A research team from Stanford University has reportedly developed a new technique that is designed to help make brain-controlled prostheses more precise.
According to a news release from Stanford School of Engineering, this technique continuously corrects brain readings to give people with spinal cord injuries a more precise way to tap out commands using a thought-controlled keypad.
The release explains that brain-controlled 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. So tiny errors in the sample—neurons that fire too fast or too slow—reduce the precision and speed of thought-controlled keypads.
According to the release, with the new technique developed by an interdisciplinary team led by Krishna Shenoy, PhD, a professor in the Department of Engineering at Stanford University, the prosthesis analyzes the neuron sample and makes dozens of corrective adjustments to the estimate of the brain’s electrical pattern—all in the blink of an eye.
Their research was published recently in the journal Nature Communications.
Shenoy’s team tested a brain-controlled cursor meant to operate a virtual keyboard. The system is intended for people with paralysis and amyotrophic lateral sclerosis (ALS). The thought-controlled keypad would allow a person with paralysis or ALS to run an electronic wheelchair and use a computer or tablet, according to the release.
The new technique is based on a recently discovered understanding of how monkeys naturally perform arm movements. The research team note in their study that the monkeys used their arms, hands, and fingers to reach for targets presented on a video screen, the release explains.
By understanding the money movements, the research team wished to learn what the electrical patterns from the 100- to 200-neuron sample looked like during a normal reach. In short, they came to understand the “brain dynamics” underlying the reaching arm movements, the release states.
“These brain dynamics are analogous to rules that characterize the interactions of the millions of neurons that control motions,” says Jonathan Kao, a doctoral student in electrical engineering and the study’s first author, in the release. “They enable us to use a tiny sample more precisely,” he continues.
In their current experiments, Shenoy’s team members distilled their understanding of brain dynamics into an algorithm that could analyze the measured electrical signals that their prosthetic device obtained from the sampled neurons. The algorithm tweaked these measured signals so that the sample’s dynamics were more like the baseline brain dynamics. The goal was to make the thought-controlled prosthetic more precise, the release explains.
To test this algorithm, they 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 team measured how many targets the monkeys could tap with their fingers in 30 seconds, and found that the monkeys averaged 29 correct finger taps in 30 seconds, the release continues.
The real experiment only scored virtual taps that came from the monkeys’ brain-controlled cursor. Although the monkey may still have moved his 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% as quickly as a monkey’s finger, per the release.
The US Food and Drug Administration recently gave Shenoy’s team the green light to conduct a pilot clinical trial of their brain-controlled cursor on people with spinal cord injuries, according to the release.
“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 says in the release.
[Source(s): Stanford School of Engineering, EurekAlert]