Effect of Electrode Skin Impedance on Electromyography Signal Quality

Sae-lim, Withit; Phukpattaranont, Pornchai; Thongpull, Kittikhun

doi:10.1109/ecticon.2018.8619967

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…Achieving a low-impedance interface between the electrode and the skin is vital to transduce signals with high fidelity, as too high of an impedance increases the thermal noise of the electrodes, thus degrading their signal-to-noise ratio (SNR) [ 5 , 6 , 7 ]. A low SNR may, in turn, prevent clean recording of muscle activation during electromyography (EMG) studies [ 8 ], or may cause morphological changes in electrocardiogram (ECG) signatures that can complicate interpretation [ 9 ]. The motion artifact susceptibility of skin-based electrodes may also increase with a higher impedance, further degrading signatures such as the QRS complex in ECG recordings [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Time Evolution of the Skin–Electrode Interface Impedance under Different Skin Treatments

Murphy

Scheid

Hendricks

et al. 2021

Sensors

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A low and stable impedance at the skin–electrode interface is key to high-fidelity acquisition of biosignals, both acutely and in the long term. However, recording quality is highly variable due to the complex nature of human skin. Here, we present an experimental and modeling framework to investigate the interfacial impedance behavior, and describe how skin interventions affect its stability over time. To illustrate this approach, we report experimental measurements on the skin–electrode impedance using pre-gelled, clinical-grade electrodes in healthy human subjects recorded over 24 h following four skin treatments: (i) mechanical abrasion, (ii) chemical exfoliation, (iii) microporation, and (iv) no treatment. In the immediate post-treatment period, mechanical abrasion yields the lowest initial impedance, whereas the other treatments provide modest improvement compared to untreated skin. After 24 h, however, the impedance becomes more uniform across all groups (<20 kΩ at 10 Hz). The impedance data are fitted with an equivalent circuit model of the complete skin–electrode interface, clearly identifying skin-level versus electrode-level contributions to the overall impedance. Using this model, we systematically investigate how time and treatment affect the impedance response, and show that removal of the superficial epidermal layers is essential to achieving a low, long-term stable interface impedance.

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Section: Introductionmentioning

confidence: 99%

Time Evolution of the Skin–Electrode Interface Impedance under Different Skin Treatments

Murphy

Scheid

Hendricks

et al. 2021

Sensors

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“…Visual inspection of the data, and reference to notes recorded by the experimenter during data collection, resulted in identification of invalid fEMG data for 27 subjects. Reasons for this varied (e.g., imprecise placement of electrodes leading to cross talk with other physiological signals, high impedance in the signal) but removal of invalid fEMG data prior to analysis is not uncommon (Potter & Bolls, 2012;Sae-lim et al, 2018). This resulted in a total of 28 participants in neutral spine and 24 participants in the spinal flexion condition included for the fEMG analyses.…”

Section: Dependent Variablesmentioning

confidence: 99%

The Effects of “Media Tech Neck”: The Impact of Spinal Flexion on Cognitive and Emotional Processing of Videos

Cores-Sarría

Han

Myrick

et al. 2023

Communication Research

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Adoption of mobile devices (e.g., smart phones and tablets) has popularized a neck-down posture during media consumption that is different from the traditional upright body posture for video viewing. A neck-down posture exerts substantial pressure upon the spine, and this posture has been previously linked to psychological effects. This study advances the literature by studying the impact of posture effects on processing audiovisual information. In a mixed design experiment ( N = 87), the effect of neck posture when viewing 24 video PSAs was tested using physiological and self-report measures. Multilevel modeling analyses of heart rate and corrugator data showed that spinal flexion lowered attentional engagement and caused incongruent emotional responses to the messages compared to a neutral spine posture. However, spinal-flexion participants exhibited greater skin conductance, counter to the predicted emotional disengagement. The impact of neck posture on message processing was largest at the beginning of the experiment and faded over time.

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“…The voltage of the sEMG signal range from 50 μV to 100 mV and the frequency is varied from 10 to 500 Hz ( Pancholi and Joshi, 2018 ). The electrode skin impedance which is one of the noises that affects the quality of EMG signals must be as low as possible to obtain effective signals ( Sae-Lim et al, 2019 ). Pancholi and Joshi (2018) obtained sEMG signals from five different arm muscles using hardware based on ADS1298 IC (Texas Instruments) and ARM cortex M4 series processor with 4,000 Hz sampling frequency.…”

Section: Acquisition Of Surface Electromyography and Preprocessingmentioning

confidence: 99%

“…The voltage of the sEMG signal range from 50 µV to 100 mV and the frequency is varied from 10 to 500 Hz (Pancholi and Joshi, 2018). The electrode skin impedance which is one of the noises that affects the quality of EMG signals must be as low as possible to obtain effective signals (Sae-Lim et al, 2019). Pancholi and Joshi (2018) (Zanca et al, 2014).…”

Section: Acquisition Of Surface Electromyography and Preprocessingmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

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Effect of Electrode Skin Impedance on Electromyography Signal Quality

Cited by 11 publications

References 9 publications

Time Evolution of the Skin–Electrode Interface Impedance under Different Skin Treatments

Time Evolution of the Skin–Electrode Interface Impedance under Different Skin Treatments

The Effects of “Media Tech Neck”: The Impact of Spinal Flexion on Cognitive and Emotional Processing of Videos

Application of Surface Electromyography in Exercise Fatigue: A Review

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