Classification of Sensory Neural Information from Rat Spinal Cord Using Principal Component Analysis

Title
Classification of Sensory Neural Information from Rat Spinal Cord Using Principal Component Analysis
Authors
한성민추준욱최귀원박종웅윤인찬
Keywords
Spinal cord; sensory signal; classification
Issue Date
2013-08
Publisher
The 7th Asian Pacific Conference on Biomechanics
Abstract
The brain-computer interface (BCI) technique is a one of the treatment for spinal cord injury (SCI). In the last 15 years, BCI research has grown rapidly. This technique is derived from the idea which is the communication method of neurons. The neurons are commanded by electrical signal through the axons and at this time of electrical signal called action potential (AP). The key to success of BCI depends on effective recording from neural signal activity of brain cortex. BCI technique has also progressed to provide alternative treatment method of SCI using neural signal recording and stimulating from the spinal cord. In this study, the microelectrode array was implanted at the dorsal horn of lumbar L5 and evoked filed potentials were directly recorded from neural activities of sensory events. The sensory events were generated by electrical stimulation on the 3 different sites of left hind paw. Signal features were extracted from each sensory events based on post stimulus time histogram. The feature vectors were projected to the feature spaces using principal component analysis and clustering was classified from neural activities of sensory events.
URI
http://pubs.kist.re.kr/handle/201004/45798
Appears in Collections:
KIST Publication > Conference Paper
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