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Journal articles on the topic 'EMG signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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

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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 averag

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J, Jasper Gnana Chandran, Shanmathi M, Jerald Jeba Kumar S, and Ambigaipriya S. "NOVEL MACHINE LEARNING FILTER PROTOTYPING FOR ECG/EEG/EMG SIGNALS." ICTACT Journal on Microelectronics 9, no. 1 (2023): 1481–86. https://doi.org/10.21917/ijme.2023.0256.

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The ECG/EEG/EMG monitoring system is a new type of medical technology that has emerged because of the convergence of mobile technology and the increased demand for healthcare management caused by an ageing population. The ECG/EEG/EMG signal detecting system makes it possible to carry out a dynamic medical diagnosis in a manner that is both quicker and accurate by giving accurate ECG/EEG/EMG signals throughout a varied range of physical activities. This study covers the installation of a prototype biomedical measurement system, which can be used to pedagogically evaluate the usefulness of speci

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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

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L N, Dayananda. "Portable Non – Invasive Device for ECG and EMG Monitoring." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48910.

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Abstract— This project proposes the design and implementation of a portable, non-invasive ECG and EMG monitoring system using the ESP32-S3 microcontroller. The system is capable of capturing and displaying real-time bioelectrical signals from the human body using two dedicated sensors—an AD8232 module for electrocardiogram (ECG) signal acquisition and a modular EMG sensor for muscle activity monitoring. The ESP32-S3’s built-in ADC is used to digitize the signals, which are then processed and displayed as scrolling waveforms on a 320×240 SPI TFT display. A push-button interface is incorporated

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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 surfa

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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 effective

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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

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Granados-Ruiz, Jackeline, David Asael Gutiérrez-Hernández, Carlos Lino-Ramírez, et al. "METHODOLOGICAL APPROACH FOR EXTRACTION OF CHARACTERISTICS OF BIOLOGICAL SIGNALS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 02 (2019): 3011–20. https://doi.org/10.5281/zenodo.14811307.

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Generally, signal processing is applied to a set of data that is derived from the sampling of an acquired signal. This treatment is carried out with the help of a computer that in turn executes a series of logical and mathematical operations. The treatment of signals is linked to other techniques and scientific disciplines. Some of the applications of the signal treatments may be in the form of processing of audio signals, treatment of digital images, digital communications and biological signals. In this case, the treatment was applied to biological signals such as ECG (Electrocardiogram sign

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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 S

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Wang, Tianyu, Shanshan Yao, Li-Hua Shao, and Yong Zhu. "Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing." Sensors 24, no. 20 (2024): 6670. http://dx.doi.org/10.3390/s24206670.

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Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and e

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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

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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 pr

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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 t

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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 in

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Shakya, Sahaj, and Bipul Ranjitkar. "Forearm Bio-Medical Signal Processing." International Journal on Engineering Technology 2, no. 1 (2024): 49–59. https://doi.org/10.3126/injet.v2i1.72518.

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This research utilizes low-dimensional surface EMG and EEG data, obtained from the human arm using ECG electrodes, to analyze forearm muscle signals through a novel approach. Both EMG and EEG signals are employed side by side: EEG captures brain activity, particularly in the beta (13-30 Hz) and alpha (8-12 Hz) frequency ranges, while EMG focuses on muscle activity in the 20 Hz to 200 Hz range. Beta waves are associated with motor planning and voluntary movements, while alpha waves decrease during movement execution, indicating disengagement from a resting state. Event-related desynchronization

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Muhumed, Sakarie M., and Muhammad I. Ibrahimy. "Noise Reduction Techniques in ECG Signal." Asian Journal of Electrical and Electronic Engineering 3, no. 1 (2023): 27–33. http://dx.doi.org/10.69955/ajoeee.2023.v3i1.43.

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The problem of noise interference in ECG signals has been addressed in this paper. Specifically, a method has been developed to filter out Electromyography noise (EMG) from ECG signals. A dataset of ECG signals with varying levels of EMG noise has been collected using the MIT-BIH dataset. An algorithm has been designed and implemented using the DA FIR filter coupled with Kaiser windowing technique to filter out the noise. The algorithm has been tested on the collected dataset using MATLAB. The performance of the algorithm has been evaluated by calculating the Signal-to-Noise Ratio (SNR) and th

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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 t

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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

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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

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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-

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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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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

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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 intera

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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 wi

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Oo, Nandar, Hla Myo Tun, Devasis Pradhan, Lei Lei Yin Win, Mya Mya Aye, and Thandar Oo. "Implementation of the Process for Contamination in Electromyography (EMG) Signal by Using Noise Removal Techniques." Journal of Novel Engineering Science and Technology 3, no. 03 (2024): 94–98. https://doi.org/10.56741/jnest.v3i03.627.

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The paper describes the analysis of electromyography (EMG) signals using noise removal techniques. The problem in this study is to consider a noise removal technique for basic EMG signal processing by the Band Pass Filter method. A research approach to designing simulation codes for observing EMG signal modeling and noise removal techniques through mathematical methods from signals and systems concepts. The results confirm that it can provide high-performance target monitoring of the EMG signal in real-world applications.

