Implantable brain–computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury
| Autor: | Lauren Zimmerman, Noeline W Prins, Steven Vanni, John H. Abel, Benyamin Meschede-Krasa, Audrey Wilson, Katie Gant, Allan D. Levi, Sebastian Gallo, Brandon Parks, Jasim Ahmad Naeem, John Tauber, Iahn Cajigas, Michael E. Ivan, Santiago Guerra, W. Dalton Dietrich, Indie C. Garwood, Jonathan R. Jagid, Emery N. Brown, Letitia Fisher, Kevin Davis, Anne E. Palermo, Abhishek Prasad |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
cervical quadriplegia
business.industry AcademicSubjects/SCI01870 Hand grasp brain–computer interface medicine.disease spinal cord injury functional electrical stimulation Cellular and Molecular Neuroscience Psychiatry and Mental health Editor's Choice Neurology medicine Original Article AcademicSubjects/MED00310 business Spinal cord injury Neuroscience Biological Psychiatry Brain–computer interface electrocorticography |
| Zdroj: | Brain Communications |
| ISSN: | 2632-1297 |
| Popis: | Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain–computer interface within the home environment. The brain–computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-weeks in-laboratory study with a mean accuracy of 89.0% (range 78–93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80–98.95%) and an accuracy of 88.3% (range 77.6–95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain–computer interface can be safely used to reliably decode movement-intent from motor cortex, allowing for accurate volitional control of hand grasp. Brain–computer interfaces, which translate internal electrical signals related to desired hand movement may be used to restore independence to patients with paralysis. In this work, Cajigas et al. demonstrate that a fully implanted interface can reliably decode motor commands using electrocorticography and can be deployed for home use. Such systems may provide means for self-driven portable rehabilitation and assistance. Graphical Abstract Graphical Abstract |
| Databáze: | OpenAIRE |
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