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How to analise EMG signals?

Computer science

Feature extraction

Curvilinear coordinates

Time–frequency analysis

Short-time Fourier transform

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To analyze electromyography (EMG) signals, various methods are employed. One approach involves mathematical modeling to simulate muscle fiber trajectories and derive action potentials, aiding in signal interpretation and validation of processing techniques . Another method includes using surface or needle electrodes to register bioelectrical signals from muscles, which can then be analyzed for myopathic or neuropathic lesions through numerical parameters . Additionally, surface EMG signals can be processed by extracting features, applying dimensionality reduction techniques, and utilizing machine learning classifiers for movement classification . Furthermore, artificial intelligence algorithms based on big data can be utilized for bio-signal analysis, enabling simpler and more accurate interpretation of EMG signals, especially when dealing with noise-induced variations .

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Proceedings Article•DOI EMG Signal Analysis Using Sensor System and Edge Device for Wearable Applications Yu Seok Han, Su Bin Jang, Eun Bin An, Hyun-Sik Choi - Show less +3 more 11 Nov 2022	EMG signals are analyzed using a sensor system for wearables and an edge device with FPGA for artificial intelligence-based processing, enabling easy and accurate bio-signal analysis.
Proceedings Article•DOI Time-frequency analysis of the EMG signal for the identification of hand grasping postures 14 Sep 2022	The EMG signals are analyzed by extracting features capturing both time and frequency information, followed by applying the Short-Time Fourier Transform (STFT) to create time-frequency graphs for hand grasping posture recognition.
Journal Article•DOI Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers With Approximate Conductivity Tensor 01 Mar 2022-IEEE Transactions on Biomedical Engineering 1 Citations	Analyse EMG signals by developing a subject-specific model simulating muscle fiber trajectories and deriving action potentials using an approximate conductivity tensor, validated through numerical and experimental comparisons.
DOI Time-frequency analysis of the EMG signal for the identification of hand grasping postures Juan Pablo Gasca Calderón, Diego Fernando Gonzalez Ruiz, Ruthber Rodriguez Serrezuela - Show less +2 more 14 Sep 2022	Time-frequency analysis of EMG signals involves feature extraction, dimensionality reduction, and spectral analysis using STFT to identify hand grasping postures in applications like prosthetics and diagnostics.
Proceedings Article•DOI Analysis of the Biological (EMG) Signal Agata Nawrocka, Marcin Nawrocki, Andrzej Kot - Show less +2 more 27 Oct 2020 1 Citations	EMG signals are analyzed by decomposing signals from needle electrodes into motor unit action potentials and using the Clustering Index method for signals from surface electrodes to detect muscle lesions.

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Are there possibilities of processing EMG signals in embedded systems?5 answersYes, there are possibilities of processing electromyography (EMG) signals in embedded systems. Various studies have explored the implementation of artificial neural networks (ANN) in embedded systems for EMG signal processing. Additionally, research has focused on developing cost-effective embedded systems utilizing techniques like short-time Fourier transform (STFT) for EMG-controlled robotic applications, showcasing stable performance and high accuracy. Furthermore, a proposed low-cost embedded system based on an ARM Cortex M4 microcontroller aims to enhance EMG signal recognition and classification for applications such as prosthetic design, highlighting the importance of computational efficiency and affordability in embedded systems for EMG processing. These findings collectively demonstrate the feasibility and potential benefits of processing EMG signals in embedded systems for various biomedical applications.

How does assesst EMG musle activity?5 answersAssessing EMG muscle activity involves monitoring the electrical signals generated by muscle cells. Various methods have been developed for this purpose. One approach is the use of machine learning-based techniques like DEMANN, which reliably detects muscle activation timing from surface EMG signals, showing minimal sensitivity to signal-to-noise ratio variations. Another method involves utilizing the QNET Myoelectric Board and NI ELVIS II+ board connected through LabVIEW for signal processing and data collection, enabling direct user interaction and analysis based on assessment criteria. Additionally, a novel approach called M-ALED and its refined version FM-ALED have been introduced, which enhance EMG activity monitoring by improving robustness against noise and impulsiveness, ultimately leading to more accurate detection of muscle activity. These methods collectively contribute to a comprehensive evaluation of EMG muscle activity in various applications.

Can emg data from throat be used to recognize alphabet production using machine learning?5 answersEMG data from the throat can be used to recognize alphabet production using machine learning. A novel throat-language decoding system (TLDS) has been proposed, which captures signals of throat vibration using flexible, low-cost, and self-powered sensors. The system extracts time-frequency dynamics features and nonlinear dynamics features from the throat vibration signals and applies a machine learning classifier for recognition. The TLDS achieved satisfactory results in recognizing letters, with an average accuracy of 90.55% for single-person recognition and 87.26% for multi-person recognition. This approach provides a promising solution for individuals who cannot speak, enabling them to communicate conveniently using throat vibration signals.

What is the amplitude of the EMG signal of spasm ?4 answersThe amplitude of the EMG signal during spasm varies depending on the condition. In patients with spasticity, the amplitude of the F response (F32) was found to be significantly increased. However, in another study, the amplitude of the EMG signal during spasm was not specifically mentioned. Therefore, it is important to consider the specific context and condition when determining the amplitude of the EMG signal during spasm.

Areas of electrodes used to record EMG on children3 answersElectrodes used to record electromyography (EMG) in children include concentric electrodesand surface electrodes. Concentric electrodes are used for stimulated EMG potential analysis and are placed in the orbicularis oculi muscle. Surface electrodes are used to record EMG activity in the sternocleidomastoid and upper trapezius muscles. Additionally, intracerebral multilead electrodes are used for chronic EEG recording of seizures in children with drug-resistant focal epilepsy. These electrodes are placed in cortical structures on the lateral, intermediate, and mesial aspect of the hemisphere, as well as deep-seated lesions. Overall, the electrodes used for EMG and EEG recordings in children vary depending on the specific purpose and location of the recording.

How can we use filter features to extract frequency domain information from EMG signals?4 answersFilter features can be used to extract frequency domain information from EMG signals. In a study by Turgunov et al., a comparative analysis of classification based on time and frequency domain features of EMG signals showed that features in the time domain had a higher rate of accuracy and speed compared to features in the frequency domain. Another study by Yao et al. introduced a feature filter that can filter out unwanted features from EEG signals, similar to the cocktail party problem in machine learning. This filter was able to filter out over 90% of alcoholism information from EEG signals while retaining an average of only 4.2% useful feature accuracy lost. Additionally, Alam et al. found that the frequency domain-LDA classifier combination achieved the highest classification accuracy for EMG characteristics. Therefore, by using filter features, it is possible to extract frequency domain information from EMG signals and improve classification accuracy.

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