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Identifikacija gruč neločljivih motoričnih enot iz večkanalnih površinskih elektromiogramov dvoglave nadlahtne mišice
Leon 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: 81; Downloads: 1
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4.
Materials for HybridNeuro webinar titled "Validation of results: statistical models and MU identification accuracy"
Aleš Holobar, Nina Murks, 2024, complete scientific database of research data

Abstract: This dataset contains a collection of teaching materials that were used in the HybridNeuro project webinar titled "Validation of results: statistical models and MU identification accuracy". The webinar was presented by Aleš Holobar and covered the complexities of motor unit (MU) identification accuracy, regression analysis and Bayesian models. The primary aim of the webinar was to spark a robust discussion within the scientific community, particularly focusing on the application and implications of linear mixed models and Bayesian regression in the realm of MU identification. The teaching materials include Matlab and R source code for statistical analysis of the included data, as well as three examples of MU identification results in CSV format (from both synthetic and experimental HDEMG signals). The presentation slides in PDF format are also included. The dataset is approximately 9 MB in size.
Keywords: HybridNeuro, webinar, teaching materials, statistical models, regression analysis, motor unit identification, matlab, rstudio, statistics, surface high density electromyogram (HDEMG), tibialis anterior, dataset
Published in DKUM: 30.05.2024; Views: 134; Downloads: 12
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5.
Simulated and experimental HDEMG signals of biceps brachii muscle for analysis of motor unit merging
Aleš 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: 91; Downloads: 4
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6.
Simulated HDEMG data of biceps brachii muscle during isometric elbow flexion
Aleš Holobar, Dario Farina, 2024, complete scientific database of research data

Abstract: This dataset contains a collection of 150 simulated surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. 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 30 seconds in length with 90 HDEMG channels and sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab’s .MAT format. The dataset is 6 GB in size.
Keywords: surface high density electromyogram (HDEMG), motor unit, simulation, biceps brachii, elbox flexion, dataset
Published in DKUM: 30.05.2024; Views: 173; Downloads: 27
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7.
Effects of jaw clenching and mental stress on persistent inward currents estimated by two different methods
Ricardo N. O. Mesquita, Janet Taylor, Gabriel Trajano, Aleš Holobar, Basilio Gonçalves, Anthony Blazevich, 2023, original scientific article

Abstract: Spinal motoneuron firing depends greatly on persistent inward currents (PICs), which in turn are facilitated by the neuromodulators serotonin and noradrenaline. The aim of this study was to determine whether jaw clenching (JC) and mental stress (MS), which may increase neuromodulator release, facilitate PICs in human motoneurons. The paired motor unit (MU) technique was used to estimate PIC contribution to motoneuron firing. Surface electromyograms were collected using a 32-channel matrix on gastrocnemius medialis (GM) during voluntary, ramp, plantar flexor contractions. MU discharges were identified, and delta frequency (ΔF), a measure of recruitment–derecruitment hysteresis, was calculated. Additionally, another technique was used (VibStim) that evokes involuntary contractions that persist after cessation of combined Achilles tendon vibration and triceps surae neuromuscular electrical stimulation. VibStim measures of plantar flexor torque and soleus activity may reflect PIC activation. ΔF was not significantly altered by JC (p = .679, n = 18, 9 females) or MS (p = .147, n = 14, 5 females). However, all VibStim variables quantifying involuntary torque and muscle activity during and after vibration cessation were significantly increased in JC (p < .011, n = 20, 10 females) and some, but not all, increased in MS (p = .017–.05, n = 19, 10 females). JC and MS significantly increased the magnitude of involuntary contractions (VibStim) but had no effect on GM ΔF during voluntary contractions. Effects of increased neuromodulator release on PIC contribution to motoneuron firing might differ between synergists or be context dependent. Based on these data, the background level of voluntary contraction and, hence, both neuromodulation and ionotropic inputs could influence neuromodulatory PIC enhancement.
Keywords: electromyography, bistability, HD-EMG, input–output function, motor neuron
Published in DKUM: 22.05.2024; Views: 58; Downloads: 1
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8.
Short example of Biceps Brachii muscle surface HDEMG decomposition using the DEMUSE Tool
Aleš Holobar, complete scientific database of research data

Abstract: This dataset contains 4 examples of synthetic high density surface EMG signals of the Biceps Brachii muscle and results of their decomposition into separate motor unit activity. It is intended as a demonstration of the DEMUSE Tool software for sEMG decomposition and as a basis for practical example of dataset preparation for the HybridNeuro project webinar on Data management and ethics (https://www.hybridneuro.feri.um.si/results.html#webinars). Two sets of data are included: the raw simulated sEMG signals and the results of decomposition of those signals with the DEMUSE Tool.
Keywords: surface high density electromyogram (HDEMG), decomposition, motor unit, DEMUSE, simulation, biceps brachii, dataset
Published in DKUM: 07.05.2024; Views: 194; Downloads: 16
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9.
Decoding firings of a large population of human motor units from high-density surface electromyogram in response to transcranial magnetic stimulation
Jakob 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: 338; Downloads: 9
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10.
ROSUS 2024 - Računalniška obdelava slik in njena uporaba v Sloveniji 2024 : Zbornik 18. strokovne konference
2024, proceedings

Abstract: ROSUS 2024 – Računalniška obdelava slik in njena uporaba v Sloveniji 2024 je strokovna računalniška konferenca, ki jo od leta 2006 naprej vsako leto organizira Inštitut za računalništvo iz Fakultete za elektrotehniko, računalništvo in informatiko, Univerze v Mariboru. Konferenca povezuje strokovnjake in raziskovalce s področij digitalne obdelave slik in strojnega vida z uporabniki tega znanja, pri čemer uporabniki prihajajo iz raznovrstnih industrijskih okolij, biomedicine, športa, zabavništva in sorodnih področij. Zbornik konference ROSUS 2024 združuje strokovne prispevke več avtorjev, od tega dve vabljeni predavanji ter več demonstracijskih prispevkov. Prispevki podajajo najnovejše dosežke slovenskih strokovnjakov s področij digitalne obdelave slik in strojnega vida, osvetljujejo pa tudi trende in novosti na omenjenih strokovnih področjih. Velik poudarek prispevkov je na promoviranju ekonomske koristnosti aplikacij računalniške obdelave slik in vida v slovenskem prostoru. Takšne računalniške aplikacije zaradi visoke natančnosti, robustnosti in izjemnih hitrosti pri obdelovanju informacij nudijo namreč nove priložnosti za uveljavitev na trgu visokih tehnologij.
Keywords: strojni vid, biomedicina, industrijske aplikacije, prenos znanja, računalniška obdelava slik
Published in DKUM: 12.03.2024; Views: 188; Downloads: 20
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