Brain-controlled prostheses sample a few hundred neurons to estimate motor commands that involve millions of neurons, so tiny sampling errors can reduce the precision and speed of thought-controlled keypads. A Stanford technique can analyze this sample and make dozens of corrective adjustments in the blink of an eye to make thought control more precise. Image courtesy of Jonathan Kao with the Shenoy Lab.
In recent years, researchers have sought to give people suffering from injury or disease some restored motor function by developing brain-controlled prostheses. Such prostheses currently work with access to a sample of only a few hundred neurons, but need to estimate motor commands that involve millions of neurons. Now, an interdisciplinary team led by Stanford University electrical engineer Krishna Shenoy, PhD, has developed a technique to make brain-controlled prostheses more precise.
In essence, the prosthesis analyzes the neuron sample and makes dozens of corrective adjustments to estimate the brain’s electrical pattern-all in the blink of an eye. Shenoy’s team tested a brain-controlled cursor meant to operate a virtual keyboard. The thought-controlled keyboard would allow a person with paralysis or amyotrophic lateral sclerosis (ALS) to run an electronic wheelchair and use a computer or tablet.
The new corrective technique is based on a recently discovered understanding of how monkeys naturally perform arm movements. “These brain dynamics are analogous to rules that characterize the interactions of the millions of neurons that control motions,” said Jonathan Kao, a doctoral student in electrical engineering and first author of the Nature Communications paper on the research. “They enable us to use a tiny sample more precisely.”
In their current experiments, Shenoy’s team members created an algorithm to analyze the measured electrical signals that their prosthetic device obtained from the sampled neurons. The goal was to make the thought-controlled prosthesis more precise. To test the algorithm, the Stanford researchers trained two monkeys to choose targets on a simplified keypad. The keypad consisted of several rows and columns of blank circles. When a light flashed on a given circle, the monkeys were trained to reach for that circle with their arms. To set a performance baseline, the researchers measured how many targets the monkeys could tap with their fingers in 30 seconds. The monkeys averaged 29 correct finger taps in 30 seconds.
The real experiment only scored virtual taps that came from the monkeys’ brain-controlled cursor. Although the monkey may still have moved its fingers, the researchers only counted a hit when the brain-controlled cursor, corrected by the algorithm, sent the virtual cursor to the target. The prosthetic scored 26 thought-taps in 30 seconds, about 90 percent as quickly as a monkey’s finger.
The goal of the research is to get thought-controlled prostheses to people with paralysis or ALS, who may currently use an eye-tracking system to direct cursors or a “head mouse” that tracks the movement of the head. Both are fatiguing to use. Neither provides the natural and intuitive control of readings taken directly from the brain.
The U.S. Food and Drug Administration recently gave Shenoy’s team approval to conduct a pilot clinical trial of its thought-controlled cursor on people with spinal cord injuries.
“This is a fundamentally new approach that can be further refined and optimized to give brain-controlled prostheses greater performance, and therefore greater clinical viability,” Shenoy said.
Editor’s note: This story was adapted from materials provided by Stanford University.
