Comparison of Signal Processing Methods for Reducing Motion Artifacts in High-Density Electromyography During Human Locomotion

Overview

Journal IEEE Open J Eng Med Biol

Publisher IEEE

Specialty Biomedical Engineering

Date 2022 Apr 11

PMID 35402949

Authors

Bryan R Schlink

Andrew D Nordin

Daniel P Ferris

Affiliations

Soon will be listed here.

Abstract

High-density electromyography (EMG) is useful for studying changes in myoelectric activity within a muscle during human movement, but it is prone to motion artifacts during locomotion. We compared canonical correlation analysis and principal component analysis methods for signal decomposition and component filtering with a traditional EMG high-pass filtering approach to quantify their relative performance at removing motion artifacts from high-density EMG of the gastrocnemius and tibialis anterior muscles during human walking and running. Canonical correlation analysis filtering provided a greater reduction in signal content at frequency bands associated with motion artifacts than either traditional high-pass filtering or principal component analysis filtering. Canonical correlation analysis filtering also minimized signal reduction at frequency bands expected to consist of true myoelectric signal. Canonical correlation analysis filtering appears to outperform a standard high-pass filter and principal component analysis filter in cleaning high-density EMG collected during fast walking or running.

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