BUSCADOR RECOLECTA

In some compound muscle action potentials (M waves) recorded using the belly–tendon configuration, the tendon electrode makes a noticeable contribution to the M wave. However, this finding has only been demonstrated in some hand and foot muscles. Here, we assessed the contribution of the tendon potential to the amplitude of the vastus lateralis, biceps brachii and tibialis anterior M waves, and we also examined the role of this tendon potential in the shoulder-like feature appearing in most M waves. M waves were recorded separately at the belly and tendon locations of the vastus lateralis, biceps brachii and tibialis anterior from 38 participants by placing the reference electrode at a distant (contralateral) site. The amplitude of the M waves and the latency of their peaks and shoulders were measured. In the vastus lateralis, the tendon potential was markedly smaller in amplitude (∼75%) compared to the belly M wave (P = 0.001), whereas for the biceps brachii and tibialis anterior, the tendon and belly potentials had comparable amplitudes. In the vastus lateralis, the tendon potential showed a small positive peak coinciding in latency with the shoulder of the belly–tendon M wave, whilst in the biceps brachii and tibialis anterior, the tendon potential showed a clear negative peak which coincided in latency with the shoulder. The tendon potential makes a significant contribution to the belly–tendon M waves of the biceps brachii and tibialis anterior muscles, but little contribution to the vastus lateralis M waves. The shoulder observed in the belly–tendon M wave of the vastus lateralis is caused by the belly potential, the shoulder in the biceps brachii M wave is generated by the tendon potential, whereas the shoulder in the tibialis anterior M wave is caused by both the tendon and belly potentials., Spanish Ministry of Science and Innovation, Grant/Award Number: PID2022-136620OB-I00

A central topic in Bioelectricity is the generation of the extracellular potential that results from the propagation of a transmembrane action potential along the muscle fiber. However, the way in which the extracellular potential is determined by the propagating action potential is difficult to describe, conceptualize, and visualize. Moreover, traditional quantitative approaches aimed at modeling extracellular potentials involve complex mathematical formulations, which do not allow students to visualize how the extracellular potential is generated around the active fiber. The present study is aimed at presenting a novel pedagogical approach to teaching the generation of extracellular potentials produced by muscle fibers based on the convolution operation. The effectiveness of this convolutional model was tested using a written exam and a satisfaction survey. Most students reported that a great advantage of this model was that it simplifies the problem by dividing it into three distinct components: 1) the input signal (associated with the action potential), 2) the impulse response (linked to the system formed by the fiber and the recording electrode), and 3) the output signal (the extracellular potential). Another key aspect of the present approach was that the input signal was represented by a sequence of electric dipoles, which allowed students to visualize the individual contribution of each dipole to the resulting extracellular potential. The results of the survey indicate that the combination of basic principles of electrical fields and intuitive graphical representations largely improves students' understanding of Bioelectricity concepts and enhances their motivation to complete their studies of biomedical engineering., This work was supported by the project PID2022-136620OB-I00 financed by the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033/FEDER, UE.

EMG filling curve characterizes the EMG filling process and EMG probability density function (PDF) shape change for the entire force range of a muscle.We aim to understand the relation between the physiological and recording variables, and the resulting EMG filling curves. We thereby present an analytical and simulation study to explain how the filling curve patterns relate to specific changes in the motor unit potential (MUP) waveforms and motor unit (MU) firing rates, the two main factors affecting the EMG PDF, but also to recording conditions in terms of noise level. We compare the analytical results with simulated cases verifying a perfect agreement with the analytical model. Finally, we present a set of real EMG filling curves with distinct patterns to explain the information about MUP amplitudes, MU firing rates, and noise level that these patterns provide in the light of the analytical study. Our findings reflect that the filling factor increases when firing rate increases or when newly recruited motor unit have potentials of smaller or equal amplitude than the former ones. On the other hand, the filling factor decreases when newly recruited potentials are larger in amplitude than the previous potentials. Filling curves are shown to be consistent under changes of the MUP waveform, and stretched under MUP amplitude scaling. Our findings also show how additive noise affects the filling curve and can even impede to obtain reliable information from the EMG PDF statistics., This work was supported by the Ministerio de Ciencia e Innovación of the Spanish Government under Grant PID2022-136620OB-I00.

Introduction: The EMG filling factor is an index to quantify the degree to which an EMG signal has been filled.
Here, we tested the validity of such index to analyse the EMG filling process as contraction force was slowly
increased.
Methods: Surface EMG signals were recorded from the quadriceps muscles of healthy subjects as force was
gradually increased from 0 to 40% MVC. The sEMG filling process was analyzed by measuring the EMG filling
factor (calculated from the non-central moments of the rectified sEMG).
Results: (1) As force was gradually increased, one or two prominent abrupt jumps in sEMG amplitude appeared
between 0 and 10% of MVC force in all the vastus lateralis and medialis.
(2) The jumps in amplitude were originated when a few large-amplitude MUPs, clearly standing out from previous activity, appeared in the sEMG signal.
(3) Every time an abrupt jump in sEMG amplitude occurred, a new stage of sEMG filling was initiated.
(4) The sEMG was almost completely filled at 2–12% MVC.
(5) The filling factor decreased significantly upon the occurrence of an sEMG amplitude jump, and increased as
additional MUPs were added to the sEMG signal.
(6) The filling factor curve was highly repeatable across repetitions.
Conclusions: It has been validated that the filling factor is a useful, reliable tool to analyse the sEMG filling
process. As force was gradually increased in the vastus muscles, the sEMG filling process occurred in one or two
stages due to the presence of abrupt jumps in sEMG amplitude., This work has been supported by the Spanish Ministry of Science and Innovation under the project PID2022-136620OB-I00.

