According to Dr. Schwartz, a part of the brain that controls movement, called the primary motor cortex, contains neurons that fire like a Geiger counter in different directions. The direction to which a neuron fires fastest is called its “preferred direction.” Many motor cortical cells change their firing rate for each movement, and this activity from the many neurons is routed through the spinal cord to different muscle groups to generate movement.

It takes thousands of neurons firing in concert to allow even the most simple of movements, and it would be impossible to tap into all of them, so the Pitt team developed an algorithm to fill in the missing neuron signals, allowing them to get a useable signal from a manageable number of electrodes. The algorithm they developed to decode the cortical signals acts like a voting machine by using each cell’s preferred direction as a label and taking a continuous tally of the population throughout the intended movement.

Monkeys were trained to reach for targets, and once the electrodes were in place, the algorithm was adjusted while the arms were restrained to assume the animal was intending to reach for targets.

“Each cell is movement-sensitive and has a preferred direction, and each cell’s preferred direction is like a vote,” said Chance Spalding, a bioengineering graduate student in Dr. Schwartz’s lab who presented the findings. “When all of the votes are added up it gives us the population vector.” These population vectors accurately predict the velocity of normal arm movement, and in the case of this prosthetic, serve as the control signal to convey the monkey’s intention to the prosthetic arm.

Because the software had to rely on a small number of the thousands of neurons needed to move the arm, the monkey did the rest of the work, learning through biofeedback how to refine the arm’s movements by modifying the firing rates of the recorded neurons.

For the task, food was placed at different locations in front of the monkey, and the animal, with its own arms restrained, used the robotic arm to bring the food to its mouth.

“The next step with this device is to add realistic hand and finger movement,” said Meel Velliste, Ph.D., a postdoctoral fellow in the Schwartz lab. “This presents quite a challenge because there are hundreds of different subtle movements we make with our hands and we will need to interpret all of them.”

The arm was developed by the Pitt researchers and custom-built by Keshen Prosthetics in Shanghai, China. The software that controls the arm was developed at Pitt and Arizona State University. Modifications to the original arm were made at the Robotics Institute at Carnegie Mellon University.

In addition to Drs. Schwartz and Velliste and Mr. Spalding, other authors include Beada Jarosiewicz, Ph.D., and Gordon Kirkwood, both of the University of Pittsburgh.