Towards a Brain-Computer Interface for Dexterous Control of a Multi-Fingered Prosthetic Hand
Acharya, S.
Aggarwal, V.
Tenore, F.
Hyun-Chool Shin
Etienne-Cummings, R.
Schieber, M.H.
Thakor, N.V.
Dept. of Biomed. Eng. & Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD;
This paper appears in: Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on
Publication Date: 2-5 May 2007
On page(s): 200-203
Location: Kohala Coast, HI,
ISBN: 1-4244-0792-3
INSPEC Accession Number: 9518665
Digital Object Identifier: 10.1109/CNE.2007.369646
Current Version Published: 2007-06-11
Abstract
Advances in brain-computer interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (Ml) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by randomly selecting contiguous subpopulations of these neurons. Non-linear hierarchical filters using artificial neural networks (ANNs) were designed to asynchronously decode the activity from multiple virtual ensembles, in real-time. The decoded output was then used to actuate individual fingers of a robotic hand. An average real-time decoding accuracy of greater than 95 % was achieved with all neurons from randomly placed voxels containing 48 neurons, and up to 80% with as few as 25 neurons. These results suggest that dexterous control of individual digits and wrist of a prosthetic hand can be achieved by real-time decoding of neuronal ensembles from the Ml hand area in primates.
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