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Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study
Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study
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Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study
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Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study
Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study

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Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study
Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study
Journal Article

Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study

2009
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Overview
Study design: 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. Setting: 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.