Filtering of surface EMG using ensemble empirical mode decomposition

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Automatic selection of IMFs to denoise the sEMG signals using EMD

Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples.

Current developments in surface electromyography

Background/aim

Surface electromyography (surface EMG) is a primary technique to detect the electrical activities of muscles through surface electrodes. In recent years, surface EMG applications have grown from conventional fields into new fields. However, there is a gap between the progress in the research of surface EMG and its clinical acceptance, characterized by the translational knowledge and skills in the widespread use of surface EMG among the clinician community. To reduce this gap, it is necessary to translate the updated surface EMG applications and technological advances into clinical research. Therefore, we aimed to present a perspective on recent developments in the application of surface EMG and signal processing methods.

Materials and methods

We conducted this scoping review following the Joanna Briggs Institute (JBI) method. We conducted a general search of PubMed and Web of Science to identify key search terms. Following the search, we uploaded selected articles into Rayyan and removed duplicates. After prescreening 133 titles and abstracts, we assessed 91 full texts according to the inclusion criteria.

Results

We concluded that surface EMG has made innovative technological progress and has research potential for routine clinical applications and a wide range of applications, such as neurophysiology, sports and art performances, biofeedback, physical therapy and rehabilitation, assessment of physical exercises, muscle strength, fatigue, posture and postural control, movement analysis, muscle coordination, motor synergies, modelling, and more. Novel methods have been applied for surface EMG signals in terms of time domain, frequency domain, time-frequency domain, statistical methods, and nonlinear methods.

Conclusion

Translating innovations in surface EMG and signal analysis methods into routine clinical applications can be a helpful tool with a growing and valuable role in muscle activation measurement in clinical practices. Thus, researchers must build many more interfaces that give opportunities for continuing education and research with more contemporary techniques and devices.

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.

Robust muscle force prediction using NMFSEMD denoising and FOS identification

In this paper, an aliasing noise restraint technique and a system identification-based surface electromyography (sEMG)-force prediction model are proposed to realize a type of robust sEMG and muscle force prediction. For signal denoising, a novel non-negative matrix factorization screening empirical mode decomposition (NMFSEMD) and a fast orthogonal search (FOS)-based muscle force prediction model are developed. First, the NMFSEMD model is used to screen the empirical mode decomposition (EMD) results into the noisy intrinsic mode functions (IMF). Then, the noise matrix is computed using IMF translation and superposition, and the matrix is used as the input of NMF to obtain the denoised IMF. Furthermore, the reconstruction outcome of the NMFSEMD method can be used to estimate the denoised sEMG. Finally, a new sEMG muscle force prediction model, which considers a kind of candidate function in derivative form, is constructed, and a data-training-based linear weighted model is obtained. Extensive experimental results validate the suggested method's correction: after the NMFSEMD denoising of raw sEMG signal, the signal-noise ratio (SNR) can be improved by about 15.0 dB, and the energy percentage (EP) can be greater than 90.0%. Comparing with the muscle force prediction models using the traditional pretreatment and LSSVM, and the NMFSEMD plus LSSVM-based method, the mean square error (MSE) of our approach can be reduced by at least 1.2%.

Research on AR-AKF Model Denoising of the EMG Signal

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.

HD-sEMG Signal Denoising Method for Improved Classification Performance in Transhumeral Amputees Pros thesis Control

