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Oo, Thandar, and Pornchai Phukpattaranont. "Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals." Fluctuation and Noise Letters 19, no. 03 (2020): 2050027. http://dx.doi.org/10.1142/s0219477520500273.
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When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.
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Huang, Jian-Jia, Chung-Yu Chang, Jen-Kuang Lee, and Hen-Wai Tsao. "RESOLVING SINGLE-LEAD ECG FROM EMG INTERFERENCE IN HOLTER RECORDING BASED ON EEMD." Biomedical Engineering: Applications, Basis and Communications 26, no. 01 (2014): 1450008. http://dx.doi.org/10.4015/s1016237214500082.
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The aim of this study was to propose an electrocardiogram (ECG) de-noising framework based on ensemble empirical mode decomposition (EEMD) to eliminate electromyography (EMG) interference without signal distortion. ECG signals are easily corrupted by EMG, especially in Holter monitor recordings. The frequency component overlapping between EMG and ECG is a challenge in signal processing that remains to be solved. The aim of the present study, therefore, was to resolve ECG signals from recorded segments with EMG noise. Two units were put into our proposed framework; first, modified moving average filter for signal preprocessing to cancel baseline wandering, and second, EEMD to cancel EMG. In order to enhance the de-noising capability (such as signal distortion in traditional EEMD), we developed a novel EEMD signal reconstruction algorithm using a statistical ECG model. We tested the proposed framework using MIT-BIH database, artificial and single-lead recorded real-world noisy signals. Correlation coefficients and ECG morphological features were used to evaluate the performance of the proposed algorithm. Our results showed that the proposed de-noising algorithm successfully resolved ECG signals from baseline wandering and EMG interference without distorting the signal waveform.
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Chang, Kang-Ming, Peng-Ta Liu, and Ta-Sen Wei. "Electromyography Parameter Variations with Electrocardiography Noise." Sensors 22, no. 16 (2022): 5948. http://dx.doi.org/10.3390/s22165948.
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Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to 32 simulated signals. Following Pan-Tompkins R-peak detection, four ECG removal methods were used to remove ECG with different compensation algorithms to obtain the denoised EMG signal. A total of 13 time-domain and four frequency-domain EMG features were calculated from the denoised EMG. In addition, the similarity of denoised EMG features compared to clean EMG was also evaluated. Our results showed that with the ratio EMG/ECG SNR = 10 and 20, the ECG can be almost ignored, and the similarity of EMG features is close to 1. When EMG/ECG SNR = 1 and 2, there is a large variation of EMG features. The results of our simulation study would be beneficial for understanding the variations of EMG features upon the different EMG/ECG SNR.
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Debbal, S. M. "Pathological Electromyogram (EMG) Signal Analysis Parameters." Clinical Cardiology and Cardiovascular Interventions 4, no. 13 (2021): 01–14. http://dx.doi.org/10.31579/2641-0419/185.
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Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis can provide a wide range of information’s related to the type of signal (normal and pathological).
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Neto, Osmar Pinto, and Evangelos A. Christou. "Rectification of the EMG Signal Impairs the Identification of Oscillatory Input to the Muscle." Journal of Neurophysiology 103, no. 2 (2010): 1093–103. http://dx.doi.org/10.1152/jn.00792.2009.
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Rectification of EMG signals is a common processing step used when performing electroencephalographic–electromyographic (EEG–EMG) coherence and EMG–EMG coherence. It is well known, however, that EMG rectification alters the power spectrum of the recorded EMG signal (interference EMG). The purpose of this study was to determine whether rectification of the EMG signal influences the capability of capturing the oscillatory input to a single EMG signal and the common oscillations between two EMG signals. Several EMG signals were reconstructed from experimentally recorded EMG signals from the surface of the first dorsal interosseus muscle and were manipulated to have an oscillatory input or common input (for pairs of reconstructed EMG signals) at various frequency bands (in Hz: 0–12, 12–30, 30–50, 50–100, 100–150, 150–200, 200–250, 250–300, and 300–400), one at a time. The absolute integral and normalized integral of power, peak power, and peak coherence (for pairs of EMG signals) were quantified from each frequency band. The power spectrum of the interference EMG accurately detected the changes to the oscillatory input to the reconstructed EMG signal, whereas the power spectrum of the rectified EMG did not. Similarly, the EMG–EMG coherence between two interference EMG signals accurately detected the common input to the pairs of reconstructed EMG signals, whereas the EMG–EMG coherence between two rectified EMG signals did not. The frequency band from 12 to 30 Hz in the power spectrum of the rectified EMG and the EMG–EMG coherence between two rectified signals was influenced by the input from 100 to 150 Hz but not from the input from 12 to 30 Hz. The study concludes that the power spectrum of the EMG and EMG–EMG coherence should be performed on interference EMG signals and not on rectified EMG signals because rectification impairs the identification of the oscillatory input to a single EMG signal and the common oscillatory input between two EMG signals.
