We study the changes of Motor Unit (MU) filters in blind source separation of high-density surface electromyograms, recorded during dynamic contractions of biceps brachii muscle. We demonstrate that these changes can be predicted locally by linearly extrapolating previously recorded changes, allowing for the linear prediction-correction paradigm of MU filter updating. We then demonstrate the efficiency of this paradigm by implementing MU filter updating by the Kalman filter and integrating it into the previously published Convolution Kernel Compensation (CKC) method. When compared to the original CKC method and the previously published cyclostationary CKC method devoted to MU identification in repeated dynamic contractions, the Kalman based MU filter prediction yielded a superior number of identified MUs and superior precision of MU firing identification. In the case of relatively fast biceps brachii contractions with full elbow flexion and extension in 2s, the Kalman based MU filter prediction identified 21.3±1.8 MUs with average sensitivity of 95.6 ± 7.0% and precision of 96.5 ± 3.5%. In the same conditions the original CKC method identified 7.1 ± 2.0 MUs with average sensitivity of 62.7 ± 20.1% and precision of 98.1 ± 3.9%, whereas cyclostationary CKC identified 18.9 ± 2.0 MUs with average sensitivity of 91.8 ± 12.2% and precision of 94.7 ± 5.1%.

We developed and tested the methodology that supports the identification of individual motor unit (MU) firings from the Hoffman (or H) reflex recorded by surface high-density EMG (HD-EMG). Synthetic HD-EMG signals were constructed from simulated 10% to 90% of maximum voluntary contraction -MVC, followed by 100 simulated H-reflexes. In each H-reflex the MU firings were normally distributed with mean latency of 20 ms and standard deviations (SDLAT) ranging from 0.1 to 1.3 ms. Experimental H-reflexes were recorded from the soleus muscle of 12 men (33.6 ± 5.8 years) using HD-EMG array of 5×13 surface electrodes. Participants performed 15 to 20 s long voluntary plantarflexions with contraction levels ranging from 10% to 70% MVC. Afterwards, at least 60 H-reflexes were electrically elicited at three levels of background muscle activity: rest, 10% and 20% MVC. HD-EMGs of voluntary contractions were decomposed using the Convolution Kernel Compensation method to estimate the MU filters. When applied to HD-EMG signals with synthetic H reflexes, MU filters demonstrated high MU identification accuracy, especially for SDLAT > 0.3 ms. When applied to experimental H-reflex recordings, the MU filters identified 14.1 ± 12.1, 18.2 ± 12.1 and 20.8 ± 8.7 firings per H-reflex, with individual MU firing latencies of 35.9 ± 3.3, 35.1 ± 3.0 and 34.6 ± 3.3 ms for rest, 10% and 20% MVC background muscle activity, respectively. Standard deviation of MU latencies across experimental H-reflexes were 1.0 ± 0.8, 1.3 ± 1.1 and 1.5 ± 1.2 ms, in agreement with intramuscular EMG studies.

We describe and test the methodology supporting the identification of individual motor unit (MU) firings in the motor response (M wave) to percutaneous nerve stimulation recorded by surface high-density electromyography (HD-EMG) on synthetic and experimental data. Methods: A set of simulated voluntary contractions followed by 100 simulated M waves with a normal distribution (MU mean firing latency: 10 ms, Standard Deviation -SDLAT 0.1-1.3 ms) constituted the synthetic signals. In experimental condition, at least 52 progressively increasing M waves were elicited in the soleus muscle of 12 males, at rest (REST), and at 10% (C10) and 20% (C20) of maximal voluntary contraction (MVC). The MU decomposition filters were identified from 15-20 s long isometric plantar flexions performed at 10-70% of MVC and, afterwards, applied to M waves. Results: Synthetic signal analysis demonstrated high accuracy of MU identification in M waves (precision ≥ 85%). In experimental conditions 42.6 ± 11.2 MUs per participant were identified from voluntary contractions. When the MU filters were applied to the M wave

No abstract

No abstract