Surface myoelectric pattern recognition (sMPR) based control strategy is a popularly adopted scheme for multifunctional upper limb prostheses. Meanwhile, above-elbow amputees (transhumeral: TH) usually have limited residual arm muscles, that mostly hinder the provision of requisite signals necessary for physiologically appropriate sMPR control. Hence, the need to maximally explore the limited signals to realize adequate sMPR control scheme in practical settings. This study proposes an effective signal denoising method driven by Multi-scale Local Polynomial Transform (MLPT) concept that can improve the signal quality, thus allowing adequate decoding of TH amputees' motion intent from high-density electromyogram (HD-sEMG) signals. The proposed method's performance was systematically investigated with HD-sEMG signals obtained from TH amputees that performed multiple classes of targeted upper limb movement tasks, and compared with two common signal denoising methods based on wavelet transform. The obtained results show that the proposed MLPT method outperformed both existing methods for motion tasks decoding with over 13.0% increment in accuracy across subjects. The possibility of generating distinct and repeatable myoelectric contraction patterns using the MLPT based denoised HDs-EMG recordings was investigated. The obtained results proved that the MLPT method can better denoise and aid the reconstruction of myoelectric signal patterns of the amputees. Therefore, this suggest the potential of the MLPT method in characterizing high-level upper limb amputees' muscle activation patterns in the context of sMPR prostheses control scheme.

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks

Infants with fragile skin are patients who would benefit from non-contact vital sign monitoring due to the avoidance of potentially harmful adhesive electrodes and cables. Non-contact vital signs monitoring has been studied in clinical settings in recent decades. However, studies on infants in the Neonatal Intensive Care Unit (NICU) are still limited. Therefore, we conducted a single-center study to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants in NICU using a digital camera. The region of interest (ROI) was automatically selected using a convolutional neural network and signal decomposition was used to minimize the noise artefacts. The experimental results have been validated with the reference data obtained from an ECG monitor. They showed a strong correlation using the Pearson correlation coefficients (PCC) of 0.9864 and 0.9453 for HR and RR, respectively, and a lower error rate with RMSE 2.23 beats/min and 2.69 breaths/min between measured data and reference data. A Bland-Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR. Therefore, this technique may be applicable in clinical environments as an economical, non-contact, and easily deployable monitoring system, and it also represents a potential application in home health monitoring.

Non-Contact SpO2 Prediction System Based on a Digital Camera

Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human’s forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.

EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding

Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error ([Formula: see text]), the improvement in signal-to-noise ratio ([Formula: see text]), and the percentage reduction in the correlation coefficient ( η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18.6, 19.2, and 8.0 dB for EMG signals corrupted with PLI, BW, and WGN at -6 dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20 dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.

Use of Surface EMG in Clinical Rehabilitation of Individuals With SCI: Barriers and Future Considerations

Surface electromyography (sEMG) is a widely used technology in rehabilitation research and provides quantifiable information on the myoelectric output of a muscle. In this perspective, we discuss the barriers which have restricted the wide-spread use of sEMG in clinical rehabilitation of individuals with spinal cord injury (SCI). One of the major obstacles is integrating the time-consuming aspects of sEMG in the already demanding schedule of physical therapists, occupational therapists, and other clinicians. From the clinicians' perspective, the lack of confidence to use sEMG technology is also apparent due to their limited exposure to the sEMG technology and possibly limited mathematical foundation through educational and professional curricula. Several technical challenges include the limited technology-transfer of ever-evolving knowledge from sEMG research into the off-the-shelf EMG systems, lack of demand from the clinicians for systems with advanced features, lack of user-friendly intuitive interfaces, and the need for a multidisciplinary approach for accurate handling and interpretation of data. We also discuss the challenges in the application and interpretation of sEMG that are specific to SCI, which are characterized by non-standardized approaches in recording and interpretation of EMGs due to the physiological and structural state of the spinal cord. Addressing the current barriers will require a collaborative, interdisciplinary, and unified approach. The most relevant steps could include enhancing user-experience for students pursuing clinical education through revised curricula through sEMG-based case studies/projects, hands-on involvement in the research, and formation of a common platform for clinicians and technicians for self-education and knowledge share.

Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.

Subject-Specific EMG Modeling with Multiple Muscles: A Preliminary Study

Modeling of surface electromyographic (EMG) signal has been proven valuable for signal interpretation and algorithm validation. However, most EMG models are currently limited to single muscle, either with numerical or analytical approaches. Here, we present a preliminary study of a subject-specific EMG model with multiple muscles. Magnetic resonance (MR) technique is used to acquire accurate cross section of the upper limb and contours of five muscle heads (biceps brachii, brachialis, lateral head, medial head, and long head of triceps brachii). The MR image is adjusted to an idealized cylindrical volume conductor model by image registration. High-density surface EMG signals are generated for two movements - elbow flexion and elbow extension. The simulated and experimental potentials were compared using activation maps. Similar activation zones were observed for each movement. These preliminary results indicate the feasibility of the multi-muscle model to generate EMG signals for complex movements, thus providing reliable data for algorithm validation.