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Strzecha, Krzysztof, Marek Krakós, Bogusław Więcek, et al. "Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences." Applied Sciences 11, no. 10 (2021): 4625. http://dx.doi.org/10.3390/app11104625.
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This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content.
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Shahbakhti, Mohammad, Elnaz Heydari, and Gia Thien Luu. "Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study." Fluctuation and Noise Letters 13, no. 04 (2014): 1450030. http://dx.doi.org/10.1142/s0219477514500308.
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The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.
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Ting, Evon Lim Wan, Almon Chai, and Lim Phei Chin. "A Review on EMG Signal Classification and Applications." International Journal of Signal Processing Systems 9, no. 1 (2022): 1–6. http://dx.doi.org/10.18178/ijsps.10.1.1-6.
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Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and processing are reviewed. The different types of feature extraction techniques preferred by researchers are also discussed, including some combination and comparison of feature extraction techniques. This paper also reviews the different types of classifiers favored by researchers to recognize human movements based on EMG signals. The current applications of EMG signals are also reviewed.
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Liang, Hongbo, Yingxin Yu, Mika Mochida, et al. "EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs." Symmetry 12, no. 11 (2020): 1851. http://dx.doi.org/10.3390/sym12111851.
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Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.
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Tejedor, Javier, Constantino A. García, David G. Márquez, Rafael Raya, and Abraham Otero. "Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review." Sensors 19, no. 21 (2019): 4708. http://dx.doi.org/10.3390/s19214708.
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This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.
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Oo, Thandar, and Pornchai Phukpattaranont. "Accounting for SNR in an Algorithm Using Wavelet Transform to Remove ECG Interference from EMG Signals." Fluctuation and Noise Letters 19, no. 01 (2019): 2050001. http://dx.doi.org/10.1142/s0219477520500017.
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When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from [Formula: see text]20 to 20[Formula: see text]dB with 5[Formula: see text]dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.
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Sarangi, Animesh, Bal Gopal Mishra, and Satyabhama Dash. "Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal." YMER Digital 21, no. 08 (2022): 400–407. http://dx.doi.org/10.37896/ymer21.08/36.
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Electromyogram (EMG) or muscle artifacts frequently affect electrocardiogram (ECG) readings. These artifacts make the required information in the ECG signal difficult to see. In this study, we introduced the singular spectrum analysis (SSA), a powerful subspace-based method for removing EMG artifacts from ECG data. In order to effectively extract the desired component from the tainted ECG data, we presented a new grouping approach and set a threshold. First, a process known as embedding converts a single channel signal into several channels of signals or data. The orthogonal eigenvectors are then calculated using singular value decomposition(SVD) from the multichannel data's covariance matrix. A threshold is selected to locate these eigenvectors, which are utilized to generate the required subspace. After locating the subspace, the multichannel data is simply projected into it, followed by a method called diagonal averaging which will create the original time series and extract the ECG signals. Keywords: Electrocardiogram, EMG artifact, Singular Spectrum Analysis, Embedding, SVD, Mobility.
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Riyadh Mahmood, Hassanein, Manaf K. Hussein, and Riyadh A. Abedraba. "Development of Low-Cost Biosignal Acquisition System for ECG, EMG, and EOG." Wasit Journal of Engineering Sciences 10, no. 3 (2022): 191–202. http://dx.doi.org/10.31185/ejuow.vol10.iss3.352.
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The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB) wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.
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Chen, Sijia, Zhizeng Luo, and Tong Hua. "Research on AR-AKF Model Denoising of the EMG Signal." Computational and Mathematical Methods in Medicine 2021 (November 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/9409560.
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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.
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Parsaei, Hossein, Daniel W. Stashuk, Sarbast Rasheed, Charles Farkas, and Andrew Hamilton-Wright. "Intramuscular EMG Signal Decomposition." Critical Reviews™ in Biomedical Engineering 38, no. 5 (2010): 435–65. http://dx.doi.org/10.1615/critrevbiomedeng.v38.i5.20.
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Agrawal, Sanskruti, Yukta Jain, Ganesh Naik, Shubham Soneji, and Keyoor Deorukhkar. "EMG Signal Controlled Wheelchair." International Journal of Computer Applications 185, no. 23 (2023): 46–48. http://dx.doi.org/10.5120/ijca2023922986.
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Martinek, Radek, Martina Ladrova, Michaela Sidikova, et al. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals." Sensors 21, no. 18 (2021): 6064. http://dx.doi.org/10.3390/s21186064.
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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).
