Muscle Fatigue Enhance Beta Band EMG-EMG Coupling of Antagonistic Muscles in Patients With Post-stroke Spasticity

Wang, Lejun; Yu, Xiao-Ming; Shao, Qineng; Yang, Hua; Huang, Shangjun; Niu, Wen

doi:10.3389/fbioe.2020.01007

Cited by 14 publications

(5 citation statements)

References 35 publications

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“…First, it would be argued that EMG activities of antagonistic muscle in the current study may be influenced by cross-talk contamination, which has been widely concerned in relevant previous researches ( Lowery et al, 2003 ; Farina et al, 2004 ; Wu et al, 2017 ). In the current research, an isometric low-force muscle contraction with only a 30% maximal voluntary contraction (MVC) as well as a much smaller size electrode (with a diameter of 6 mm and area of 28 mm 2 ) compared to the traditional electrode (with a diameter of 10 mm and area of 79 mm 2 ) were adopted, which has been proved to reduce cross-talk effectively ( Jaskolska et al, 2006 ; Farmer et al, 2007 ; Wang et al, 2020b ). Second, a repeated cross-over study design would be better to reduce interference of random factors and increase reliability and generality of the results, which would inevitably increase the cost of the experiment ( Machado S. et al, 2019 ; Machado D. G. D. S. et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Halo Sport Transcranial Direct Current Stimulation Improved Muscular Endurance Performance and Neuromuscular Efficiency During an Isometric Submaximal Fatiguing Elbow Flexion Task

Wang

Yang

Shao

et al. 2022

Front. Hum. Neurosci.

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The present study examined the effects of transcranial direct current stimulation (tDCS) using Halo Sport on the time to exhaustion (TTE) in relation with muscle activities and corticomuscular coupling of agonist and antagonist muscles during a sustained isometric fatiguing contraction performed with the elbow flexors. Twenty healthy male college students were randomly assigned to tDCS group and control group. The two group participants performed two experimental sessions which consisted of pre-fatigue isometric maximal voluntary contraction (MVC), sustained submaximal voluntary contractions (30% maximal torque) performed to exhaustion, and post-fatigue MVC with the right elbow flexor muscles. Sham stimulation (90 s) and tDCS (20 min) were applied for control and tDCS group participants 20 min prior to the second session test, respectively. MVC strength in pre- and post-fatigue test, TTE, electroencephalogram (EEG), and electromyography (EMG) of biceps brachii (BB) and triceps brachii (TB) were recorded during the tests. It was found that tDCS using the Halo Sport device significantly increased TTE and thus improved muscular endurance performance. The improvement may be partly related to the improvement of neuromuscular efficiency as reflected by decrease of antagonistic muscle coactivation activities, which may be related to cortical originated central processing mechanism of neuromuscular activities.

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Section: Discussionmentioning

confidence: 99%

Halo Sport Transcranial Direct Current Stimulation Improved Muscular Endurance Performance and Neuromuscular Efficiency During an Isometric Submaximal Fatiguing Elbow Flexion Task

Wang

Yang

Shao

et al. 2022

Front. Hum. Neurosci.

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“…The raw sEMG data was filtered using 4 th order zero phase lag bandpass Butterworth filter in the frequency range [5,500] Hz. This was done to remove low frequency noise (created by motion artifacts) and high frequency noise [65]- [67]. The goniometer data was filtered using 2 nd order low pass IIR filter at 30 Hz to remove high frequency noise and muscular vibrations [68]- [70].…”

Section: Part 2 Filteringmentioning

confidence: 99%

An Attention-based Deep CNN-BiLSTM Model for Forecasting of Fatigue-induced Surface Electromyography Signals During Isotonic Contractions

Bala¹,

Joshi²

2022

Preprint

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<p> An accurate estimation of muscle fatigue is critical for adaptive control of existing assistive devices, such as an exoskeleton, prosthesis, and functional electrical stimulation (FES)-based neuroprostheses. However, the estimation of muscle fatigue using surface electromyography (sEMG) for a long duration of time becomes challenging due to loosening of sEMG sensors, sweating, and other accidental failures. These problems can be potentially solved by forecasting future sEMG signals using initially recorded high-quality data points. For the first time, we attempt to forecast the fatigue-induced electromyography signal using the initial sEMG recorded for a shorter interval of time, during biceps curl with weights of 1 kg, 2 kg, 3 kg, and 4 kg. An attention-based deep CNN-BiLSTM neural network model that captures input sEMG dynamics to forecast future sEMG signals corresponding to fatigue state was trained and tested. An average mean absolute percentage error (MAPE) of 26.7% between forecasted and recorded sEMG was observed across eight subjects, five muscles, and four weights. In addition, the time domain features like integrated EMG (IEMG), root-mean-square (RMS) value, and variance of EMG (VEMG) were compared between forecasted and recorded sEMG (fatigue state), which yielded an average MAPE of 8%, 19.2%, and 31.7%, across eight subjects, five muscles, and four weights, for (IEMG and MAV), RMS, and (VEMG and SSI) respectively. The results encourage combining the proposed approach with wearable technology for forecasting fatigue-induced sEMG to drive stimulation devices like FES and robotic devices. </p>

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“…It has been widely investigated in the rehabilitation area, aiming at creating adaptive rehabilitation systems that be taken into account to make real-time adjustment to the interventions. In particular in stroke rehabilitation, the effects of muscular fatigue have been explored in patients with post-stroke spasticity which present abnormal antagonistic muscle co-activation patterns, because there exist a significant influence of muscle fatigue on the coupling of antagonistic muscles (Wang L.-J. et al, 2020) Comment on current/potential applications: The exploration of potential adaptive robotic system for rehabilitation using muscle fatigue as a trigger has been tested for improve engagement and performance (Meyer-Rachner et al, 2017;Mugnosso et al, 2018;Huang et al, 2019;Kanal et al, 2019).…”

Section: Muscle Fatiguementioning

confidence: 99%

Neuromechanical Biomarkers for Robotic Neurorehabilitation

et al. 2021

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One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

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Muscle Fatigue Enhance Beta Band EMG-EMG Coupling of Antagonistic Muscles in Patients With Post-stroke Spasticity

Cited by 14 publications

References 35 publications

Halo Sport Transcranial Direct Current Stimulation Improved Muscular Endurance Performance and Neuromuscular Efficiency During an Isometric Submaximal Fatiguing Elbow Flexion Task

Halo Sport Transcranial Direct Current Stimulation Improved Muscular Endurance Performance and Neuromuscular Efficiency During an Isometric Submaximal Fatiguing Elbow Flexion Task

An Attention-based Deep CNN-BiLSTM Model for Forecasting of Fatigue-induced Surface Electromyography Signals During Isotonic Contractions

Neuromechanical Biomarkers for Robotic Neurorehabilitation

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