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

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## Abstract

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## 1. Introduction

#### 1.1. Review Objectives and Research Strategy

#### 1.2. Content of the Article

## 2. Contaminant Types

#### Other Terminology Considerations

## 3. Contaminant Reduction Methods

#### 3.1. Conventional Digital Filters

**Low-pass filters:**As mentioned earlier, the sEMG spectrum comprises components of up to 500 Hz. Therefore, any signal with a frequency greater than 500 Hz is considered noise. Usually, this noise has the characteristics of white Gaussian noise. A low-pass filter with a cutoff frequency between 400 and 500 Hz is nearly always used to remove this noise. The cutoff frequency is not critical and can effectively be lower than the maximum frequency of the EMG signals because the portion of its energy over 350 Hz is very low [6].

**High-pass filters:**The high-pass filter’s cutoff frequency is more critical here because some EMG contaminants have overlapping spectra. For example, motion artifacts caused by movement of the body can be up to 20 Hz, while artifacts of up to 50 Hz can be caused by cables. To filter these contaminants most effectively without compromising the EMG signal itself, researchers have recommended several different cutoff frequencies (from 5–30 Hz) [20,21,22,23]. However, common ground has not been clearly established, likely because the optimal value depends on the application. More recently, ref. [10] compared three cutoff frequencies (10–20–30 Hz) used to filter motion artifacts and background noise from sEMG signals. They concluded that the cutoff frequency used should be based on both the application and the muscle studied. However, they also stated that cutoff frequencies lower than 20 Hz are not recommended, arguing that the proportion of the EMG signal is negligible compared to noise for frequencies below 20 Hz. On the other hand, ref. [24] highlighted that cutoff frequencies greater than 20 Hz may not be appropriate for fatigue analysis. Ref. [10] also recommended a minimal filter order of 2, which seems to be more generally accepted in the literature.

**Band-stop filters:**PLI is a principal contaminant of sEMG. Although some researchers argue that it can be assessed directly using the right sensors, it may still contribute to signal contamination. Further, in some cases, the use of active sensors is not always possible. The traditional means of removing PLI after acquisition is to use a narrow digital band-stop filter, such as a notch filter centered at 50 Hz or 60 Hz. As the name suggests, this kind of filter introduces a “notch” in the signal’s spectrum. Multiple notches can also be applied to remove its harmonics. An alternative to using multiple notch filters is the comb filter, which creates narrow rejection bands at every harmonic frequency [25]. However, the use of comb filters is not often reported in the literature. Band-stop filters (20–40 Hz) have also been reported in the literature to remove ECG by focusing on its maximum energy band [26].

#### 3.2. Gating and Clipping Methods

#### 3.3. Subtraction Methods in the Time Domain

#### 3.3.1. Estimation of PLI Using the Regression Method on a Reference Signal

#### 3.3.2. Estimation of PLI Using Spectral Analysis on a Reference Signal

#### 3.3.3. Estimation of PLI Using the Least Squares Algorithm on the Signal Itself

#### 3.3.4. Template Estimation of the ECG Interference Signal

#### 3.3.5. Adaptive Estimation of the Interference Signal by Means of Filtering the Raw Signal

#### 3.3.6. Adaptive Noise Canceller (ANC)

#### 3.3.7. Nonlinear ANC

#### 3.4. Denoising Methods after Signal Decomposition

#### 3.4.1. Decomposition Methods after Fourier Decomposition

#### 3.4.2. Denoising Methods after Wavelet Decomposition

**Step 1. Decomposition using wavelet transform**: While the Fourier transform decomposes the signal from the time domain into the frequency domain, the wavelet transform generates components (kernels) that are defined in terms of both time and frequency. In this approach, the components are created by translating and dilating a fixed function called the mother wavelet ($\Psi \left(t\right)$, thus allowing a multi-dimensional representation of the signal. Unlike the sine/cosine waves used in the Fourier transform, the amplitude of the mother wavelet varies across its length. Translating it over the signal thus allows the definition of each component in time.