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PHINYOMARK, ANGKOON, PORNCHAI PHUKPATTARANONT, CHUSAK LIMSAKUL, and MONTRI PHOTHISONOTHAI. "ELECTROMYOGRAPHY (EMG) SIGNAL CLASSIFICATION BASED ON DETRENDED FLUCTUATION ANALYSIS." Fluctuation and Noise Letters 10, no. 03 (2011): 281–301. http://dx.doi.org/10.1142/s0219477511000570.
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Electromyography (EMG) signal is a useful signal in various medical and engineering applications. To extract the useful information in the EMG signal, feature extraction method should be performed. The extracted features of the EMG signal are usually calculated based on linear or statistical methods, but the EMG signal exhibits the nonlinear and more complex in the properties. With recent advances in nonlinear analysis we are proposing the study of the EMG signals from upper-limb movements using Detrended Fluctuation Analysis (DFA) method. This study used EMG signals obtained from eight upper-limb movements and five muscle positions as representative EMG signals. The usefulness of the DFA method has been proposed to discriminate the upper-limb movements. Complete comparative studies of an optimal parameter of the DFA method were performed. From the viewpoints of maximum class separability, robustness, and complexity, scaling exponent obtained from the DFA method shows the appropriateness to be used as a feature in the classification of the EMG signal. From the experimental results, an optimal DFA method is obtained under these conditions: the minimum box size is approximately four, the maximum box size is one-tenth of the signal length, the box size increment is based on a power of two, and the quadratic polynomial fits is used in the fitting procedure. Moreover, the classification performance of the DFA method is better than other existing nonlinear methods, including the Higuchi's method.
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Kim, Sehyeon, Dae Youp Shin, Taekyung Kim, Sangsook Lee, Jung Keun Hyun, and Sung-Min Park. "Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography." Sensors 22, no. 2 (2022): 680. http://dx.doi.org/10.3390/s22020680.
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Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group.
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Tanuja Subba, Et al. "A Study on Electromyography Signal as a Controller." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2024): 4662–67. http://dx.doi.org/10.17762/ijritcc.v11i9.10014.
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Human computer interaction (HCI) is the study of interfaces between human and computer. When an input keyboard is pressed the output is displayed in the monitor is a simple example of human and computer interaction. World Wide Web is yet another example of HCI. HCI is everywhere and has become an important aspect in human life. HCI have many subfields and one among them is the study of biosignals. Signals that are generated from living body during muscle contraction, eye movement, brain signal are biosignals and these signals have potential for developing an interface for human computer interaction. There are many such bio electric signals which can be made to use for developing interface and that can be done by acquiring these signals which will form a linkage with the computer technique. These types of signals are brain signal called Electroencephalogram (EEG), heart signal Electrocardiogram (ECG), eye movement signal Electrooculogram (EOG) and muscle signalElectromyogram (EMG). The paper focuses on the study of muscle signal controller as HCI, EMG signals are captured during contraction of a skeletal muscle. The signal is then amplified and converted into usable signals that will be fed as an input to computer and can be used for controlling certain devices.
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Merletti, R., B. Indino, T. Graven-Nielsen, and D. Farina. "Surface EMG Crosstalk Evaluated from Experimental Recordings and Simulated Signals." Methods of Information in Medicine 43, no. 01 (2004): 30–35. http://dx.doi.org/10.1055/s-0038-1633419.
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Summary Objectives: Surface EMG crosstalk is the EMG signal detected over a non-active muscle and generated by a nearby muscle. The aim of this study was to analyze the sources of crosstalk signals in surface EMG recordings and to discuss methods proposed in the literature for crosstalk quantification and reduction. Methods: The study is based on both simulated and experimental signals. The simulated signals are generated by a structure based surface EMG signal model. Signals were recorded with both intramuscular and surface electrodes and single motor unit surface potentials were extracted with the spike triggered averaging approach. Moreover, surface EMG signals were recorded from electrically stimulated muscles. Results: From the simulation and experimental analysis it was clear that the main determinants of crosstalk are non-propagating signal components, generated by the extinction of the intracellular action potentials at the tendons. Thus, crosstalk signals have a different shape with respect to the signals detected over the active muscle and contain high frequency components. Conclusions: Since crosstalk has signal components different from those dominant in case of detection from near sources, commonly used methods to quantify and reduce crosstalk, such as the cross-correlation coefficient and high-pass temporal filtering, are not reliable. Selectivity of detection systems must be discussed separately as selectivity with respect to propagating and non-propagating signal components. The knowledge about the origin of crosstalk signal constitutes the basis for crosstalk interpretation, quantification, and reduction.
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Xu, Lin, Elisabetta Peri, Rik Vullings, Chiara Rabotti, Johannes P. Van Dijk, and Massimo Mischi. "Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography." Sensors 20, no. 17 (2020): 4890. http://dx.doi.org/10.3390/s20174890.
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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.
