Relationship between EMG and fNIRS during Dynamic Movements

Abstract

In the scientific literature focused on surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS), which have been described together and separately many times, presenting different possible applications, researchers have explored a diverse range of topics related to these advanced physiological measurement techniques. However, the analysis of the two signals and their interrelationships continues to be a focus of study in both static and dynamic movements. The main purpose of this study was to determine the relationship between signals during dynamic movements. To carry out the analysis described, the authors of this research paper chose two sports exercise protocols: the Astrand–Rhyming Step Test and the Astrand Treadmill Test. In this study, oxygen consumption and muscle activity were recorded from the gastrocnemius muscle of the left leg of five female participants. This study found positive correlations between EMG and fNIRS signals in all participants: 0.343–0.788 (median-Pearson) and 0.192–0.832 (median-Spearman). On the treadmill, the signal correlations between the participants with the most active and least active lifestyle achieved the following medians: 0.788 (Pearson)/0.832 (Spearman) and 0.470 (Pearson)/0.406 (Spearman), respectively. The shapes of the changes in the EMG and fNIRS signals during exercise suggest a mutual relationship during dynamic movements. Furthermore, during the treadmill test, a higher correlation was observed between the EMG and NIRS signals in participants with a more active lifestyle. Due to the sample size, the results should be interpreted with caution.

Wavelet Analysis of Respiratory Muscle sEMG Signals during the Physiological Breakpoint of Static Dry End-Expiratory Breath-Holding in Naive Apneists: A Pilot Study

“…The observed links between the sEMG activity and BHD, as well as their interpretation in the context of MU recruitment in relation to the respiratory muscle's response to hypoxia/hypercapnia, are also supported by a recent study [58] in which an sEMG-NIRS correlation analysis was performed. Namely, using near-infrared spectroscopy (NIRS), as a standard method for measuring local tissue oxygenation, in correlation with sEMG activity of the locomotor muscle, a higher correlation between the EMG and NIRS signals was observed in participants with a more active lifestyle.…”

Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based Feature Selection Methods

“…For lower limb rehabilitation training, multichannel MMG signals were collected from the thigh, and feature selection methods based on two swarm intelligence algorithms were proposed for investigating pattern recognition of knee and ankle movement in sitting and standing positions, thereby demonstrating the relationship between classification accuracy and number of selected features. EMG sensors on the legs were adopted to represent the electrical activity of muscles during a lower limb movement [ 35 ]. Currently, studies about pattern recognition of lower limb movements based on sEMG, especially gait recognition are being applied to an exoskeleton [ 36 ].…”

“…EMG sensors on the legs were adopted to r resent the electrical activity of muscles during a lower limb movement [34]. Curren studies about pattern recognition of lower limb movements based on sEMG, especia gait recognition are being applied to an exoskeleton [35]. Compared with sEMG, anot…”

Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG)

“…During movement, participants were corrected based on the previous analysis of their rowing technique in terms of common errors such as leg straightening or pulling hands towards the rowing machine. During the exercise, participants were asked about their level of fatigue each minute using the Borg Rating of Perceived Exertion (RPE) scale ( Daniel et al, 2023 ). RPE is based on the physical sensations experienced by an individual during physical activity, including increased heart rate, increased breathing or breath frequency, increased perspiration, and muscle fatigue.…”

“…Each participant performed three tests, and in each trial, EMG signals from six muscles were recorded: L–GAS, R–GAS, L - RF, R–RF, L–BF and R–BF. Each EMG signal was pre-processed by applying a Butterworth filter with cutoff frequencies of 20 Hz and 500 Hz to delimit the physiological frequency band of the EMG signal and to remove high- and low-frequency interference ( Daniel et al, 2023 ). Data processing was carried out based on the diagram presented in Figure 2 .…”