Crosstalk effect on surface electromyogram of the forearm flexors during a static grip task

Yong Ku Kong, M. Susan Hallbeck, Myung Chul Jung

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

The fraction of crosstalk was examined from the surface EMG signals collected from digit- and wrist-dedicated flexors with a blind signal separation (BSS) algorithm. Six participants performed static power grip tasks in a neutral posture at four different exertion levels of 25%, 50%, 75%, and 100% MVC. The signals were collected from the flexor digitorum superficialis, flexor digitorum profundus, flexor carpi radialis, palmaris longus, and flexor carpi ulnaris using a bipolar electrode configuration. The percentage of root mean square (RMS) was used as an amplitude-based index of crosstalk by normalizing the signals including crosstalk to those excluding crosstalk by the BSS algorithm for each %MVC exertion. The peak R2 value of a cross-correlation function was also calculated as a correlation-based index of crosstalk for a group of forearm flexors by force level and algorithm application. The fraction of crosstalk ranged from 32% to 50% in the wrist-dedicated flexors and from 11% to 25% in the digit-dedicated flexors. Since surface EMG signals had such high levels of crosstalk, reduction methods like the BSS algorithm should be employed, as the BSS significantly reduced crosstalk in the forearm flexors 33% over all muscles and exertion levels. Thus, it is recommended that BSS be utilized to reduce crosstalk for the digit- and wrist-dedicated flexors during gripping tasks.

Original languageEnglish (US)
Pages (from-to)1223-1229
Number of pages7
JournalJournal of Electromyography and Kinesiology
Volume20
Issue number6
DOIs
StatePublished - Dec 1 2010

Keywords

  • Blind signal separation (BSS) algorithm
  • Crosstalk
  • Forearm flexors
  • Power grip task

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biophysics
  • Clinical Neurology

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