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Lee, Ukjun, and Hyunchol Shin. "Study on Compressed Sensing of ECG/EMG/EEG Signals for Low Power Wireless Biopotential Signal Monitoring." Journal of the Institute of Electronics and Information Engineers 52, no. 3 (2015): 89–95. http://dx.doi.org/10.5573/ieie.2015.52.3.089.
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Ahmad, Mohiuddin, Atiqul Islam, T. T. Khan Munia, M. A. Rashid, and T. M. N. Tunku Mansur. "PHYSIOLOGICAL SIGNAL ANALYSIS FOR COGNITIVE STATE ESTIMATION." Biomedical Engineering: Applications, Basis and Communications 24, no. 01 (2012): 57–69. http://dx.doi.org/10.4015/s1016237212002950.
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The purpose of this paper is to identify inconsistency in human physiological signals based on cognitive states by measuring and analyzing bio-signals. In this paper, the cognitive states are estimated using physiological signal analysis. The parameters are electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and blood pressure (BP). The signals have been collected using BIOPAC system in which the subjects were induced to undergo the specific sequence of the cognitive state. For getting physiological signals during different conditions, we utilized power point slide show, video clips and question answer method which elicits mental reactions from the subjects. Data is taken before and after four tasks that encompassed the motor action (MA), thought (TH), memory related (MR) and emotion (EM). These measured values are analyzed using BIOPAC Acknowledge software. It was found that the motor action and thought states have effects on BP while MR and EM state mainly affect the ECG measurement. The decibel value and frequency found for EM state in ECG are minimum compared to relaxed state (RS) condition. Similarly, the maximum frequency and dB value is found for MR state. No significant variation was seen for MA and TH states. Thus it was decided that the MR and EM states mainly affect the ECG measurement. For BP the value increases in MA state and decreases in TH state. The MA state mainly affects the EMG signal while other states have no significant changes. The EEG mainly detects the signal of task performed by the specific brain region where the electrodes are placed. In EEG analysis, the electrodes are placed in occipital lobe region which gives mainly the variation in alpha amplitude of EEG with eyes closed and eyes opened. Alpha wave amplitudes vary with the subjects attention to mental tasks performed with eyes closed.
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Sadikoglu, Fahreddin, Cemal Kavalcioglu, and Berk Dagman. "Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease." Procedia Computer Science 120 (2017): 422–29. http://dx.doi.org/10.1016/j.procs.2017.11.259.
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Anas Fouad Ahmed. "A quick survey of EEG signal noise removal methods." Global Journal of Engineering and Technology Advances 11, no. 3 (2022): 098–104. http://dx.doi.org/10.30574/gjeta.2022.11.3.0100.
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An Electroencephalogram (EEG) is produced as a consequence of the electrical voltage of neurons in the brain. The EEG signal is crucial for detecting brain activity and attitude. Because this signal has very low amplitude, it is easily corrupted by different artefacts. The study and analysis of brain signals in the presence of these artifacts is a challenging task. ECG, EOG, EMG, and motion are the popular artifacts that induce disturbance to the EEG signal. This survey paper emphasizes the artifact elimination methods with their substantial parameters that must be considered during the study of published research on this trend.
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Kale, S. N., and S. V. Dudul. "Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network." Applied Computational Intelligence and Soft Computing 2009 (2009): 1–12. http://dx.doi.org/10.1155/2009/129761.
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Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error) as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP) and Radial Basis Function NN (RBF) models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.
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Wei, Chang Zhi. "Stress Emotion Recognition Based on RSP and EMG Signals." Advanced Materials Research 709 (June 2013): 827–31. http://dx.doi.org/10.4028/www.scientific.net/amr.709.827.
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To recognize the stress emotion, a subject was put alternately in periods of high and low stress by configuring the speed and difficulty of a game named Tetris. The respiration (RSP) signal and the electromyogram (EMG) signal with different stress level were then acquired. After preprocessing, the mathematical features were calculated and automatic detection of stress level based on Fisher linear discriminant classifier was realized. The results show that the average correct detection rate of stress level based on the EMG signal can reach 97.8%. That of the RSP signal is only 86.7%. The EMG signal is more effective than the RSP signal in detection of stress level. Union of multiple physiological signals can effectively improve the correct detection rate.
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Dai, Yangyang, Feng Duan, Fan Feng, et al. "A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition." Entropy 23, no. 9 (2021): 1170. http://dx.doi.org/10.3390/e23091170.
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An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
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Marquez-Figueroa, Sandra, Yuriy S. Shmaliy, and Oscar Ibarra-Manzano. "Improving Gaussianity of EMG Envelope for Myoelectric Robot Arm Control." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (August 5, 2021): 106–12. http://dx.doi.org/10.37394/23208.2021.18.12.
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Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.
