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Neural repair and rehabilitation

Achieving complex control of a neuroprosthetic arm

“One small nibble for a woman, one giant bite for BCI.” This was Jan Scheuermann's reaction to her new-found ability to guide a chocolate bar to her mouth via a brain–computer interface (BCI)-controlled robotic arm. The 53-year-old, who had long-standing tetraplegia as a result of spinocerebellar degeneration, was able to perform complex, coordinated movements of the neuroprosthetic arm after only 13 weeks of training.

In a study conducted at the University of Pittsburgh Medical Center (UPMC), PA, USA, two 96-channel microelectrode arrays were implanted into Ms Scheuermann's motor cortex. The microelectrodes recorded neuronal activity associated with specific imagined movements: according to lead investigator Jennifer Collinger, “we could actually see the neurons fire on the computer screen when she thought about closing her hand.” This activity was then translated into signals that could be used to drive a robotic arm with seven degrees of freedom of movement.

Jan Scheuermann takes a bite out of a chocolate bar she has guided into her mouth with a thought-controlled robotic arm. Research assistants Brian Wodlinger and Elke Brown watch in the background. Photo credit: UPMC.

Previous research has demonstrated the capacity of BCI-based systems to generate grasping movements in a neuroprosthetic limb. Collinger and colleagues have now shown that an individual with tetraplegia can, within a relatively short space of time, learn to control a robotic arm to accomplish a wide range of tasks with direct relevance to activities of daily living.

“This is a spectacular leap toward greater function and independence for people who are unable to move their own arms,” says senior investigator Andrew Schwartz. “We're hoping this can become a fully implanted, wireless system that people can actually use in their homes without our supervision,” adds Collinger.

Possible future developments in BCI technology include two-way systems that provide sensory feedback to enable more-refined movement of the neuroprosthesis, as well as devices that directly stimulate an individual's own limb muscles.

References

ORIGINAL RESEARCH PAPER

  1. Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet doi:10.1016/S0140-6736(12)61816-9

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FURTHER READINGS

  1. Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012)

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  2. Jackson, A. & Zimmermann, J. B. Neural interfaces for the brain and spinal cord—restoring motor function. Nat. Rev. Neurol. 8, 690–699 (2012)

    CAS  Article  Google Scholar 

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Wood, H. Achieving complex control of a neuroprosthetic arm. Nat Rev Neurol 9, 62 (2013). https://doi.org/10.1038/nrneurol.2013.1

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  • DOI: https://doi.org/10.1038/nrneurol.2013.1

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