High-Density Surface EMG Denoising Using Independent Vector Analysis

High-density surface electromyography (HD-sEMG) can provide rich temporal and spatial information about muscle activation. However, HD-sEMG signals are often contaminated by power line interference (PLI) and white Gaussian noise (WGN). In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as two popular used blind source separation techniques, are widely used for noise removal from HD-sEMG signals. In this paper, a novel method to remove PLI and WGN was proposed based on independent vector analysis (IVA). Taking advantage of both ICA and CCA, this method exploits the higher order and second-order statistical information simultaneously. Our proposed method was applied to both simulated and experimental EMG data for performance evaluation, which was at least 37.50% better than ICA and CCA methods in terms of relative root mean squared error and 28.84% better than ICA and CCA methods according to signal to noise ratio. The results demonstrated that our proposed method performed significantly better than either ICA or CCA. Specifically, the mean signal to noise ratio increased considerably. Our proposed method is a promising tool for denoising HD-sEMG signals while leading to a minimal distortion.

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.The intrinsic physiological mechanisms limiting practical implementations of myoelectric deviceswere explored: the limb position effect and the contraction intensity effect. The degradationof electromyography (EMG) pattern recognition in the presence of these factors was demonstratedon six datasets, where classification performance was 13% and 20% lower than the controlledsetting for the limb position and contraction intensity effect, respectively. The experimental designsof limb position and contraction intensity literature were surveyed. Current state-of-the-art trainingstrategies and robust algorithms for both effects were compiled and presented. Recommendationsfor future limb position effect studies include: the collection protocol providing exemplars of at least 6positions (four limb positions and three forearm orientations), three-dimensional space experimentaldesigns, transfer learning approaches, and multi-modal sensor configurations. Recommendationsfor future contraction intensity effect studies include: the collection of dynamic contractions, nonlinearcomplexity features, and proportional control.

Detection of stretch reflex onset based on empirical mode decomposition and modified sample entropy

Background

Accurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study.

Results

The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%.

Conclusions

The empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined.

VMD-based denoising methods for surface electromyography signals

OBJECTIVE

Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising.

APPROACH

The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and analyzed. In this paper, three methods are used for quantitative evaluation of the filtering performance: the signal-to-noise ratio (SNR), root mean square error and R-squared value. The proposed two methods (VMD-WST, VMD-SIT) are compared with the empirical mode decomposition (EMD) method and the wavelet method.

MAIN RESULTS

The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effects were better than the EMD method and the wavelet method. The VMD-SIT method has the best performance.

SIGNIFICANCE

This study provides a new means of eliminating the noise of sEMG signals based on the VMD method, and it can be applied in the fields of limb movement classification, disease diagnosis, human-machine interaction and so on.

Three dimensional innervation zone imaging in spastic muscles of stroke survivors

OBJECTIVE

The outcome of botulinum toxin (BTX) therapy of post-stroke spasticity relies largely on accuracy of BTX injection to the proximity of innervation zones (IZs). Recently developed three-dimensional IZ imaging (3DIZI) is the only technique currently available to provide 3D distributions of IZs in vivo, yet its performance has not been validated under pathological conditions.

APPROACH

The performance of 3DIZI was evaluated in the spastic biceps brachii muscles of four chronic stroke subjects. High-density surface electromyography (sEMG) and intramuscular electromyography (iEMG) were simultaneously recorded. The IZ location in the 3D space of the spastic biceps calculated using the 3DIZI technique from sEMG recordings were compared against the IZ location detected using intramuscular wires.

