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Updated: May 15, 2026

Extraction of the EPP Component from the Surface EMG

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

Filtering of surface EMG using ensemble empirical mode decomposition.

Xu Zhang¹, Ping Zhou

¹Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.

Medical Engineering & Physics

|December 19, 2012

Summary

This study introduces empirical mode decomposition (EMD) methods for cleaning surface electromyogram (EMG) signals. Ensemble EMD (EEMD) effectively removes noise like power line interference, white Gaussian noise, and baseline wandering, outperforming traditional filters.

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Area of Science:

Biomedical Engineering
Signal Processing

Background:

Surface electromyogram (EMG) signals are frequently contaminated by power line interference (PLI), white Gaussian noise (WGN), and baseline wandering (BW).
Effective noise reduction is crucial for accurate analysis of EMG signals in various applications.

Purpose of the Study:

To develop and evaluate a novel framework for reducing multiple noise types from surface EMG signals.
To compare the performance of Empirical Mode Decomposition (EMD) based methods against traditional digital filters for EMG denoising.

Main Methods:

A novel framework utilizing Empirical Mode Decomposition (EMD) was developed for surface EMG denoising.
Ensemble Empirical Mode Decomposition (EEMD) was specifically examined as an advanced denoising technique.
EMD-based methods were compared with conventional digital filters using routine electrode array surface EMG recordings.

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Last Updated: May 15, 2026

Extraction of the EPP Component from the Surface EMG

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Published on: December 16, 2009

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Main Results:

EMD-based methods demonstrated superior performance in reducing noise contamination compared to traditional digital filters.
The denoising effectiveness was particularly pronounced in low signal-to-noise ratio (SNR) conditions.
The Ensemble Empirical Mode Decomposition (EEMD) approach yielded the best overall surface EMG denoising results among the methods tested.

Conclusions:

EMD-based techniques offer a robust and effective solution for cleaning surface EMG signals.
EEMD presents a highly promising method for achieving high-quality EMG signal denoising.
These findings support the use of EMD-based approaches in applications requiring clean EMG data.