1. Tutorial on editing the decomposition results from isometric contractionsNina Murks, Jakob Škarabot, Aleš Holobar, 2025, dictionary, encyclopaedia, lexicon, manual, atlas, map Abstract: This tutorial focuses on editing decomposition results from high-density electromyograms. It covers the most common scenarios that may arise during the editing process and demonstrates how to resolve them using the CKC Inspector in the Demuse tool.
Keywords: Editing, decomposisition results, dhEMG, Demuse, CKC inspector
Published in DKUM: 12.02.2025; Views: 0; Downloads: 66
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2. Motor unit discharge rate modulation during isometric contractions to failure is intensity- and modality-dependentTamara Valenčič, Paul Ansdell, Callum G. Brownstein, Padraig M. Spillane, Aleš Holobar, Jakob Škarabot, 2024, original scientific article Abstract: The physiological mechanisms determining the progressive decline in the maximal muscle torque production capacity during isometric contractions to task failure are known to depend on task demands. Task-specificity of the associated adjustments in motor unit discharge rate (MUDR), however, remains unclear. This study examined MUDR adjustments during different submaximal isometric knee extension tasks to failure. Participants performed a sustained and an intermittent task at 20% and 50% of maximal voluntary torque (MVT), respectively (Experiment 1). High-density surface EMG signals were recorded from vastus lateralis (VL) and medialis (VM) and decomposed into individual MU discharge timings, with the identified MUs tracked from recruitment to task failure. MUDR was quantified and normalised to intervals of 10% of contraction time (CT). MUDR of both muscles exhibited distinct modulation patterns in each task. During the 20% MVT sustained task, MUDR decreased until ∼50% CT, after which it gradually returned to baseline. Conversely, during the 50% MVT intermittent task, MUDR remained stable until ∼40–50% CT, after which it started to continually increase until task failure. To explore the effect of contraction intensity on the observed patterns, VL and VM MUDR was quantified during sustained contractions at 30% and 50% MVT (Experiment 2). During the 30% MVT sustained task, MUDR remained stable until ∼80–90% CT in both muscles, after which it continually increased until task failure. During the 50% MVT sustained task the increase in MUDR occurred earlier, after ∼70–80% CT. Our results suggest that adjustments in MUDR during submaximal isometric contractions to failure are contraction modality- and intensity-dependent.
Keywords: muscle contractions, high-density EMG signals, electromyiograms
Published in DKUM: 23.08.2024; Views: 117; Downloads: 10
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3. Motor unit identification in the M waves recorded by high-density electromyMiloš Kalc, Jakob Škarabot, Matjaž Divjak, Filip Urh, Matej Kramberger, Matjaž Vogrin, Aleš Holobar, 2023, original scientific article Keywords: M wave, high-density surface EMG, firing identification, motor unit
Published in DKUM: 13.06.2024; Views: 144; Downloads: 15
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4. Identification of motor unit firings in H-reflex of soleus muscle recorded by high-density surface electromyographyMiloš Kalc, Jakob Škarabot, Matjaž Divjak, Filip Urh, Matej Kramberger, Matjaž Vogrin, Aleš Holobar, 2023, original scientific article Keywords: motor units identification, high-density surface EMG, decomposition, H-reflex
Published in DKUM: 13.06.2024; Views: 133; Downloads: 15
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5. Identifikacija gruč neločljivih motoričnih enot iz večkanalnih površinskih elektromiogramov dvoglave nadlahtne mišiceLeon Kutoš, Jakob Škarabot, Aleš Holobar, 2023, published scientific conference contribution Abstract: We analyzed the capability of previously introduced Convolution Kernel Compensation (CKC) method to identify clusters of motor units (MUs) that share similar motor unit action potentials (MUAPs) and, therefore, cannot be mutually discriminated by the decomposition of high-density surface electromyograms (hdEMG). The tests were performed on biceps brachii muscle because hdEMG decomposition yields relatively small number of individual MUs in this muscle.
In this study, we analyzed how many MUs of biceps brachii get merged into the same spike train by the CKC method due to MUAP similarity and what are the sensitivity and precision of MU discharge identification in merged spike trains. We compared these metrics with the identification of individual MUs in both synthetic and experimental hdEMG. In synthetic hdEMG with 20 dB noise, the number of identified MUs increased from 5.2 ± 2.8 (individual MUs) to 16.4±8.4 MUs when merged MU spike trains were taken into consideration, in addition to individual MUs. Discharges of individual MUs were identified with sensitivity of 77.0±15.8 % and precision of 86.1±25.4 %, whereas the merg ed MUs were identified with sensitivity of 79.6±15.7 % and precision of 97.4±11.8 %. Similar results were observed also for noiseless hdEMG.