MAIN RESULTS

3DIZI successfully reconstructed the IZs in the 3D space of the spastic biceps of all four stroke subjects, with a localization error of 4.7  ±  2.7 mm, and specifically a depth error of 1.8  ±  0.4 mm.

SIGNIFICANCE

Results have demonstrated the robust performance of 3DIZI under pathological conditions, laying a solid foundation for clinical application of 3D source imaging in leading precise BTX injections for spasticity management.

Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding

Surface electromyogram (sEMG) signals are physiological signals that are widely applied in certain fields. However, sEMG signals are frequently corrupted by noise, which can lead to catastrophic consequences. A novel scheme based on complementary ensemble empirical mode decomposition (CEEMD), improved interval thresholding (IT), and component correlation analysis is developed in this study to reduce noise contamination. To solve the problem of losing desired information from sEMG, an sEMG signal is first decomposed using CEEMD to obtain intrinsic mode functions (IMFs). Subsequently, IMFs are selected via component correlation analysis, which is a measure used to select relevant modes. Thus, each selected IMF is modified through improved IT. Finally, the sEMG signal is reconstructed using the processed and residual IMFs. Root-mean-square error (RMSE) and signal-to-noise ratio (SNR) are introduced as evaluation criteria for the sEMG signal from the standard database. With SNR varying from 1 dB to 25 dB, the proposed method increases SNR by at least 1 dB and reduces RMSE compared with stationary wavelet transform and other denoising algorithms based on empirical mode decomposition. Moreover, the proposed method is applied to hand motion recognition. Results show that the rate of the denoised sEMG signal is higher than that of the raw sEMG signal.

The importance of cutaneous feedback on neural activation during maximal voluntary contraction

PURPOSE

The purpose of this study was to investigate the importance of cutaneous feedback on neural activation during maximal voluntary contraction (MVC) of the ankle plantar flexors.

METHODS

The effects of cutaneous plantar anaesthesia were assessed in 15 subjects and compared to 15 controls, using a one-day pre/post-repeated measures design. Cutaneous plantar anaesthesia was induced by lidocaine injection at the centre of forefoot, lateral midfoot, and heel. Each subject performed isometric MVCs of the ankle plantar flexors. During each isometric ramp contraction, the following variables were assessed: maximal isometric torque; surface electromyography (EMG) activity of the medial gastrocnemius (MG) and tibialis anterior (TA) muscles; and co-contraction index (CCI) between the MG and TA.

RESULTS

For ankle torque, two-way ANOVA showed no significant interaction between the pre/post-measurements × group (p = 0.166). However, MG activity presented significant interactions between the pre/post-measurements × group (p = 0.014). Post hoc comparisons indicated a decrease of MG activity in the experimental group, from 85.9 ± 11.9 to 62.7 ± 30.8% (p = 0.016). Additionally, the post-anaesthesia MG activity of the experimental group differed statistically with pre- and post-MG activity of the control group (p = 0.027 and p = 0.008, respectively). For TA activity and CCI, two-way ANOVA detected no significant interactions between the pre/post-measurements × group (p = 0.605 and p = 0.332, respectively).

CONCLUSION

Our results indicate that during MVC, cutaneous feedback modulates neural activity to MG muscle, without changing the extent of MG-TA co-contraction.

Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal

Most existing non-contact monitoring systems are limited to detecting physiological signs from a single subject at a time. Still, another challenge facing these systems is that they are prone to noise artifacts resulting from motion of subjects, facial expressions, talking, skin tone, and illumination variations. This paper proposes an efficient non-contact system based on a digital camera to track the cardiorespiratory signal from a number of subjects (up to six persons) at the same time with a new method for noise artifact removal. The proposed system relied on the physiological and physical effects as a result of the activity of the cardiovascular and respiratory systems, such as skin color changes and head motion. Since these effects are imperceptible to the human eye and highly affected by the noise variations, we used advanced signal and video processing techniques, including developing video magnification technique, complete ensemble empirical mode decomposition with adaptive noise, and canonical correlation analysis to extract the heart rate and respiratory rate from multiple subjects under the noise artifact assumptions. The experimental results of the proposed system had a significant correlation (Pearson's correlation coefficient = 0.9994, Spearman correlation coefficient = 0.9987, and root mean square error = 0.32) when compared with the conventional contact methods (pulse oximeter and piezorespiratory belt), which makes the proposed system a promising candidate for novel applications.

