A woman with quadriplegia feeds herself chocolate using mind-controlled robot arm.
Using a sophisticated brain-computer interface, a woman paralyzed from the neck down learned to control a robotic prosthetic arm with her thoughts and perform several activities of daily living.
With training, she rapidly learned to use the robotic arm to reach and grasp items and made significant gains in upper limb function. She learned to do maneuvers "with coordination, skill and speed almost similar to that of an able-bodied person," according to the research team.
"This is a spectacular leap toward greater function and independence for people who are unable to move their own arms," senior investigator Andrew B. Schwartz, PhD, professor, Department of Neurobiology, University of Pittsburgh School of Medicine, Pennsylvania, said in a statement.
"This technology, which interprets brain signals to guide a robot arm, has enormous potential that we are continuing to explore. Our study has shown us that it is technically feasible to restore ability," he added.
The achievement is detailed in a paper published online December 16 in the Lancet.
Robust 7-D Movement Mastered
The participant was a 52-year-old woman with chronic tetraplegia due to spinocerebellar degeneration. By using stereotactic image guidance with structural and functional MRI, 2 microelectrodes were implanted in her left motor cortex, which allowed researchers to pinpoint and record neuronal activity when the woman was asked to imagine using her hand and arm.
The electrode points pick up signals from individual neurons, and computer algorithms are used to identify the firing patterns associated with particular observed or imagined movements, such as raising or lowering the arm, or turning the wrist, the researchers explained in a Webcast. Two cables run from connectors on the participant's head to the recording apparatus and another cable from the computer to the prosthetic arm mounted on a stand next to the woman.
The woman participated in brain-computer interface training for 13 weeks. On the second day of training, the woman was able to move the robotic arm freely in the 3-dimensional workspace. After 13 weeks, she could routinely perform "robust" 7-dimensional movements, the researchers report.
On average, her success rate on target-based reaching tasks was 91.6%. Over time, she developed fluid and rapid control over skillful prosthetic arm movements. She could accurately reach for objects, adjust the opening of the prosthetic hand to grasp items of various shapes and sizes, and move them to any desired location in the workspace.
The woman also achieved "clinically significant gains on standard tests of upper limb function," the researchers say. No adverse events occurred.
Radically Different Approach
In a statement, Dr. Schwartz notes that one of the biggest challenges in developing mind-controlled prosthetics is how to translate brain signals that indicate limb movement into computer signals that can reliably and accurately control a robotic prosthesis.
"Most mind-controlled prosthetics have achieved this by an algorithm which involves working through a complex 'library' of computer-brain connections," he explained. His team pursued a radically different approach. They used a model-based computer algorithm that closely mimics the way that an unimpaired brain controls limb movement. "The result is a prosthetic hand which can be moved far more accurately and naturalistically than previous efforts," Dr. Schwartz said.
The authors of a linked comment note that the control of the robotic arm movements that the woman was able to achieve with training was "highly intuitive, and probably responsible for the unprecedented performance of the developed brain-machine interfaces."
"This bioinspired brain-machine interface is a remarkable technological and biomedical achievement," add Grégoire Courtine, PhD, from the Swiss Federal Institute of Technology Lausanne, Switzerland, and coauthors.
"(Although many obstacles remain, neural prosthetic systems are rapidly approaching clinical fruition. Through concerted efforts, combining several strategies, translational neuroprosthetics might soon revolutionary treatment models for sensorimotor paralysis," they say.
Dr. Schwartz and colleagues say next steps in improving this type of thought-controlled prosthetic include incorporating sensory elements — so that the patient might, for instance, be able to tell the difference between hot and cold, or smooth and coarse, surfaces — and also to incorporate wireless technology, removing the need for connecting wires between the patient's head and their prosthesis.
Funding for the project was provided by the Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Two of the investigators have a patent application pending in the United States that covers some of the methods used in this study. The authors and the comment authors have disclosed no relevant financial relationships.
Lancet. Published online December 16, 2012
"This is a spectacular leap toward greater function and independence for people who are unable to move their own arms," senior investigator Andrew B. Schwartz, PhD, professor, Department of Neurobiology, University of Pittsburgh School of Medicine, Pennsylvania, said in a statement.
"This technology, which interprets brain signals to guide a robot arm, has enormous potential that we are continuing to explore. Our study has shown us that it is technically feasible to restore ability," he added.
The achievement is detailed in a paper published online December 16 in the Lancet.
Robust 7-D Movement Mastered
The participant was a 52-year-old woman with chronic tetraplegia due to spinocerebellar degeneration. By using stereotactic image guidance with structural and functional MRI, 2 microelectrodes were implanted in her left motor cortex, which allowed researchers to pinpoint and record neuronal activity when the woman was asked to imagine using her hand and arm.
The electrode points pick up signals from individual neurons, and computer algorithms are used to identify the firing patterns associated with particular observed or imagined movements, such as raising or lowering the arm, or turning the wrist, the researchers explained in a Webcast. Two cables run from connectors on the participant's head to the recording apparatus and another cable from the computer to the prosthetic arm mounted on a stand next to the woman.
The woman participated in brain-computer interface training for 13 weeks. On the second day of training, the woman was able to move the robotic arm freely in the 3-dimensional workspace. After 13 weeks, she could routinely perform "robust" 7-dimensional movements, the researchers report.
The woman also achieved "clinically significant gains on standard tests of upper limb function," the researchers say. No adverse events occurred.
Radically Different Approach
In a statement, Dr. Schwartz notes that one of the biggest challenges in developing mind-controlled prosthetics is how to translate brain signals that indicate limb movement into computer signals that can reliably and accurately control a robotic prosthesis.
"Most mind-controlled prosthetics have achieved this by an algorithm which involves working through a complex 'library' of computer-brain connections," he explained. His team pursued a radically different approach. They used a model-based computer algorithm that closely mimics the way that an unimpaired brain controls limb movement. "The result is a prosthetic hand which can be moved far more accurately and naturalistically than previous efforts," Dr. Schwartz said.
The authors of a linked comment note that the control of the robotic arm movements that the woman was able to achieve with training was "highly intuitive, and probably responsible for the unprecedented performance of the developed brain-machine interfaces."
"This bioinspired brain-machine interface is a remarkable technological and biomedical achievement," add Grégoire Courtine, PhD, from the Swiss Federal Institute of Technology Lausanne, Switzerland, and coauthors.
"(Although many obstacles remain, neural prosthetic systems are rapidly approaching clinical fruition. Through concerted efforts, combining several strategies, translational neuroprosthetics might soon revolutionary treatment models for sensorimotor paralysis," they say.
Dr. Schwartz and colleagues say next steps in improving this type of thought-controlled prosthetic include incorporating sensory elements — so that the patient might, for instance, be able to tell the difference between hot and cold, or smooth and coarse, surfaces — and also to incorporate wireless technology, removing the need for connecting wires between the patient's head and their prosthesis.
Funding for the project was provided by the Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Two of the investigators have a patent application pending in the United States that covers some of the methods used in this study. The authors and the comment authors have disclosed no relevant financial relationships.
Lancet. Published online December 16, 2012
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