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Soundirarajan, Mirra, Mohammad Hossein Babini, Sue Sim, Visvamba Nathan, and Hamidreza Namazi. "Decoding of the Relationship between Brain and Facial Muscle Activities in Response to Dynamic Visual Stimuli." Fluctuation and Noise Letters 19, no. 04 (2020): 2050041. http://dx.doi.org/10.1142/s0219477520500418.
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In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of analysis and showed that visual stimulus with greater complexity has greater effect on the complexity of EEG and EMG signals. Therefore, we showed the relationship between facial muscles and brain activities in this paper. The method of analysis in this research can be further employed to investigate the relationship between other human organs’ activities and brain activity.
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Viswanadham, Talabattula, and Rajesh Kumar P. "Artefacts Removal from ECG Signal: Dragonfly Optimization-based Learning Algorithm for Neural Network-enhanced Adaptive Filtering." Scalable Computing: Practice and Experience 21, no. 2 (2020): 247–63. http://dx.doi.org/10.12694/scpe.v21i2.1657.
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Electrocardiogram (ECG) artefact removal is the major research topic as the pure ECG signals are an essential part of diagnosing heart-related problems. ECG signals are highly prominent to the interaction with the other signals like the Electromyography (EMG), Electroencephalography (EEG), and Electrooculography (EOG) signals and the interference mainly occurs at the time of recording. The removal of the artefacts from the ECG signal is a hectic challenge, for which, a novel algorithm is proposed in this work. The proposed method utilizes the adaptive filter termed as the (Dragonfly optimization + Levenberg Marqueret learning algorithm) DLM-based Nonlinear Autoregressive with eXogenous input (NARX) neural network for the removal of the artefacts from the ECG signals. Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction procedure. The clean signal thus obtained is used for effective diagnosis purposes, and the experimentation performed to prove the effectiveness of the proposed method proves that the proposed method obtained a maximum Signal-to-noise ratio (SNR) of 52.8789 dB, a minimum error of 0.1832, and minimum error of 0.428.
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HAMZI, Maroua, Mohamed BOUMEHRAZ, and Rafia HASSANI. "Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features." Electrotehnica, Electronica, Automatica 71, no. 3 (2023): 61–68. http://dx.doi.org/10.46904/eea.23.71.3.1108007.
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Electromyography (EMG) records the electrical activity generated by skeletal muscles, offering valuable insights into muscle function and movement. To address the complexity of EMG signals, various signal analysis methods have been developed in the time and frequency domains for engineering applications like myoelectric control of prosthetics and movement analysis. In this study, EMG signals were acquired from ten healthy volunteers in different forearm positions using a Myoware Muscle Sensor and MPU6050 board. From each EMG signal, root mean square (RMS), standard deviation (STD), and mean absolute value (MAV) were computed and selected as representative features. These features were then fed into an LDA classifier to estimate forearm flexion angles. The study aims to compare the effectiveness of features calculated from the EMG signal and those derived from its discrete wavelet decomposition. The experimental results demonstrate the proposed method's efficiency in estimating forearm flexion angles using a single channel of EMG signals, achieving an average classification accuracy of 97.50 % across four gesture classes.
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MULDAYANI, WAHYU, ARIZAL MUJIBTAMALA NANDA IMRON, KHAIRUL ANAM, SUMARDI SUMARDI, WIDJONARKO WIDJONARKO, and ZILVANHISNA EMKA FITRI. "Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (2020): 591. http://dx.doi.org/10.26760/elkomika.v8i3.591.
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ABSTRAKSinyal EMG merupakan salah satu sinyal yang dapat digunakan untuk memberikan perintah pada kursi roda listrik. Sinyal EMG yang digunakan diambil dari sinyal otot fleksor dan ekstensor yang berada di tangan kanan. Sinyal tersebut diambil menggunakan sensor Myo Armband. Klasifikasi sinyal EMG diambil dari pergerakan jari yang mewakili perintah gerak yaitu jari kelingking untuk bergerak maju, jari manis untuk berhenti, jari tengah untuk belok kanan dan jari telunjuk untuk belok kiri. Setiap sinyal EMG diekstraksi fitur untuk menentukan karakteristik sinyal sehingga fitur yang diperoleh adalah Average Absolute Value, Root Mean Square, Simple Integral Square, EMG Simple Variant and Integrated EMG. Kemudian fitur tersebut digunakan sebagai input dari metode klasifikasi Artificial Neural Network Backpropagation. Jumlah data latih yang digunakan adalah 800 data sedangkan data uji yang digunakan adalah 200 data. Tingkat keberhasilan proses klasifikasi ini sebesar 93%.Kata kunci: electromyogram, artificial neural network, klasifikasi sinyal, tangan kanan, Myo Armband. ABSTRACTEMG signal is one of the signals that can be used to give orders to electric wheelchairs. The EMG signal used is taken from the flexor and extensor muscle signals in the right hand. The signal is taken using the Myo Armband sensor. The EMG signal classification is taken from the movement of the finger which represents the command of motion ie the little finger to move forward, ring finger to stop, middle finger to turn right and index finger to turn left. Each EMG signal is extracted features to determine the signal characteristics so that the features obtained are Average Absolute Value, Root Mean Square, Simple Integral Square, EMG Simple Variant and Integrated EMG. Then the feature is used as input from the Backpropagation classification method. The amount of training data used is 800 data while the test data used is 200 data. The success rate of this classification process is 93%.Keywords: electromyogram, artificial neural network, signal classification, right hand, Myo Armband.
