A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings

Objective.Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters.Approach.Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients.Main result.Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data.Significance.Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Analysis and applications of respiratory surface EMG: report of a round table meeting

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

Morphological ECG subtraction method for removing ECG artifacts from diaphragm EMG

BACKGROUND

Diaphragmatic electromyographic (EMGdi) is a helpful method to reflect the respiratory center's activity visually. However, the electrocardiogram (ECG) severely affected its weakness, limiting its use.

OBJECTIVE

To remove the ECG artifact from the EMGdi, we designed a Morphological ECG subtraction method (MES) based on three steps: 1) ECG localization, 2) morphological tracking, and 3) ECG subtractor.

METHODS

We evaluated the MES method against the wavelet-based dual-threshold and stationary wavelet filters using visual and frequency-domain characteristics (median frequency and power ratio).

RESULTS

The results show that the MES method can preserve the features of the original diaphragm signal for both surface diaphragm signal (SEMGdi) and clinical collection of diaphragm signal (EMGdi_clinic), and it is more effective than the wavelet-based dual-threshold and stationary wavelet filtering methods.

CONCLUSION

The MES method is more effective than other methods. This technique may improve respiratory monitoring and assisted ventilation in patients with respiratory diseases.

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

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.

A DENOISING METHOD OF DIAPHRAGM ELECTROMYOGRAM SIGNALS BASED ON DUAL-THRESHOLD FILTER

Diaphragmatic electromyography (EMGdi) signals can effectively reflect human respiratory process and effort, but simultaneously interfered by electrocardiogram (ECG) signals, leading to the limited application of EMGdi signals. Conventional denoising schemes for ECG cancellation from EMGdi signals are usually limited to the cases of irregular ECG signals. For this purpose, this paper proposes a denoising method of ECG signals by using a dual-threshold filter, which performs effectively in the scenarios of irregular ECG condition, such as arrhythmia. Specifically, we first employ wavelet transform to decompose EMGdi signals to wavelets of different frequencies, by which QRS complex detection is performed to determine the location of ECG signals. Then we propose to remove the ECG interference by reforming the interference range with the adjacent signals. Experimental results indicate that the proposed denoising method performs superior to the state-of-the-art schemes, especially for the cases of weak EMGdi signal scenarios.

ECG Signal Denoising and Reconstruction Based on Basis Pursuit

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

Recruitment pattern of the diaphragm and extradiaphragmatic inspiratory muscles in response to different levels of pressure support

Background

Inappropriate ventilator assist plays an important role in the development of diaphragm dysfunction. Ventilator under-assist may lead to muscle injury, while over-assist may result in muscle atrophy. This provides a good rationale to monitor respiratory drive in ventilated patients. Respiratory drive can be monitored by a nasogastric catheter, either with esophageal balloon to determine muscular pressure (gold standard) or with electrodes to measure electrical activity of the diaphragm. A disadvantage is that both techniques are invasive. Therefore, it is interesting to investigate the role of surrogate markers for respiratory dive, such as extradiaphragmatic inspiratory muscle activity. The aim of the current study was to investigate the effect of different inspiratory support levels on the recruitment pattern of extradiaphragmatic inspiratory muscles with respect to the diaphragm and to evaluate agreement between activity of extradiaphragmatic inspiratory muscles and the diaphragm.

Methods

Activity from the alae nasi, genioglossus, scalene, sternocleidomastoid and parasternal intercostals was recorded using surface electrodes. Electrical activity of the diaphragm was measured using a multi-electrode nasogastric catheter. Pressure support (PS) levels were reduced from 15 to 3 cmH₂O every 5 min with steps of 3 cmH₂O. The magnitude and timing of respiratory muscle activity were assessed.

Results

We included 17 ventilated patients. Diaphragm and extradiaphragmatic inspiratory muscle activity increased in response to lower PS levels (36 ± 6% increase for the diaphragm, 30 ± 6% parasternal intercostals, 41 ± 6% scalene, 40 ± 8% sternocleidomastoid, 43 ± 6% alae nasi and 30 ± 6% genioglossus). Changes in diaphragm activity correlated best with changes in alae nasi activity (r² = 0.49; P < 0.001), while there was no correlation between diaphragm and sternocleidomastoid activity. The agreement between diaphragm and extradiaphragmatic inspiratory muscle activity was low due to a high individual variability. Onset of alae nasi activity preceded the onset of all other muscles.

