CONNECTING BRAIN TO ARTIFICIAL ARMS

Northwestern University researchers of washington have pioneered a technique called targeted muscle reinnervation (TMR), which allows an artificial limb to respond directly to the brain’s signals, making it much easier to use than traditional motorized body parts.



The technique, which is still under development, allows wearers to open and close their artificial hands and bend and straighten their artificial elbows nearly as naturally as their own arms.

“The idea is that when you lose your arm, you lose the motors, the muscles and the structural elements of the bones. But the control information should still be there in the residual nerves,” said Dr Todd A Kuiken, a physiatrist at the Rehabilitation Institute of Chicago and professor at Northwestern University.

He conceived the idea of taking the residual nerves that once carried the commands from the brain to produce arm, wrist and hand movements, and of connecting them to the chest muscles so that the signals can be used to move the artificial limb.

Motorised prosthetic arms are known to produce two arm movements - open and close hand and bend and straighten elbow. However, Kuiken’s team has revealed that TMR has the potential to provide an even greater number of arm and hand movements, beyond the four they’ve already achieved.

A report on the project titled Decoding a new neural-machine interface for control of artificial limbs, published in the Journal of Neurophysiology, reveals that the researchers have begun work with two US Army medical centres to help soldiers who have lost limbs.

“We’re excited to move forward in doing this surgery with our soldiers some day. We’ve been able to demonstrate remarkable control of artificial limbs and it’s an exciting neural machine interface that provides a lot of hope,” Kuiken said.

During the study, the researchers placed between 79-128 electrodes from an electromyogram (EMG)—which picks up the electrical signal that the muscle emits when it contracts—onto the chest muscles of five patients to see if they could identify the unique EMG patterns emitted with 16 different elbow, wrist, hand, thumb and finger movements they asked the patients to perform.

Analysing EMG signals from each of the 16 movements using advanced signal processing techniques enabled the researchers to recognise the signals associated with the different arm movements with 95 per cent accuracy.

The researchers now plan to study whether or not the microprocessor of the artificial arm can be programmed to perform such moves as may enable an individual to hold a baseball, pick up a pen or grasp a tool.