Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

Abstract

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.

Keywords: artifact; contaminant reduction; denoising; electromyography; filtering; interference; noise; signal processing.

Figure 1

Power spectrum of the EMG signal (A) and of some of its contaminants: power line interference (B), motion artifact (C), electrocardiographic signal (D) and baseline noise (E).

Figure 2

Baseline noise power spectrum.

Figure 3

Principle of the subtraction methods in the time domain.

Figure 4

General block diagram of an interference reduction method using an adaptive estimation of the interference signal by means of filtering the raw signal.

Figure 5

Block diagram of the classic adaptive noise canceller.

Figure 6

Block diagram of the adaptive noise canceller along with the measured signal composition.

Figure 7

Block diagram of the decomposition methods.

Figure 8

General scheme of the method proposed by [17] to remove background noise from the EMG signal: 1. Estimation of the power spectrum coefficients of the Background noise by performing a fast Fourier transform (FFT) on the noisy signal (the electrode is placed on the skin, but the muscle is not contracted), 2. Estimation of the power spectrum coefficients of the measured signal during contraction using the FFT, 3. Subtraction of the noise coefficients from the measured coefficients and 4. Reconstruction of the signal using the inverse Fourier Transform.

Figure 9

(A) Filter bank resulting from a a DWT at level 3 of decomposition and (B) the resulting coefficients of the DWT. The coefficients/components used in the DWT are presented in grey.

Figure 10

(A) Resulting filter bank of a WPT at level 3 of decomposition along with (B) resulting coefficients of the WPT. The coefficients/components used in WPT are presented in grey.

Figure 11

Output coefficient obtained according to the input coefficient for HAD (left) and SOF (right) functions.

Figure 12

Block diagram of Wavelet-ICA.

Figure 13

Block diagram of adaptive filtering using wavelet transform on the raw signal to estimate the interference.