Selective stimulation on nerve fibers by using high-frequency blocking technique

Chih-Wei Peng, Jia Jin Jason Chen, Chou Ching K Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traditional neural stimulation method recruits the muscle nerves in a reverse order of physiologic manner, i.e., can not recruit small diameter nerve fibers without recruiting large diameter ones. However, high frequency blocking technique is a feasible method for achieving selective stimulation and blocking nerve fibers. The aim of the study is to establish an animal model for studying order recruitment by using high frequency blocking investigation. In this study, a nerve cuff electrode was mounted on sciatic nerve with which two channel of stimulus was delivered for stimulation as well as for high frequency blocking investigation. Furthermore, a torque measurement system was established for assessing the stimulation or blocking performance in this study. Our results shows that varied levels of blocking current can produce gradual change in the blocking effect from 8% of residual torque to more than 90% of maximal torque output. In muscle fatigue test, our results proved that high frequency blocking technique could achieve selective stimulation of smaller nerve fibers and blocking of larger fibers.

Original languageEnglish
Title of host publicationInternational IEEE/EMBS Conference on Neural Engineering, NER
PublisherIEEE Computer Society
Pages630-632
Number of pages3
Volume2003-January
ISBN (Print)0780375793
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
Duration: Mar 20 2003Mar 22 2003

Other

Other1st International IEEE EMBS Conference on Neural Engineering
CountryItaly
CityCapri Island
Period3/20/033/22/03

Keywords

  • Animals
  • Corrosion
  • Electrical stimulation
  • Electrodes
  • Frequency
  • Muscles
  • Nerve fibers
  • Rabbits
  • Recruitment
  • Torque

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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