Document Detail


Electrocorticographic control of a prosthetic arm in paralyzed patients.
MedLine Citation:
PMID:  22052728     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
OBJECTIVE: Paralyzed patients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzed patients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic. METHODS: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals. RESULTS: In all patients, the high gamma power (80-150Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm. INTERPRETATION: ECoG signals appear useful for prosthetic arm control and may provide clinically feasible motor restoration for patients with paralysis but no injury of the sensorimotor cortex. ANN NEUROL 2011;
Authors:
Takufumi Yanagisawa; Masayuki Hirata; Youichi Saitoh; Haruhiko Kishima; Kojiro Matsushita; Tetsu Goto; Ryohei Fukuma; Hiroshi Yokoi; Yukiyasu Kamitani; Toshiki Yoshimine
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-8-19
Journal Detail:
Title:  Annals of neurology     Volume:  -     ISSN:  1531-8249     ISO Abbreviation:  -     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2011-11-4     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7707449     Medline TA:  Ann Neurol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 American Neurological Association.
Affiliation:
Department of Neurosurgery, Osaka University Medical School, Osaka; ATR Computational Neuroscience Laboratories, Kyoto.
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