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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 appl

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Elouaham, S., A. Dliou, N. Elkamoun, et al. "Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 829–36. https://doi.org/10.11591/ijeecs.v23.i2.pp829-836.

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The health of the brain and muscles depends on the proper analysis of electroencephalogram and electromyogram signals without noise. The latter blends into the recording of biomedical signals for external or internal reasons of the human body. Therefore, to obtain a more accurate signal, it is needed to select filtering techniques that minimize the noise. In this study, the techniques used are empirical mode decomposition and its variants. Among the new versions of variants is the improved complete ensemble empirical mode decomposition with adaptive noise. These methods are applied to electroe

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Azad Feyzullayev, Aynur Jabiyeva, Azad Feyzullayev, Aynur Jabiyeva. "STRATEGY FOR REDUCING MUSCLE FATIGUE IN THE USE OF BIOELECTRIC PROSTHESIS BASED ON ELECTROMYOGRAPHIC ANALYSIS." Caucasus-Economic and Social Analysis Journal of Southern Caucasus 63, no. 01 (2025): 25–36. https://doi.org/10.36962/cesajsc63012025-25.

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Muscle fatigue is characterised by a decrease in the ability of muscles to perform required functions, which is accompanied by accumulation of metabolites, changes in ionic balance and electrical properties of muscle tissue. These physiological changes lead to marked transformations of electromyographic (EMG) signals, including a shift of their spectrum to lower frequencies. Such changes affect the quality of signal recognition and control accuracy of bioelectric prostheses, since most myoelectric control algorithms are based on the stability of spectral and amplitude characteristics of EMG si

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Zukro Aini, Rasyida Shabihah. "EMG Instrumentation Modeling and Feature Processing Based On Discrete Wavelet Transform." Indonesian Applied Physics Letters 5, no. 1 (2024): 1–13. http://dx.doi.org/10.20473/iapl.v5i1.56245.

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Electromyography (EMG) instrumentation is essential in generating electrical signals from skeletal muscles. EMG sensors are helpful in various cases requiring the detection of human muscle contractions, neuromuscular disorders, and rehabilitation. EMG instrumentation is divided into two parts, namely, the analogue part and the digital part. The EMG instrumentation design comprises a digital-to-analog converter (DAC), instrumentation amplifier, filter, and analog-to-digital converter (ADC). Meanwhile, in digital signal processing adopting the Discrete Wavelet Transform (DWT) method, frequency a

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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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K, Aparna, Vasantharaj A, Sudipta Ghosh, and Bhumika Choksi. "DEVELOPMENT OF HIGH-PERFORMANCE ANALOG AND DIGITAL FILTER FOR BIOMEDICAL SIGNAL PROCESSING." ICTACT Journal on Microelectronics 9, no. 2 (2023): 1539–44. https://doi.org/10.21917/ijme.2023.0268.

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Electromyography (EMG) is a valuable biomedical signal used to study the electrical activity of muscles, providing crucial insights into neuromuscular disorders, motor control, and rehabilitation therapies. However, EMG signals are inherently contaminated with noise and artifacts, challenging the accurate extraction of relevant information. This paper presents a novel approach for enhancing EMG signals using an Infinite Impulse Response (IIR) filter. The IIR filter design was carefully tailored to meet the specific requirements of EMG signal processing, including the extraction of electromyogr

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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 pr

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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

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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. https://doi.org/10.5281/zenodo.6962085.

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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

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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 si

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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 s

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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

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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,

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Zohirov, K., S. Boykobilov, M. Temirov, M. Sattorov, and F. Ruziboev. "Analytical review of methods for recording and classifying movements based on electromyography." Международный Журнал Теоретических и Прикладных Вопросов Цифровых Технологий 8, no. 1 (2025): 175–82. https://doi.org/10.62132/ijdt.v8i1.246.

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This paper provides a comprehensive overview of optimal methods and processes for recording, processing, and classifying electromyography (EMG) signals in the context of human movement rehabilitation. It begins by exploring advanced techniques for accurate and noise-free EMG signal acquisition, emphasizing the importance of electrode placement, signal amplification, and filtering strategies. The paper then delves into modern signal processing methods, such as feature extraction and dimensionality reduction, which enhance the interpretability of EMG data. Furthermore, the study highlights cutti

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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

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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 optimizati

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Chandrasekhar, Thalari, and Shruthi M.L.J. "ADVANCED SIGNAL PROCESSING IN EMG ANALYSIS USING KNN KERNEL-BASED SVM FOR ENHANCED DATA CLASSIFICATION AND OUTLIER DETECTION." ICTACT Journal on Communication Technology 15, no. 4 (2024): 3392–99. https://doi.org/10.21917/ijct.2024.0503.

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Electromyography (EMG) signals provide critical insights into muscular and neurological functions, but their complex nature makes accurate classification and outlier detection challenging. Traditional signal processing approaches often fail to address the variability in EMG signals, leading to suboptimal data interpretation. The integration of advanced algorithmic innovations, such as K-Nearest Neighbors (KNN) kernel-based Support Vector Machine (SVM), offers a robust solution for enhancing EMG signal processing. In this study, EMG signals from 500 datasets, sampled at 2 kHz, were preprocessed

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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 ab

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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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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 ada

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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 inf

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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 extra

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