Detection and classification of sensory information from acute spinal cord recordings

One avenue of research for partial restoration of function following spinal cord injury is the use of neural prostheses, an example of which is functional electrical stimulation (FES) devices for motor functions. Neural prostheses may also be useful for the extraction of sensory information directly...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2006-08, Vol.53 (8), p.1715-1719
Main Authors: Borisoff, J.F., McPhail, L.T., Saunders, J.T.W., Birch, G.E., Ramer, M.S.
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Algorithms
Animals
Brain interface
Classification
Computer Simulation
Data mining
Devices
Electric Stimulation
Electrical stimulation
Extracellular
functional electrical stimulation
Information Storage and Retrieval - methods
Male
Models, Neurological
Multichannel
Nervous system
Neural prostheses
neural prosthesis
Neuromuscular stimulation
Neurons, Afferent - physiology
Posterior Horn Cells - physiology
Prosthetics
Rats
Rats, Sprague-Dawley
Recording
Restoration
Spinal cord
Spinal Cord - physiology
Spinal cord injuries
Spinal cord injury
Stimulation
Testing
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Summary:One avenue of research for partial restoration of function following spinal cord injury is the use of neural prostheses, an example of which is functional electrical stimulation (FES) devices for motor functions. Neural prostheses may also be useful for the extraction of sensory information directly from the nervous system. We suggest the spinal cord as a possible site for the detection of peripheral sensory information from neural activity alone. Acute multichannel extracellular recordings were used to extract neural spike activity elicited from peripheral sensations from the spinal cords of rats. To test the recording method and classification potential, eight classes of sensory events were recorded consisting of electrical stimulation of seven locations on rat forepaws, and another class of data during which no stimulus was present. A dual-stage classification scheme using principal component analysis and k-Means clustering was devised to classify the sensory events during single trials. The eight tasks were correctly identified at a mean accuracy of 96%. Thus, we have shown the methodology to detect and classify peripheral sensory information from multichannel recordings of the spinal cord. These methods may be useful, for example, in a closed-loop FES for restoration of hand grasp
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2006.877800