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Assistive technology and robotic control using MI ensemble-based neural interface systems in humans with tetraplegia

2007

Article

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This review describes the rationale, early stage development, and initial human application of neural interface systems (NISs) for humans with paralysis. NISs are emerging medical devices designed to allowpersonswith paralysis to operate assistive technologies or to reanimatemuscles based upon a command signal that is obtained directly fromthe brain. Such systems require the development of sensors to detect brain signals, decoders to transformneural activity signals into a useful command, and an interface for the user.We review initial pilot trial results of an NIS that is based on an intracortical microelectrode sensor that derives control signals from the motor cortex.We review recent findings showing, first, that neurons engaged by movement intentions persist in motor cortex years after injury or disease to the motor system, and second, that signals derived from motor cortex can be used by persons with paralysis to operate a range of devices. We suggest that, with further development, this form of NIS holds promise as a useful new neurotechnology for those with limited motor function or communication.We also discuss the additional potential for neural sensors to be used in the diagnosis and management of various neurological conditions and as a new way to learn about human brain function.

Author(s): Donoghue, J. P. and Nurmikko, A. and Black, M., J. and Hochberg, L.
Journal: Journal of Physiology, Special Issue on Brain Computer Interfaces
Volume: 579
Pages: 603--611
Year: 2007

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

DOI: 10.1113/jphysiol.2006.127209

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BibTex

@article{Donoghue:JoP:07,
  title = {Assistive technology and robotic control using {MI} ensemble-based neural interface systems in humans with tetraplegia},
  author = {Donoghue, J. P. and Nurmikko, A. and Black, M., J. and Hochberg, L.},
  journal = {Journal of Physiology, Special Issue on Brain Computer Interfaces},
  volume = {579},
  pages = {603--611},
  year = {2007}
}