How Brain-operated Machines Can Be Stable, Functional
DURHAM, N.C. -- In order to function stably over long periods, brain-operated devices such as neural prosthetic limbs for paralyzed people will require brain signals fed from hundreds of infinitesimal recording electrodes in the brain, Duke University researchers have concluded.
Their findings in studies with monkeys are defining the requirements for successful brain-machine systems, as the researchers progress toward the first clinical trials of fully functional neural prosthetics.
The researchers, led by neurobiologist Miguel Nicolelis, published their findings in the Nov. 16, 2005, issue of the Journal of Neuroscience. Besides Nicolelis, co-authors of the study were Jose Carmena and Mikhail Lebedev in the Nicolelis laboratory and biomedical engineer Craig Henriquez in the Pratt School pf Engineering. All are members of the Duke Center for Neuroengineering. The research was sponsored by the Christopher Reeve Paralysis Foundation, the Defense Advanced Research Projects Agency, the National Institutes of Health, the National Science Foundation and the James S. McDonnell Foundation.
Basically, the researchers gave macaque monkeys a task involving reaching with a hand-held pole and analyzed the stability of the outputs from neurons in the monkeys' brains, as measured by scores of recording electrodes.
They found that individual electrodes, or even small numbers, showed variability in output that would render brain-operated devices inconsistent in their operation. However, the researchers found that integrating signals from large ensembles of neurons yielded a stable output that could support complex functionality of neural prosthetics such as arms and hands.
"Ever since the initial discoveries that brain signals could operate external devices, there has been a debate over the neuronal output required to operate such devices," said Nicolelis. "It is technically very difficult to record from many neurons and integrate the signals, so some in the field have advocated using smaller samples of neurons to operate devices.
"We have held that the number should be in the hundreds, while other groups have proposed that they could get by recording from ten or twenty neurons," he said. "This paper shows that such numbers would not work. A clinically relevant device --such as an arm that would produce continuous movement over many years -- would require sampling from much, much larger numbers of neurons than what has been proposed by other groups.
"Output from many cells is necessary, because there is fluctuation in output among individual neurons," said Nicolelis. "At a particular moment, some cells might give quite good output, while at other times, they would not be adequate. It's like taking a poll before an election. The question is how many people you need to take a reliable poll of who will win," he said.
"Our studies show the brain is continuously sampling from large ensembles of neurons to achieve a stable output, such as controlling a limb" said Nicolelis. "It samples from such a huge number of cells to enable it to remove noise and average the contributions of the network to achieve such stability."
According to Nicolelis, evolution has favored such redundancy, since it enables the brain to lose cells or to tolerate inconsistency in individual cells and still maintain a stable output.
Similarly, he said, more extensive electrode implants for neural prosthetics would make clinical sense because it would avoid subsequent surgeries necessary to re-implant electrodes if any malfunctioned, or if the cells became non-responsive.
"The technology already exists for such large electrode arrays, and the surgery need not be any more complex than implanting a heart pacemaker," said Nicolelis.
In their experiments, the researchers gave macaque monkeys a reaching task, in which the animals had to operate a joystick-like control to move a cursor over a target, in order to receive a juice reward. The researchers had implanted arrays of up to 64 hair-thin recording electrodes in the animals' brains.
In their analysis, the researchers systematically varied the number of neurons that they included in integrating to provide the output, and measured the impact on output stability. When few neurons were involved, they found, the output was unstable; but large ensembles of neurons yielded stable output.
In further studies, Nicolelis and his colleagues are exploring whether they can develop techniques to extract from the same ensemble of neurons multiple signals for controlling different components of a device -- such as different "muscles" in a robotic arm. It may be that such multiplicity of outputs can arise from the same ensemble, because different subpopulations of neurons contribute to different outputs, said Nicolelis.