Conclusions

Extradiaphragmatic inspiratory muscle activity increases in response to lower inspiratory support levels. However, there is a poor correlation and agreement with the change in diaphragm activity, limiting the use of surface electromyography (EMG) recordings of extradiaphragmatic inspiratory muscles as a surrogate for electrical activity of the diaphragm.

Assessment of respiratory drive with esophageal diaphragmatic electromyography in patients with acute respiratory distress syndrome treated with prone position ventilation

BACKGROUND

Prone position ventilation (PPV) is an important strategy for patients with severe acute respiratory distress syndrome (ARDS). This prospective study investigated the use of electromyography of the diaphragm (EMGdi) for monitoring respiratory drive in patients with moderate to severe ARDS during long-term PPV.

METHODS

An integrated nostril-gastric feeding tube containing an esophageal electrode and balloon was placed in 14 patients with severe ARDS prior to PPV. EMGdi and trans-pulmonary pressure (∆P_L) data were collected before PPV (baseline), every 2 h during PPV, and 2 h after the restoration of supine position ventilation (post-2 h SPV).

RESULTS

In ARDS patients, the static compliance of the chest wall was significantly decreased after PPV. EMGdi levels were slightly lower in the early, middle, and late stages of PPV compared with baseline. Patients who received neuromuscular blocker experienced a greater drop in EMGdi from baseline than those who did not.

CONCLUSIONS

For ARDS patients, EMGdi was slightly decreased after prolonged PPV. This is contrary to the change in diaphragm electromyography during normal body position changes. Monitoring EMGdi regularly during PPV in ARDS patients is feasible and can be used as a reference for lung protective ventilation strategies.

Processing transcutaneous electromyography measurements of respiratory muscles, a review of analysis techniques

Transcutaneous electromyography (tc-EMG) has been used to measure the electrical activity of respiratory muscles during inspiration in various studies. Processing the raw tc-EMG signal of these inspiratory muscles has shown to be difficult as baseline noise, cardiac interference, cross-talk and motion artefacts can influence the signal quality. In this review we will discuss the most important sources of signal noise in tc-EMG of respiratory muscles and the various techniques described to suppress or reduce this signal noise. Furthermore, we will elaborate on the options available to develop or improve an algorithm that can be used to guide the approach for analysis of tc-EMG signals of inspiratory muscles in future research.

D. Leest S, Torres A, Jané R, Duiverman M, Garde A. Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation

To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.

A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.

The application of ECG cancellation in diaphragmatic electromyographic by using stationary wavelet transform

In this paper, we present and investigate a special kind of stationary wavelet algorithm using "inverse" hard threshold to eliminate the electrocardiogram (ECG) interference included in diaphragmatic electromyographic (EMGdi). Differing from traditional wavelet hard threshold, "inverse" hard threshold is used to shrink strong coefficients of ECG interference and reserve weak coefficients of EMGdi signal. Meanwhile, a novel QRS location algorithm is proposed for the position detection of R wave by using low frequency coefficients in this paper. With the proposed method, raw EMGdi is decomposed by wavelet at fifth scale. Then, each ECG interference threshold is calculated by mean square, which is estimated by wavelet coefficients in the ECG cycle at each level. Finally, ECG interference wavelet coefficients are removed by "inverse" hard threshold, and then the de-noised signal is reconstructed by wavelet coefficients. The simulation and clinical EMGdi de-noising results show that the "inverse" hard threshold investigated in this paper removes the ECG interference in EMGdi availably and reserves its signal characteristics effectively, as compared to wavelet threshold.

Neuromuscular Impairments Are Associated With Impaired Head and Trunk Stability During Gait in Parkinson Fallers

Background The trunk plays a critical role in attenuating movement-related forces that threaten to challenge the body's postural control system. For people with Parkinson's disease (PD), disease progression often leads to dopamine-resistant axial symptoms, which impair trunk control and increase falls risk. Objective This prospective study aimed to evaluate the relationship between impaired trunk muscle function, segmental coordination, and future falls in people with PD. Methods Seventy-nine PD patients and 82 age-matched controls completed clinical assessments and questionnaires to establish their medical history, symptom severity, balance confidence, and falls history. Gait characteristics and trunk muscle activity were assessed using 3-dimensional motion analysis and surface electromyography. The incidence, cause, and consequence of any falls experienced over the next 12 months were recorded and indicated that 48 PD and 29 control participants fell at least once during this time. Results PD fallers had greater peak and baseline lumbar multifidus (LMF) and thoracic erector spinae (TES) activations than control fallers and nonfallers. Analysis of covariance indicated that the higher LMF activity was attributable to the stooped posture adopted by PD fallers, but TES activity was independent of medication use, symptom severity, and trunk orientation. Furthermore, greater LMF and TES baseline activity contributed to increasing lateral head, trunk, and pelvis movements in PD fallers but not nonfallers or controls. Conclusions The results provide evidence of neuromuscular deficits for PD fallers that are independent of medications, symptom severity, and posture and contribute to impaired head, trunk, and pelvis control associated with falls in this population.

