Some Aspects of Nonstationary Myoelectric Signal Processing

Xiong, Fuqin; Shwedyk, E.

doi:10.1109/tbme.1987.326041

Cited by 28 publications

(5 citation statements)

References 11 publications

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“…Indeed, power spectral analyses can be considered valid only for stationary segments (Bilodeau et al, 1997; Hary et al, 1982; Merletti et al, 1992; Papoulis, 1984; Xiong and Shwedyk, 1987). Most previous studies reporting PSD analyses of DIAm EMG signals assumed stationarity, but did not explicitly test for stationarity (Cairns and Road, 1998; Hussain et al, 1991).…”

Section: Discussionmentioning

confidence: 99%

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle

Seven

Mantilla

Zhan

et al. 2013

Respiratory Physiology & Neurobiology

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We hypothesized that diaphragm muscle (DIAm) by a shift in the EMG power spectral density (PSD) to higher frequencies reflects recruitment of more fatigable fast-twitch motor units and motor unit recruitment is reflected by EMG non-stationarity. DIAm EMG was recorded in anesthetized rats during eupnea, hypoxia-hypercapnia (10% O2-5% CO2), airway occlusion, and sneezing (maximal DIAm force). Although power in all frequency bands increased progressively across motor behaviors, PSD centroid frequency increased only during sneezing (p<0.05). The non-stationary period at the onset of EMG activity ranged from ~70 ms during airway occlusion to ~150 ms during eupnea. Within the initial non-stationary period of EMG activity 80–95% of motor units were recruited during different motor behaviors. Motor units augmented their discharge frequencies progressively beyond the non-stationary period; yet, EMG signal became stationary. In conclusion, non-stationarity of DIAm EMG reflects the period of motor unit recruitment, while a shift in the PSD towards higher frequencies reflects recruitment of more fatigable fast-twitch motor units.

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Section: Discussionmentioning

confidence: 99%

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle

Seven

Mantilla

Zhan

et al. 2013

Respiratory Physiology & Neurobiology

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“…Electromyography (EMG) signals have been effectively used in different medical fields like medical cases [1], [2], Control of the prosthetic arm with force estimation [3], [4], and fatigue detection with different methods [5]- [7]. There is many information presented on voluntaries that is related to muscle-computer interface such as EMG and muscle signal processing [8]- [17], brain-computer interface [18], [19] and etc.…”

Section: Introductionmentioning

confidence: 99%

A Virtual EMG Signal Control and Analysis for Optimal Hardware Design

Aljobouri

2022

Int. J. Onl. Eng.

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Background: A muscle-computer interface is one of the new applications of the human-computer interface technologies and specifically the brain-computer interface. Brain-muscle-computer interface based on the Electromyography (EMG) signal. EMG signal is an electrical activity from a muscle that is used as an input for effecting several tasks.Objective: This work presented an interfacing process between the Graphical User Interface (GUI) and hardware system. Using the implemented system, the researcher shall deals with the raw EMG data easily by analyzing the signal from the muscle sensor detection.Material and Methods: A novel virtual EMG signal control and analysis system design is proposed in this work. The system consists mainly of two parts, hardware and software toolbox. Hardware design is mainly dependent on using a muscle movement sensor as well as the feedback from the virtual toolbox. The virtual software design offers a relatively simple design of a friendly graphical user interface. It consists mainly of the input EMG signal and output signal after using different processing methods. Feedback response from the final EMG signal results after the processing may help the designer to present the optimal hardware design.Results: The output results show the output performance of the proposed virtual EMG data controlling and analysis with the implemented hardware design of the muscle sensor movement detection. The results show promise that these interfaces may provide a new option to benefit the designer in choosing the optimal prosthesis design of severely disabled persons.

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“…A shorter segment of EMG signal can be locally stationary. For sEMG signals various studies have shown that shorter segments possess stationarity [32][33][34]. Beck et al (2006) Any myoelectric control based rehabilitative and assistive robotic device should decode the EMG signal in less than 300ms for natural and robust control of the device [37].…”

Section: Introductionmentioning

confidence: 99%

“…A shorter segment of EMG signal can be locally stationary. For sEMG signals, various studies have shown that shorter segments possess stationarity [31][32][33]. Beck et al conducted a study to test the stationarity of the sEMG signals using a RUNS test, reverse arrangement (RA) test and modified RA test [34].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices

et al. 2021

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Objective. Intramuscular electromyography (iEMG) signals, invasively recorded, directly from the muscles are used to diagnose various neuromuscular disorders/diseases and to control rehabilitative and assistive robotic devices. iEMG signals are potentially being used in neurology, kinesiology, rehabilitation and ergonomics, to detect/diagnose various diseases/disorders. Electromyography-based classification and analysis systems are being designed and tested for the classification of various neuromuscular disorders and to control rehabilitative and assistive robotic devices. Many studies have explored parameters such as the pre-processing, feature extraction and selection of classifiers that can affect the performance and efficacy of iEMG-based classification systems. The pre-processing stage includes the removal of any unwanted noise from the original signal and windowing of the signal. Approach. This study investigated and presented the optimum windowing configurations for robust control and better performance results of an iEMG-based analysis system based on the stationarity rate (SR) and classification accuracy. Both disjoint and overlap, windowing techniques with varying window and overlap sizes have been investigated using a machine learning-based classification algorithm called linear discriminant analysis. Main results. The optimum window size ranges are from 200–300 ms for the disjoint and 225–300 ms for the overlap windowing technique, respectively. The inferred results show that for the overlap windowing technique the optimum range of overlap size is from 10%–30% of the length of window size. The mean classification accuracy (MCA) and mean stationarity rate (MSR) were found to be lower in the disjoint windowing technique compared to overlap windowing at all investigated overlap sizes. Statistical analysis (two-way analysis of variance test) showed that the MSR and MCA of the overlap windowing technique was significantly different at overlap sizes of 10%–30% (p-values < 0.05). Significance. The presented results can be used to achieve the best possible classification results and SR for any iEMG-based real-time diagnosis, detection and control system, which can enhance the performance of the system significantly.

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Some Aspects of Nonstationary Myoelectric Signal Processing

Cited by 28 publications

References 11 publications

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle

A Virtual EMG Signal Control and Analysis for Optimal Hardware Design

Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices

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