title: Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study.
abstract: METHODS
  Proof of concept study to control a neuroprosthesis for grasping using identification of arm movements from ECoG signals.
  
  
  OBJECTIVE
  To test the feasibility of using electrocorticographic (ECoG) signals as a control method for a neuroprosthesis for grasping.
  
  
  METHODS
  Acute care hospital, Toronto Western Hospital and spinal cord injury (SCI) rehabilitation centre, Toronto Rehabilitation Institute, Lyndhurst Centre. Both hospitals are located in Toronto, Canada.
  
  
  METHODS
  Two subjects participated in this study. The first subject had subdural electrodes implanted on the motor cortex for the treatment of essential tremor (ET). ECoG signals were recorded while the subject performed specific arm movements. The second subject had a complete SCI at C6 level (ASIA B score) and was fitted with a neuroprosthesis, capable of identifying arm movements from ECoG signals off-line, for grasping. To operate the neuroprosthesis, subject 2 issued a command that would trigger the release of a randomly selected ECoG signal recorded from subject 1, associated with a particular arm movement. The neuroprosthesis identified which arm movement was performed at the time of recording and used that information to trigger the stimulation sequence. A correct ECoG classification resulted in the neuroprosthesis producing the correct hand function (that is grasp and release).
  
  
  RESULTS
  The neuroprosthesis classified ECoG signals correctly delivering the correct stimulation strategy with 94.5% accuracy.
  
  
  CONCLUSIONS
  The feasibility of using ECoG signals as a control strategy for a neuroprosthesis for grasping was shown.
authors.affiliation: Rehabilitation Engineering Laboratory, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M4G 3V9.
authors.forename: C
authors.initials: C
authors.lastname: Márquez-Chin
authors.forename: M R
authors.initials: MR
authors.lastname: Popovic
authors.forename: T
authors.initials: T
authors.lastname: Cameron
authors.forename: A M
authors.initials: AM
authors.lastname: Lozano
authors.forename: R
authors.initials: R
authors.lastname: Chen
mesh_terms: D000328:Adult; D000368:Aged; D003201:Computers / Q000639:trends; D004599:Electric Stimulation Therapy / Q000295:instrumentation / Q000379:methods*; D004567:Electrodes, Implanted / Q000592:standards / Q000639:trends; D004582:Electronics, Medical / Q000295:instrumentation / Q000379:methods; D004594:Electrophysiology / Q000295:instrumentation / Q000379:methods*; D020329:Essential Tremor / Q000503:physiopathology / Q000534:rehabilitation; D005260:Female; D006225:Hand / Q000294:innervation / Q000502:physiology; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000502:physiology / Q000601:surgery; D009068:Movement / Q000502:physiology; D009069:Movement Disorders / Q000503:physiopathology / Q000534:rehabilitation*; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology; D010243:Paralysis / Q000503:physiopathology / Q000534:rehabilitation*; D019736:Prostheses and Implants / Q000639:trends*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013119:Spinal Cord Injuries / Q000503:physiopathology / Q000534:rehabilitation; D014584:User-Computer Interface