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León, Gabriela, Emely López, Hans López, and Cesar Hernandez. "Design of an EMG Signal Generator Based on Random Firing Patterns." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 07 (2024): 104–29. http://dx.doi.org/10.3991/ijoe.v20i07.47375.
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Electromyographic (EMG) signals exhibit complex interference patterns that comprise several single motor unit action potentials (SMUAPs). Evidence of a model that can generate EMG signals and considers intrinsic characteristics, such as long-range dependence (LRD) or shortrange dependence (SRD), or that supports the study of pathology-related signals is lacking. Therefore, the present study aimed to develop an EMG signal generator based on SRD or LRD derived from firing patterns. We used a dynamic model to parameterize up to 15 SMUAP waveforms of real EMG signals extracted from a database. Then, we used relative appearance rates for some signals based on the number of SMUAPs to generate the latter randomly. Furthermore, we complemented our model by generating a random firing pattern. The synthetic reconstruction of the signals indicated a displacement compared with their respective firing patterns, with the highest error rate being 4.1%. The model of the EMG signal generator in its current state could be useful for a specialist who intends to study the behavior of the signals, starting with the exploration of synthetic signals and then proceeding to the real signals.
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van der Hiele, Karin, Robert H. A. M. Reijntjes, Alla A. Vein, et al. "Electromyographic Activity in the EEG in Alzheimer's Disease: Noise or Signal?" International Journal of Alzheimer's Disease 2011 (2011): 1–6. http://dx.doi.org/10.4061/2011/547024.
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Many efforts have been directed at negating the influence of electromyographic (EMG) activity on the EEG, especially in elderly demented patients. We wondered whether these “artifacts” might reflect cognitive and behavioural aspects of dementia. In this pilot study, 11 patients with probable Alzheimer's disease (AD), 13 with amnestic mild cognitive impairment (MCI) and 13 controls underwent EEG registration. As EMG measures, we used frontal and temporal 50–70 Hz activity. We found that the EEGs of AD patients displayed more theta activity, less alpha reactivity, and more frontal EMG than controls. Interestingly, increased EMG activity indicated more cognitive impairment and more depressive complaints. EEG variables on the whole distinguished better between groups than EMG variables, but an EMG variable was best for the distinction between MCI and controls. Our results suggest that EMG activity in the EEG could be more than noise; it differs systematically between groups and may reflect different cerebral functions than the EEG.
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Ulubas, Mustafa Kutay, Ahmet Akpinar, Ozlem Coskun, and Mesud Kahriman. "EMG Controlled Artificial Hand and Arm Design." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (March 26, 2022): 41–46. http://dx.doi.org/10.37394/23208.2022.19.6.
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Today, there are many people who have lost their hands or arms for various reasons. This situation affects both psychology and daily life of people negatively. With the developing technology, prosthetic hand and arm studies are carried out to facilitate the life of disabled people and to eliminate this negativity. Thanks to the existing biopotentials in the body, it is possible to read the human body. In this context, it can explain our hand and arm movements with the existing biopotential signals and transfer these signals to a prosthesis, enabling people to make the desired movement. Since the biopotential signals in the body are of very low amplitude and frequency, the first goal is to obtain the EMG signal cleanly without noise. In this study, the obtained analog signal was converted into digital information by using software in the computer environment. Thus, each signal gained a meaning. As a result, the movement of the prosthesis was provided by transferring it to stepper motors with the help of Arduino.
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Cárdenas-Valdez, José Ricardo, Ángel Humberto Corral-Domínguez, Manuel de Jesús García-Ortega, Andrés Calvillo-Téllez, Carlos Hurtado-Sánchez, and Everardo Inzunza-González. "EMG signal transmission system under RF schemes." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 11, Especial4 (2023): 277–82. http://dx.doi.org/10.29057/icbi.v11iespecial4.11413.