**Step 2. Denoising in the wavelet domain:**As for all decomposition methods, once decomposition is completed, the signal is denoised in the decomposition domain. While the block diagram of wavelet denoising methods is similar to those based on the Fourier transform and cosine transform, a considerable amount of research has focused on wavelet denoising in the context of WGN removal. Therefore, instead of simply subtracting the coefficients obtained with the noisy signal from those of the trial, the denoising stage is usually performed by applying a threshold to each of the coefficients, as proposed by [91]. Thus, this stage is separated into two main steps: 1. threshold selection and 2. application of the thresholds.

**Step 3. Reconstruction using the modified coefficients**: The modified coefficients are then used to reconstruct the signal in the time domain using the inverse wavelet transform (IWT).

#### 3.4.3. Denoising after Empirical Mode Decomposition (EMD)

#### 3.4.4. Denoising after Variational Mode Decomposition (VMD)

#### 3.5. Combining Methods and Hybrid Methods

#### 3.5.1. Wavelet-ICA and EMD-ICA

#### 3.5.2. Wavelet-Adaptive

#### 3.5.3. Wavelet-Wiener and FFT-Wiener

## 4. Performance Evaluation

## 5. Discussion

#### 5.1. Contaminant Type to Be Eliminated

#### 5.2. Possibility to Be Used in Real-Time

#### 5.3. Adaptivity of the Methods

#### 5.4. Complementarity of the Methods

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ANC | Adaptive noise canceller |

ANFIS | Adaptive neuro fuzzy inference system |

BL | Baseline noise |

BN | Background noise |

BPN | Back propagation network |

BSS | Blind source separation |

BW | Baseline wander |

CCA | Canonical correlation analysis |

CCN | Cascade correlation network |

CEEMD | Complementary ensemble empirical mode decomposition |

CWT | Continuous wavelet transform |

DCT | Discrete cosine transform |

DPR | Maximum drop in power density |

DWT | Discrete wavelet transform |

ECG | Electrocardiographic signal |

EEMD | Ensemble empirical mode decomposition |

EMD | Empirical mode decomposition |

EMG | Electromyography |

ESAIC | Event-synchronous adaptive interference canceller |

FIR | Finite impulse response |

FT | Fourier transform |

HD-EMG | High-dimension Electromyography |

ICA | Independent component analysis |

IMFs | Intrinsic mode functions |

IT | Interval thresholding |

IWT | Inverse wavelet transform |

LMS | Least mean squares |

MA | Motion artifacts |

PCA | Principal component analysis |

PLI | Power-line interference |

RLS | Recursive least square |

SCICA | Single channel independent component analysis |

sEMG | Surface Electromyography |

SER | Signal-to-ECG ratio |

SIT | Soft interval thresholding |

SMR | Signal-to-motion artifact ratio |

SNR | Signal-to-noise ratio |

SPR | Signal-to-powerline ratio |

SWT | Stationary wavelet transform |

VMD | Variational mode decomposition |

VMFs | Variational mode functions |

WGN | White Gaussian noise |

WPT | Wavelet packet transform |

WT | Wavelet transform |

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

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**MDPI and ACS Style**

Boyer, M.; Bouyer, L.; Roy, J.-S.; Campeau-Lecours, A.
Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review. *Sensors* **2023**, *23*, 2927.
https://doi.org/10.3390/s23062927

**AMA Style**

Boyer M, Bouyer L, Roy J-S, Campeau-Lecours A.
Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review. *Sensors*. 2023; 23(6):2927.
https://doi.org/10.3390/s23062927

**Chicago/Turabian Style**

Boyer, Marianne, Laurent Bouyer, Jean-Sébastien Roy, and Alexandre Campeau-Lecours.
2023. "Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review" *Sensors* 23, no. 6: 2927.
https://doi.org/10.3390/s23062927