FastICA peel-off for ECG interference removal from surface EMG

BACKGROUND

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart.

METHODS

A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference.

RESULTS

It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference.

CONCLUSIONS

The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

ECG-EMG separation by using enhanced non-negative matrix factorization

We present a novel approach to single-channel ECG-EMG signal separation by means of enhanced non-negative matrix factorization (NMF). The approach is based on a linear decomposition of the input signal spectrogram in two non-negative components, which represent the ECG and EMG spectrogram estimates. As ECG and EMG have different time-frequency (TF) patterns, the decomposition is enhanced by reshaping the input mixture spectrogram in order to emphasize a sparse ECG over a noisy-like EMG. Moreover, initialization of the classical NMF algorithm with accurately designed ECG and EMG structures further increases its separation performance. The comparative study suggests that the proposed method outperforms two reference methods for both synthetic and real signal mixture scenarios.

Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy

Diaphragm electromyography is a valuable technique for the recording of electrical activity of the diaphragm. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of the neural respiratory drive (NRD). The EMGdi signal is, however, corrupted by electrocardiographic (ECG) activity, and this presence of cardiac activity can make the EMGdi interpretation more difficult. Traditionally, the EMGdi amplitude has been estimated using the average rectified value (ARV) and the root mean square (RMS). In this study, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to the traditional ARV and RMS methods. The fSampEn is calculated using a tolerance value fixed and independent of the standard deviation of the analysis window. Thus, this method quantifies the amplitude of the complex components of stochastic signals (such as EMGdi), and being less affected by changes in amplitude due to less complex components (such as ECG). The proposed method was tested in synthetic and recorded EMGdi signals. fSampEn was less sensitive to the effect of cardiac activity on EMGdi signals with different levels of NRD than ARV and RMS amplitude parameters. The mean and standard deviation of the Pearson's correlation values between inspiratory mouth pressure (an indirect measure of the respiratory muscle activity) and fSampEn, ARV, and RMS parameters, estimated in the recorded EMGdi signal at tidal volume (without inspiratory load), were 0.38±0.12, 0.27±0.11 , and 0.11±0.13, respectively. Whereas at 33 cmH2O (maximum inspiratory load) were 0.83±0.02, 0.76±0.07, and 0.61±0.19 , respectively. Our findings suggest that the proposed method may improve the evaluation of NRD.

A combination method for electrocardiogram rejection from surface electromyogram

The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.

Effects of muscle fatigue on multi-muscle synergies

We studied the effects of fatigue of ankle dorsiflexors on multi-muscle synergies defined as co-varied adjustments of elemental variables (M-modes) that stabilize a task-related performance variable (trajectory of the center of pressure, COP). M-modes were defined as muscle groups with parallel changes in activation levels. Healthy participants performed voluntary body sway in the anterior-posterior direction while trying to minimize sway in the medio-lateral direction at 0.25, 0.5, and 0.75 Hz. The trials were repeated before and during fatigue induced with a timed voluntary contraction against a constant load. Factor extraction using the principal component method was used to identify four M-modes within the space of integrated indices of muscle activity. Variance in the M-mode space at different phases across sway cycles was partitioned into two components, one that did not affect the average value of COP shift and the other that did. There were no significant effects of fatigue on variability of performance of the explicit task and on the amplitude of the COP shift. Variance of muscle activation indices and M-mode magnitudes increased during fatigue for muscles (and M-modes) both involved and not involved in the fatiguing exercise. Most of the M-mode variance increase was within the sub-space compatible with the unchanged COP trajectory resulting in an increase of the index of the multi-M-mode synergy. We conclude that one of the adaptive mechanisms to fatigue within a redundant multi-muscle system involves an increase in the variance of activation of non-fatigued muscles with a simultaneous increase in co-variation among muscle activations. The findings can be interpreted within the referent configuration hypothesis on the control of whole-body actions.