-in (ODF), in this case, the app waits for an external command from a controller app containing information on the questionnaire to be shown; the fill-in procedure is the same but nothing is stored within the device, instead results are sent back to the controller. (C) The procedure seen from the experimenter's point of view, here, the role of the other software programs of the platform (Easy Quest Create, Easy Quest Evaluate) is explained.

Figure 4

Software architecture. The main components of the platform depicted as squares, external services are shown with an icon and communication with arrows, some show a label with examples of the information flowing through. A gray shadow surrounds the software modules of the mobile app (Easy Quest).

Figure 5

Usability assessment. All the usability scales are reported: Overall reaction to the software, QUIS, SUS, NAU, and ASQ. Three clinicians, six engineers, and three patients evaluated the psychophysical platform (N = 12). The data in the figure are represented as means ± standard deviations. The last row resumes usage metrics.