Brain-Controlled Electrical Stimulation Restores Continuous Finger Function

Abstract

Brain-machine interfaces have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user's own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand. In a one-dimensional, continuous, finger-related target acquisition task, the monkey improved his success rate to 83% (1.5s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5s median acquisition, equivalent to chance) when attempting to use his temporarily paralyzed hand. With two monkeys under general anesthesia, we found FES alone could control the monkeys' fingers to rapidly reach targets in a median 1.1s but caused oscillation about the target. Finally, when attempting to perform a virtual two-finger continuous target acquisition task in brain-control mode following temporary hand paralysis, we found performance could be completely recovered by executing recalibrated feedback-intention training one time following temporary paralysis. These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting performance factor in a BCFES neuroprosthesis.

The impact of task context on predicting finger movements in a brain-machine interface

“…However, in FES applications ( Ajiboye et al, 2017 ; Bouton et al, 2016 ; Nason-Tomaszewski et al, 2022 ), biomechanics are important. The final outputs are stimulation parameters that cause a desired amount of muscle contraction.…”

The Impact of Task Context on Predicting Finger Movements in a Brain-Machine Interface

“…This may suggest that kinematic variables are more consistent to use as a command signal when kinematics are the final output. However, in FES applications (Ajiboye et al, 2017; Bouton et al, 2016; Nason-Tomaszewski et al, 2022), the large increases in muscle activation required to acquire these off-context targets may be more likely to impact performance. Here the final outputs are stimulation parameters that cause a desired amount of muscle contraction.…”