Providing Artificial Feedback from a Hand That’s Akin to Touch

Achievement date: 
2017
Outcome/accomplishment: 

Researchers used direct stimulation of the human brain surface, with artificial electrical signals providing sensory feedback akin to touch—a major goal in restoring function to people with spinal-cord injuries. The research team worked with support from the Center for Sensorimotor Neural Engineering (CSNE), an NSF-funded Engineering Research Center (ERC) with its headquarters at the University of Washington (UW).

Impact/benefits: 

Getting signals to the brain from a fingertip, hand, or other extremity is key to restoring functionality after a spinal injury, where research has largely focused on transmitting signals from the brain to the extremity. The two-way, closed-loop “bi-directional brain-computer interfaces” (BCCIs) enable two-way communication in the nervous system. These BBCIs would allow the brain to directly control external prosthetics or other devices that can enhance movement—or even reanimate a paralyzed limb—with the accuracy and functionality added through sensory feedback. 

Explanation/Background: 

Grasping a cup, brushing hair, or cooking a meal requires the sort of touch feedback lost to people without limbs and who suffer from paralysis. Information from a fingertip, limb, or external device helps a brain to understand how firmly to grip something, or how much pressure is needed to perform simple tasks.

A glove embedded with sensors, linked to electrodes stimulating the patient’s brain, delivered electrical signals that varied with how fingers and joints were positioned. When their hands opened too far, they received no electrical stimulus to the brain. When their hand was too closed, like squeezing something too hard, the sensors emitted more intense electrical stimuli. Only the artificial electrical data provided feedback to the test subjects.

While unrelated to the sort of signals a brain receives across a normal human nervous system, the signals provided one patient with enough feedback that they could reach a target position with accuracy well above chance. Performance dropped when the patient received random signals regardless of hand position, suggesting that the subject had been using the artificial sensory feedback to control hand movement.