In experimental hdEMG signals from biceps brachii muscle of two young healthy individuals, the number of identified MUs increased from 7.5±2.4 (individual MUs) to 21.9±17.6 MUs (merged MU spike trains). Individual MUs were identified with sensitivity of 83.3±15.9 % and precision of 80.0±19.0 %, whereas when considering also merged MUs, the sensitivity and precision of MU discharge identification increased to 82.3±22.4 % and 94.9±11.4 %, respectively.
In conclusion, the merged MU spike trains obtained by hdEMG decompositions carry important information about the activity of skeletal muscles and can be used to increase the number of MUs identified from hdEMG.
Keywords: površinski elektromiogram (EMG), motorična enota, vlak impulzov, zlivanje motoričnih enot, biceps brachii
Published in DKUM: 30.05.2024; Views: 175; Downloads: 10
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6. Simulated and experimental HDEMG signals of biceps brachii muscle for analysis of motor unit mergingAleš Holobar, Jakob Škarabot, Dario Farina, 2024, complete scientific database of research data Abstract: This dataset contains a collection of simulated and experimental surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. Simulated data contains 50 recordings: 5 subjects and 5 excitation levels, each with and without added noise. Experimental data contains 16 recordings: 2 subjects with 4 excitation levels and 2 repetitions of each level. Synthetic data was simulated using the cylindrical volume conductor model [1] and the motor unit recruitment and firing modulation model proposed in [2]. Each recording is 20 seconds in length with 90 HDEMG channels sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab’s MAT format. Experimental surface EMG data was recorded on two volunteers during isometric contractions at constant force level. Each recording is 25 seconds in length with 64 HDEMG channels sampled at 2048 Hz and is also stored as a 2D matrix of raw EMG values in Matlab’s MAT format. The dataset is approximately 1.5 GB in size.
Keywords: surface high density electromyogram (HDEMG), motor unit, spike train, motor unit merging, simulated data, experimental data, biceps brachii, dataset
Published in DKUM: 30.05.2024; Views: 172; Downloads: 23
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7. Decoding firings of a large population of human motor units from high-density surface electromyogram in response to transcranial magnetic stimulationJakob Škarabot, Claudia Ammann, Thomas G. Balshaw, Matjaž Divjak, Filip Urh, Nina Murks, Guglielmo Foffani, Aleš Holobar, 2023, original scientific article Abstract: We describe a novel application of methodology for high-density surface electromyography (HDsEMG) decomposition to identify motor unit (MU) firings in response to transcranial magnetic stimulation (TMS). The method is based on the MU filter estimation from HDsEMG decomposition with convolution kernel compensation during voluntary isometric contractions and its application to contractions elicited by TMS. First, we simulated synthetic HDsEMG signals during voluntary contractions followed by simulated motor evoked potentials (MEPs) recruiting an increasing proportion of the motor pool. The estimation of MU filters from voluntary contractions and their application to elicited contractions resulted in high (>90%) precision and sensitivity of MU firings during MEPs. Subsequently, we conducted three experiments in humans. From HDsEMG recordings in first dorsal interosseous and tibialis anterior muscles, we demonstrated an increase in the number of identified MUs during MEPs evoked with increasing stimulation intensity, low variability in the MU firing latency and a proportion of MEP energy accounted for by decomposition similar to voluntary contractions. A negative relationship between the MU recruitment threshold and the number of identified MU firings was exhibited during the MEP recruitment curve, suggesting orderly MU recruitment. During isometric dorsiflexion we also showed a negative association between voluntary MU firing rate and the number of firings of the identified MUs during MEPs, suggesting a decrease in the probability of MU firing during MEPs with increased background MU firing rate. We demonstrate accurate identification of a large population of MU firings in a broad recruitment range in response to TMS via non-invasive HDsEMG recordings. KEY POINTS: Transcranial magnetic stimulation (TMS) of the scalp produces multiple descending volleys, exciting motor pools in a diffuse manner. The characteristics of a motor pool response to TMS have been previously investigated with intramuscular electromyography (EMG), but this is limited in its capacity to detect many motor units (MUs) that constitute a motor evoked potential (MEP) in response to TMS. By simulating synthetic signals with known MU firing patterns, and recording high-density EMG signals from two human muscles, we show the feasibility of identifying firings of many MUs that comprise a MEP. We demonstrate the identification of firings of a large population of MUs in the broad recruitment range, up to maximal MEP amplitude, with fewer required stimuli compared to intramuscular EMG recordings. The methodology demonstrates an emerging possibility to study responses to TMS on a level of individual MUs in a non-invasive manner.
Keywords: transcranial magnetic stimulation, TMS, electromyiograms
Published in DKUM: 10.04.2024; Views: 421; Downloads: 22
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