Motor units are the smallest functional units of our skeletal muscles. The study of their activation provides a window into the mechanisms of neural control of movement in humans. The classic methods for motor unit investigations date to several decades ago. They are based on invasive recordings with selective needle or wire electrodes. Conversely, the noninvasive (surface) EMG has been commonly processed as an interference signal, with the extraction of its global characteristics, e.g., amplitude. These characteristics, however, are only crudely associated to the underlying motor unit activities. In the last decade, methods have been proposed for reliably extracting individual motor unit activities from the interference surface EMG signal. We describe these methods in this review, with a focus on blind source separation (BSS) and techniques used on decomposed EMG signals. For example, from the motor unit discharge timings, information can be extracted regarding the synaptic input received by the corresponding motor neurons. In reviewing these methods, we also provide examples of applications in representative conditions, such as pathological tremor.
COBISS.SI-ID: 19441174
We describe the method for identification of motor unit (MU) firings from high-density surface electromyograms (hdEMG), recorded during repeated dynamic muscle contractions. New convolutive data model for dynamic hdEMG is presented, along with Pulse-to-Noise Ratio (PNR) metric for assessment of MU identification accuracy and analysis of the impact of MU action potential (MUAP) changes in dynamic muscle contractions on MU identification. We tested the presented methodology on signals from biceps brachii, vastus lateralis and rectus famoris muscles, all during different speeds of dynamic contractions. In synthetic signals with excitation levels of 10%, 30% and 50% and MUAPs experimentally recorded from biceps brachii muscle, the presented method identified 15 ± 1, 18 ± 1 and 20 ± 1 MUs per contraction, respectively, all with average sensitivity and precision ) 90% and PNR ) 30 dB. In experimental signals acquired during low force contractions of vastus lateralis and rectus femoris muscle, the method identified 9.4 ± 1.9 and 7.8 ± 1.4 MUs with PNR values of 35.4 ± 3.6 and 34.1 ± 2.7 dB. Comparison with previously introduced Convolution Kernel Compensation (CKC) method confirmed the capability of the new method to follow dynamic MUAP changes, also in relatively fast muscle contractions.
COBISS.SI-ID: 21986838
We compared non-negative matrix factorization (NMF) and convolution kernel compensation techniques for high-density electromyogram decomposition. The experimental data were recorded from nine healthy persons during controlled single degree of freedom (DOF) wrist flexion-extension, supination-pronation, and ulnar-radial deviation movements. We assembled the identified motor units and NMF components into three groups. Those active mostly during the first and the second movement direction per DOF were placed in the G1 and G3 groups, respectively. The remaining components were nonspecific for movement direction and were placed in the G2 group. In ulnar and radial deviation, the relative energies of identified cumulative motor unit spike trains (CSTs) and NMF components were similarly distributed among the groups. In other two movement types, the energy of NMF components in the G2 group was significantly larger than the energy of CSTs. We further performed a coherence analysis between CSTs and sums of NMF components in each group. Both decompositions demonstrated a solid match, but only at frequencies (3 Hz. At higher frequencies, the coherence hardly exceeded the value of 0.5. Potential reasons for these discrepancies include the negative impact of motor unit action potential shapes and noise on NMF decomposition.
COBISS.SI-ID: 21717270
Many studies have demonstrated the feasibility and bene?ts of Conduction Velocity (CV) estimation from surface electromyograms (EMGs) in various experimental conditions. Among them, a method based on optical ?ow was proposed recently, demonstrating relatively accurate CV estimation for EMG signals acquired in monopolar mode. We extended this method by a new data model that compensates more realistically for the spatial Motor Unit Action Potential (MUAP) shape variability and enables accurate CV estimation also in single di?erential acquisition mode. The proposed modi?cation was validated on 5,000 synthetic Motor Units (MUs) with known CV and direction of ?bres. It was shown that the mean CV estimation error was signi?cantly lower for our proposed modi?cation compared to the original CV estimation procedure by up to 2% in the case of monopolar EMG signals and by up to 18.6% for single di?erential EMG signals. When estimating ?bre directions, the mean error was lower by up to 2.4 % (for monopolar EMG signals) and 9.6 % (for single di?erential EMG signals).
COBISS.SI-ID: 20327446
This book chapter describes advantages and limitations of surface electromyogram (sEMG) decomposition into contributions of individual motor units. First, generation and mixing process of sEMG is described in details and the convolutive data model of sEMG introduced. Next, we present, discuss and mutually compare different approaches to sEMG decomposition, including template matching and latent variable analysis approaches. Afterwards, techniques for validation of sEMG decomposition are thoroughly described, along with their limitations. Representativeness of motor unit identification from sEMG is also analysed. This is highly important issue, as sEMG decomposition identifies from ~5 to ~60 motor units per contraction, whereas several hundreds of motor units are usually active in muscle tissue. Finally, several applications of sEMG decomposition are briefly discussed.
COBISS.SI-ID: 19684374