Alterations in Spectral Attributes of Surface Electromyograms after Utilization of a Foot Drop Stimulator during Post-Stroke Gait

BACKGROUND

A foot drop stimulator (FDS) is a rehabilitation intervention that stimulates the common peroneal nerve to facilitate ankle dorsiflexion at the appropriate time during post-stroke hemiplegic gait. Time-frequency analysis (TFA) of non-stationary surface electromyograms (EMG) and spectral variables such as instantaneous mean frequency (IMNF) can provide valuable information on the long-term effects of FDS intervention in terms of changes in the motor unit (MU) recruitment during gait, secondary to improved dorsiflexion.

OBJECTIVE

The aim of this study was to apply a wavelet-based TFA approach to assess the changes in neuromuscular activation of the tibialis anterior (TA), soleus (SOL), and gastrocnemius (GA) muscles after utilization of an FDS during gait post-stroke.

METHODS

Surface EMG were collected bilaterally from the TA, SOL, and GA muscles from six participants (142.9 ± 103.3 months post-stroke) while walking without the FDS at baseline and 6 months post-FDS utilization. Continuous wavelet transform was performed to get the averaged time-frequency distribution of band pass filtered (20-300 Hz) EMGs during multiple walking trials. IMNFs were computed during normalized gait and were averaged during the stance and swing phases. Percent changes in the energies associated with each frequency band of 25 Hz between 25 and 300 Hz were computed and compared between visits.

RESULTS

Averaged time-frequency representations of the affected TA, SOL, and GA EMG show altered spectral attributes post-FDS utilization during normalized gait. The mean IMNF values for the affected TA were significantly lower than the unaffected TA at baseline (p = 0.026) and follow-up (p = 0.038) during normalized stance. The mean IMNF values significantly increased (p = 0.017) for the affected GA at follow-up during normalized swing. The frequency band of 250-275 Hz significantly increased in the energies post-FDS utilization for all muscles.

CONCLUSION

The application of wavelet-based TFA of EMG and outcome measures (IMNF, energy) extracted from the time-frequency distributions suggest alterations in MU recruitment strategies after the use of FDS in individuals with chronic stroke. This further establishes the efficacy of FDS as a rehabilitation intervention that may promote motor recovery in addition to treating the secondary complications of foot drop due to post-stroke hemiplegia.

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

BACKGROUND

Ensemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.

EXPERIMENT

The experimental data was obtained from the Center for Machine Learning and Intelligent Systems, University of California Irvine (UCI). The data was donated by the Nueva Granada Military University and the Technopark node Manizales in Colombia. The databases of 11 male subjects from the healthy group were taken into the study. The subjects undergo three exercise programs, leg extension from a sitting position (sitting), flexion of the leg up (standing), and gait (walking), while four electrodes were placed on biceps femoris (BF), vastus medialis (VM), rectus femoris (RF), and semitendinosus (ST).

METHODS

Based on the experimental data, a comparative study is provided by assessing the Empirical Mode Decomposition (EMD)-based approaches, EEMD, Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). The outcomes from these approaches are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing.

RESULTS

Both MEMD and NA-MEMD methods (except EEMD) can guarantee equal numbers of IMFs. For mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD.

CONCLUSIONS

This study proposes the NA-MEMD approach for multichannel EMG signal processing. This finding implies that NA-MEMD is effective for simultaneously analysing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing the functional activities of daily living.

