Assessing vibrotactile feedback strategies by controlling a cursor with unstable dynamics

Abstract

Brain computer interface (BCI) control predominately uses visual feedback. Real arm movements, however, are controlled under a diversity of feedback mechanisms. The lack of additional BCI feedback modalities forces users to maintain visual contact while performing tasks. Such stringent requirements result in poor BCI control during tasks that inherently lack visual feedback, such as grasping, or when visual attention is diverted. Using a modified version of the Critical Tracking Task [1] which we call the Critical Stability Task (CST), we tested the ability of 9 human subjects to control an unstable system using either free arm movements or pinch force. The subjects were provided either visual feedback, ‘proportional’ vibrotactile feedback, or ‘on-off’ vibrotactile feedback about the state of the unstable system. We increased the difficulty of the control task by making the virtual system more unstable. We judged the effectiveness of a particular form of feedback as the maximal instability the system could reach before the subject lost control of it. We found three main results. First, subjects can use solely vibrotactile feedback to control an unstable system, although control was better using visual feedback. Second, ‘proportional’ vibrotactile feedback provided slightly better control than ‘on-off’ vibrotactile feedback. Third, there was large intra-subject variability in terms of the most effective input and feedback methods. This highlights the need to tailor the input and feedback methods to the subject when a high degree of control is desired. Our new task can provide a complement to traditional center-out paradigms to help boost the real-world relevance of BCI research in the lab.

The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates

“…We adapted the CST from the human performance literature, where it was introduced by Jex et al (1966). Previously we used it to study the usability of different effectors and feedback modalities in humans (Quick et al 2014). Here we report on its successful use by two Rhesus monkeys, using two different sensory modalities for feedback.…”

“…This is probably due to variations in training that were made to suit the behavioral traits of each monkey. However, it might also reflect natural variability in ability, since human subjects also exhibited a range of skill (Quick et al 2014), despite a fairly consistent amount of exposure to the task. For both monkeys, performance with visual feedback was better than performance with vibrotactile feedback, by factors of 1.6 and 2.1.…”

“…We converted the differential equation that defines the CST (Eq. 1) into an equivalent discrete-time algorithm to implement the CST on a computer (Quick et al 2014): 2, ... (3) where x͑k͒ ϭ x͑t͒| tϭkT s is the current horizontal cursor position at time t ϭ kT s and T s ϭ 0.01s is the update interval; u(k) is the current horizontal hand position of the subject; x(k ϩ 1) is the cursor position to be rendered at the next update; and a ϭ e T s Ͼ 1 is the discrete system instability parameter as determined by the value of .…”

“…tage conferred by different types of feedback (Quick et al 2014;Rechenmann et al 2016), for both real and prosthetic movements. Artificial feedback requires some learning before it can be used by the subject (Dadarlat et al 2015;Godlove et al 2014;O'Doherty et al 2011).…”

“…Previously we reported on the performance of human subjects engaged in the CST (Quick et al 2014). Here we report on the behavior of two Rhesus monkeys that performed the CST.…”