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Updated: Jun 9, 2026
09:36
Measurement of Spatial Stability in Precision Grip
Published on: June 4, 2020
Crosstalk effect on surface electromyogram of the forearm flexors during a static grip task
Yong-Ku Kong1, M Susan Hallbeck, Myung-Chul Jung
1Department of Systems Management Engineering, Sungkyunkwan University, Suwon 440-746, South Korea.
Summary
Surface electromyography (EMG) signals show high crosstalk, especially in wrist muscles. Blind signal separation (BSS) effectively reduces this crosstalk in forearm flexors during gripping tasks.
Area of Science:
- Biomechanics
- Neuroscience
- Signal Processing
Background:
- Surface electromyography (sEMG) is crucial for analyzing muscle activity.
- Crosstalk, or signal overlap between muscles, can confound sEMG analysis.
- Forearm flexors are susceptible to crosstalk during gripping tasks.
Purpose of the Study:
- To quantify crosstalk in digit- and wrist-dedicated forearm flexors during static power grip.
- To evaluate the effectiveness of a blind signal separation (BSS) algorithm in reducing sEMG crosstalk.
Main Methods:
- Six participants performed static power grip tasks at varying exertion levels (25-100% MVC).
- sEMG signals were recorded from five forearm flexor muscles using bipolar electrodes.
- Crosstalk was assessed using root mean square (RMS) and cross-correlation analysis.
- A BSS algorithm was applied to separate and reduce crosstalk.
Main Results:
- Crosstalk fractions ranged from 32-50% in wrist flexors and 11-25% in digit flexors.
- BSS significantly reduced overall forearm flexor crosstalk by 33%.
- Crosstalk levels varied with exertion and muscle group.
Conclusions:
- High levels of crosstalk are present in sEMG recordings from forearm flexors during gripping.
- BSS is an effective method for reducing sEMG crosstalk in these muscles.
- Employing BSS is recommended for accurate sEMG analysis of digit- and wrist-dedicated flexors.
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