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With the rise in pathological conditions during the post-pandemic era, particularly concerning the management of biomedical signals, a significant surge has been observed. This research endeavors to develop a self-adaptive algorithm for the discretization and data encapsulation of electromyographic (EMG) signals. The synthetic signal used for analysis is acquired from the PhysioNet database, specifically focusing on the implementation of the tibialis anterior muscle. A transmission chain is established utilizing the AD9361 transceiver, while a power amplifier is employed for base radio applications, operating at a carrier frequency of 2.45 GHz. The spectral validation of this system reveals that the 16-QAM modulation, which is subjected to testing, yields an accuracy of -15.5 dB NMSE. As a further work, an EMG signal acquisition stage is proposed, based on a high-resolution analog-to-digital converter (ADC) card, alongside the exploration of higher order n-QAM schemes to enhance accuracy in the receiver stage.
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Boyer, Marianne, Laurent Bouyer, Jean-Sébastien Roy, and Alexandre Campeau-Lecours. "Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review." Sensors 23, no. 6 (2023): 2927. http://dx.doi.org/10.3390/s23062927.
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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.
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Rasheed, Sarbast. "A MATLAB-Based Interactive Environment for EMG Signal Decomposition Utilizing Matched Template Filters." Computer Engineering and Applications Journal 4, no. 3 (2015): 189–204. http://dx.doi.org/10.18495/comengapp.v4i3.145.
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An interactive software package for analyzing and decomposing electromyographic (EMG) signals is designed, constructed, and implemented using the MATLAB high-level programming language and its interactive environment. EMG signal analysis in the form of signal decomposition into their constituent motor unit potential trains (MUPTs) is considered as a classification task. Matched template filter methods have been employed for the classification of motor unit potentials (MUPs) in which the assignment criterion used for MUPs is based on a combination of MUP shapes and motor unit firing pattern information. The developed software package consists of several graphical user interfaces used to detect individual MUP waveforms from raw EMG signals, extract relevant features, and classify MUPs into MUPTs using matched template filter classifiers. The proposed software package is useful for enhancing the analysis quality and providing a systematic approach to the EMG signal decomposition process. It also worked as a very helpful environment for testing and evaluating algorithms developed for EMG signal decomposition research.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 2, no. 3 (2016): 35. http://dx.doi.org/10.26417/ejis.v2i3-35-44.
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Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 2, no. 3 (2016): 35. http://dx.doi.org/10.26417/ejis.v2i3.35-44.
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Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 2, no. 3 (2016): 35. http://dx.doi.org/10.26417/ejis.v2i3.p35-44.
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Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 5, no. 1 (2016): 35. http://dx.doi.org/10.26417/ejis.v5i1.p35-44.
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Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Huang, Q. H., Y. P. Zheng, X. Chena, J. F. He, and J. Shi. "A System for the Synchronized Recording of Sonomyography, Electromyography and Joint Angle." Open Biomedical Engineering Journal 1, no. 1 (2007): 77–84. http://dx.doi.org/10.2174/1874120700701010077.
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Ultrasound and electromyography (EMG) are two of the most commonly used diagnostic tools for the assessment of muscles. Recently, many studies reported the simultaneous collection of EMG signals and ultrasound images, which were normally amplified and digitized by different devices. However, there is lack of a systematic method to synchronize them and no study has reported the effects of ultrasound gel to the EMG signal collection during the simultaneous data collection. In this paper, we introduced a new method to synchronize ultrasound B-scan images, EMG signals, joint angles and other related signals (e.g. force and velocity signals) in real-time. The B-mode ultrasound images were simultaneously captured by the PC together with the surface EMG (SEMG) and the joint angle signal. The deformations of the forearm muscles induced by wrist motions were extracted from a sequence of ultrasound images, named as Sonomyography (SMG). Preliminary experiments demonstrated that the proposed method could reliably collect the synchronized ultrasound images, SEMG signals and joint angle signals in real-time. In addition, the effect of ultrasound gel on the SEMG signals when the EMG electrodes were close to the ultrasound probe was studied. It was found that the SEMG signals were not significantly affected by the amount of the ultrasound gel. The system is being used for the study of contractions of various muscles as well as the muscle fatigue.
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Kledrowetz, Vilem, Roman Prokop, Lukas Fujcik, Michal Pavlik, and Jiří Háze. "Low-power ASIC suitable for miniaturized wireless EMG systems." Journal of Electrical Engineering 70, no. 5 (2019): 393–99. http://dx.doi.org/10.2478/jee-2019-0071.
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Abstract Nowadays, the technology advancements of signal processing, low-voltage low-power circuits and miniaturized circuits have enabled the design of compact, battery-powered, high performance solutions for a wide range of, particularly, biomedical applications. Novel sensors for human biomedical signals are creating new opportunities for low weight wearable devices which allow continuous monitoring together with freedom of movement of the users. This paper presents the design and implementation of a novel miniaturized low-power sensor in integrated circuit (IC) form suitable for wireless electromyogram (EMG) systems. Signal inputs (electrodes) are connected to this application-specific integrated circuit (ASIC). The ASIC consists of several consecutive parts. Signals from electrodes are fed to an instrumentation amplifier (INA) with fixed gain of 50 and filtered by two filters (a low-pass and high-pass filter), which remove useless signals and noise with frequencies below 20 Hz and above 500 Hz. Then signal is amplified by a variable gain amplifier. The INA together with the reconfigurable amplifier provide overall gain of 50, 200, 500 or 1250. The amplified signal is then converted to pulse density modulated (PDM) signal using a 12-bit delta-sigma modulator. The ASIC is fabricated in TSMC0.18 mixed-signal CMOS technology.