Introduction: the probability density function (PDF) of the surface electromyogram (sEMG) depends on contraction force. This dependence, however, has so far been investigated by having the subject generate force at a few fixed percentages of MVC. Here, we examined how the shape of the sEMG PDF changes with contraction force when this force was gradually increased from zero.
Methods: voluntary surface EMG signals were recorded from the vastus lateralis of healthy subjects as force was increased in a continuous manner vs. in a step-wise fashion. The sEMG filling process was examined by measuring the EMG filling factor, computed from the non-central moments of the rectified sEMG signal.
Results: in 84% of the subjects, as contraction force increased from 0 to 10% MVC, the sEMG PDF shape oscillated back and forth between the semi-degenerate and the Gaussian distribution; the PDF–force relation varied greatly among subjects for forces between 0 and ~ 10% MVC, but this variability was largely reduced for forces above 10% MVC; the pooled analysis showed that, as contraction force gradually increased, the sEMG PDF evolved rapidly from the semi-degenerate towards the Laplacian distribution from 0 to 5% MVC, and then more slowly from the Laplacian towards the Gaussian distribution for higher forces.
Conclusions: the study demonstrated that the dependence of the sEMG PDF shape on contraction force can only be reliably assessed by gradually increasing force from zero, and not by performing a few constant-force contractions. The study also showed that the PDF–force relation differed greatly among individuals for contraction forces below 10% MVC, but this variability was largely reduced when force increased above 10% MVC., Open Access funding provided by Universidad Pública de Navarra. This work has been supported by the project PID2022-136620OB-I00 financed by Spanish Ministry of Science and Innovation MCIN/AEI/ https://doi.org/10.13039/501100011033/FEDER,UE.

Objectives: The progression of recruitment of motor unit potentials (MUPs) during increasing voluntary contraction can provide important information about the motor units (MUs) innervating a muscle. Here, we described a method to quantitate the recruitment level of the intramuscular electromyographic (iEMG) signal during an increasing force level. Methods: Concentric needle EMG signals were recorded from the tibialis anterior of healthy subjects as force was gradually increased from 0 to maximum force. The iEMG filling process was analyzed by measuring the EMG filling factor (FF), calculated from the mean rectified iEMG and the root mean square iEMG. Results: (1) The iEMG activity at low contraction forces was “discrete” (FF<0.3) for all participants. (2) The iEMG activity at maximal effort was “full” (FF>0.5) for 83 % of the participants, whereas it was “incompletely-reduced” (0.3<FF< 0.5) for 17 % of the participants. (3) The FF increased rapidly for forces up to 20 % MVC, and then levelled off for higher forces: thus, the FF curve had a typical exponential shape. Conclusions: The iEMG filling method can be considered of general applicability since the FF increased over a wide range in all healthy participants. Significance: The EMG filling analysis may have potential to detect scenarios of MU loss and remodelling in neurogenic and motor neuron diseases., This work has been supported by the Spanish Ministry of Science and Innovation under the project PID2022-136620OB-I00

Introduction: It has been shown that, for male subjects, the sEMG activity at low contraction forces is normally 'pulsatile', i.e., formed by a few large-amplitude MUPs, coming from the most superficial motor units. The subcutaneous layer thickness, known to be greater in females than males, influences the electrode detection volume. Here, we investigated the influence of the subcutaneous layer thickness on the type of sEMG activity (pulsatile vs. continuous) at low contraction forces. Methods: Voluntary surface EMG signals were recorded from the quadriceps muscles of healthy males and females as force was gradually increased from 0% to 40% MVC. The sEMG filling process was examined by measuring the EMG filling factor, computed from the non-central moments of the rectified sEMG signal. Results: 1) The sEMG activity at low contraction forces was ¿continuous¿ in the VL, VM and RF of females, whereas this sEMG activity was ¿pulsatile¿ in the VL and VM of males. 2) The filling factor at low contraction forces was lower in males than females for the VL (p = 0.003) and VM (p = 0.002), but not for the RF (p = 0.54). 3) The subcutaneous layer was significantly thicker in females than males for the VL (p = 0.001), VM (p = 0.001), and RF (p = 0.003). 4) A significant correlation was found in the vastus muscles between the subcutaneous layer thickness and the filling factor (p < 0.05). Discussion: The present results indicate that the sEMG activity at low contraction forces in the female quadriceps muscles is 'continuous' due to the thick subcutaneous layer of these muscles, which impedes an accurate assessment of the sEMG filling process., This work has been supported by the Spanish Ministry of Science and Innovation under the project PID 2022-136620OB-I00.