Performance evaluation of Noise-Assisted Multivariate Empirical Mode Decomposition and its application to multichannel EMG signals

The use of the Empirical Mode Decomposition (EMD) for nonlinear signal processing has been popularized in the recent years. However, its utility for the processing of multichannel Electromyography (EMG) signals is still limited. This paper investigates the decomposition performance of multichannel EMGs by using the EMD-based approaches, Ensemble EMD (EEMD), Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). In the experiment, 11 male subjects undergo three exercise programs, leg extension from a sitting position, flexion of the leg up, and gait, while electrodes are placed on the muscle groups, biceps femoris, vastus medialis, rectus femoris, and semitendinosus. The outcomes are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing. Results show both MEMD and NA-MEMD can guarantee equal numbers of IMFs, whereas for mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD. This finding implies that NA-MEMD is effective for simultaneously analyzing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing functional activities of daily living.

Filtered Virtual Reference: A New Method for the Reduction of Power Line Interference With Minimal Distortion of Monopolar Surface EMG

GOAL

This study tests and validates a new method to remove power line interference from monopolar EMGs detected by multichannel systems: the filtered virtual reference (FVR). FVR is an adaptation of the virtual reference (VR) method, which consists in referencing signals detected by each electrode in a grid to their spatial average. Signals may however be distorted with the VR approach, in particular when the skin region where the detection system is positioned does not cover the entire muscle.

METHODS

Simulated and experimental EMGs were used to compare the performance of FVR and VR in terms of interference reduction and distortion of monopolar signals referred to a remote reference.

RESULTS

Simulated data revealed the monopolar EMG signals processed with FVR were significantly less distorted than those filtered by VR. These results were similarly observed for experimental signals. Moreover, FVR method outperformed VR in removing power line interference when it was distributed unevenly across the signals of the grid.

CONCLUSION

Key results demonstrated that FVR improves the VR method as it reduces interference while preserving the information content of monopolar signals.

SIGNIFICANCE

Although the actual distribution of motor unit action potential is represented in monopolar EMGs, collecting high quality monopolar signals is challenging. This study presents a possible solution to this issue; FVR provides undistorted monopolar signals with negligible interference and is insensitive to muscle architecture. It is therefore relevant for EMG applications benefiting from a clean monopolar detection (e.g., decomposition, control of prosthetic devices, motor unit number estimation).

Denoising of HD-sEMG signals using canonical correlation analysis

High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.

Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis

The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.

Myoelectric pattern identification of stroke survivors using multivariate empirical mode decomposition

This study presents a novel feature extraction method for myoelectric pattern recognition using a multivariate extension of empirical mode decomposition (EMD), namely multivariate EMD (MEMD). The method processes multiple surface electromyogram (EMG) channels simultaneously rather than in a channel-by-channel manner. From mode-aligned intrinsic mode functions (IMFs, representing signal components over multiple scales) derived from the MEMD analysis, normalized amplitude distributions of the same-mode/scale IMFs across different channels were calculated as features, which serve to reveal the underlying relationship in the aligned intrinsic scales across multiple muscles. The proposed method was assessed for identification of 18 different functional movement patterns via 27-channel surface EMG signals recorded from the paretic forearm muscles of 12 subjects with hemiparetic stroke. With a linear discriminant classifier, the proposed MEMD based feature set resulted in an average error rate of 4.61 ± 4.70% for classification of all the different movements, significantly lower than that of the conventional time-domain feature set (7.14 ± 6.15%, p < 0.05). The results indicate that the MEMD based feature extraction of multi-channel surface EMG data provides a promising approach to modeling of muscle couplings and identification of different myoelectric patterns.

Empirical mode decomposition and neural network for the classification of electroretinographic data

The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals' features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behavior characterized by strong nonlinear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyze electroretinograms, i.e., the retinal response to a light flash, with the aim to detect and classify retinal diseases. The present application focuses on two retinal pathologies: achromatopsia, which is a cone disease, and congenital stationary night blindness, which affects the photoreceptoral signal transmission. The results indicate that, under suitable conditions, the method proposed here has the potential to provide a powerful tool for routine clinical examinations, since it is able to recognize with high level of confidence the eventual presence of one of the two pathologies.

Surface electromyography signal processing and classification techniques

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.