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Sezgin, Necmettin. "Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra." Scientific World Journal 2012 (2012): 1–5. http://dx.doi.org/10.1100/2012/478952.
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The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are aggressive or normal actions. The EMG dataset was obtained from the machine learning repository. First, the aggressive and normal EMG activities were analyzed using bispectrum and the quadratic phase coupling of each EMG episode was determined. Next, the features of the analyzed EMG signals were fed into learning machines to separate the aggressive and normal actions. The best classification result was 99.75%, which is sufficient to significantly classify the aggressive and normal actions.
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Malik Mohd Ali, Abdul, Syed Faiz Ahmed, Athar Ali, M. Kamran Joyo, Kushairy A. Kadir, and Radzi Ambar. "EMG-Based Spasticity Robotic Arm Forupper Arm Fatigue Identification." International Journal of Engineering & Technology 7, no. 2.34 (2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.34.13917.
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Electromyogram (EMG) signal reflect the electrical activity of human muscle and contains information about the structure of muscle. Furthermore, motor unit action potential (MUAP) is the results from spatial and temporal summation of difference muscle fibers of a single motor. The EMG signal results, in turn is from the summation of different MUAPs which are sufficiently near the recording electrode. EMG signal can identify the differences between signals from bicep, triceps and forearms during exercise. Raw data from the experiment is vital to assist physiotherapy to understand when the subject fatigue of noise high pick signal during rehabilitation. Several normal subjects were selected to perform experiments to understand the pattern of fatigue in early state, middle stage and last stage of exercises.
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Rusli, Rusli Ully, Ruslan Ruslan, Sarifin G., Arimbi Arimbi, and Mariyal Qibtiyah. "Measurement of Medial Head Gastrocnemius Muscle Contraction Strength in Basic Sepak Takraw Techniques Using Electromyogram Signals." COMPETITOR: Jurnal Pendidikan Kepelatihan Olahraga 15, no. 3 (2023): 683. http://dx.doi.org/10.26858/cjpko.v15i3.53403.
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This study aims to measure the strength of contraction of the gastrocnemius medial head muscle in basic techniques sepak sila using electromyogram signals. The subjects in this research were 3 South Sulawesi sepak takraw athletes. EMG signal measurement using the Trigno™ Wireless EMG System. The output data is the results of the EMG signal, the Root Mean Square value of each muscle component measured. The data analysis technique uses quantitative descriptive. The results of EMG signal measurements produce RMS values for each muscle measured as follows: (1). The subject produced the largest first EMG signal from the right gastrocnemius medial head muscle, 0.44631mV with an RMS value of 93.009mV, and the largest left gastrocnemius medial head muscle, 0.33889mV with an RMS value of 61.302mV. (2). The subject produced the second largest right gastrocnemius medial head muscle EMG signal of 1.66238mV with an RMS value of 38.7856mV, and the largest left gastrocnemius medial head muscle of 1.37871mV with an RMS value of 25.6827mV. (3). The subject produced the third largest right gastrocnemius medial head muscle EMG signal of 2.02191mV with an RMS value of 76.7969mV, and the largest left gastrocnemius medial head muscle of 0.37397mV with an RMS value of 47.3252mV. It was concluded that the third subject produced the highest muscle EMG strength which occurred in the right gastrocnemius medial head muscle signal and the smallest EMG signal in the left gastrocnemius medial head.
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Zhu, Zheng Han, Jing Quan Liu, Yue Feng Rui, and Chun Sheng Yang. "A Research on Implantable Microelectrodes for EMG Signal Acquisition." Key Engineering Materials 483 (June 2011): 387–91. http://dx.doi.org/10.4028/www.scientific.net/kem.483.387.
Full textAbstract:
Signal acquisition microelectrode works as an interface between tissue and circuit in neural engineering. Stable, precise and lossless detection of EMG is important to functional neuromuscular stimulation. In this paper, we propose an implantable microelectrode for EMG acquisition fabricated by MEMS technology and test the impedance of several microelectrodes fabricated with different parameters. By analyzing the amplitudes and power spectrum of the EMG signals acquired from rabbits by fabricated microelectrodes, the signal acquisition performances of the microelectrodes are evaluated and compared both in time domain and frequency domain.