Cited by 400Muscle activation and electromyography studiesPeripheral Nerve DisordersMuscle Physiology and Disorders

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.

Open access

Holobar, A, Minetto, M A, Farina, D·2014·Journal of Neural Engineering

Open this paper in CoChat — free account

Abstract

OBJECTIVE: A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. APPROACH: The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. MAIN RESULTS: In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. SIGNIFICANCE: The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

Integrity signals

Registry

Crossref · 10.1088/1741-2560/11/1/016008

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Independent Component Analysis
Hyvärinen, A., Hoyer, P. O., & Inki, M. (2004) · The MIT Press eBooks · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,938
Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Erkki Oja, A. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,003
Relation between size of neurons and their susceptibility to discharge
Henneman, E. (1957) · Science · doi:10.1126/science.126.3287.1345 · Cited by 1,227
Electromyography
(2004) · Wiley eBooks · doi:10.1002/0471678384 · Cited by 1,165
Models of recruitment and rate coding organization in motor-unit pools.
Fuglevand, A. J., Winter, D. A., & Patla, A. E. (1993) · Journal of Neurophysiology · doi:10.1152/jn.1993.70.6.2470 · Cited by 988 · cited 3×
“The distribution of recruitment thresholds for the motor neurons was modelled with an exponential function with many low-threshold neurons and progressively fewer high-threshold neurons, as described by Fuglevand et al (1993).”
MethodInfluential
Multichannel Blind Source Separation Using Convolution Kernel Compensation
Holobar, A., & Zazula, D. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.896108 · Cited by 651
Self-citation
Comparison of the recruitment and discharge properties of motor units in human brachial biceps and adductor pollicis during isometric contractions
Kukulka, C. G., & Clamann, H. P. (1981) · Brain Research · doi:10.1016/0006-8993(81)90266-3 · Cited by 583
Decomposition of Surface EMG Signals
Luca, C. J. D., Adam, A., Wotiz, R. P., Gilmore, L., & Nawab, S. H. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00009.2006 · Cited by 513

Show all 31 references

Research works printed but not linked: 2

Holobar A, 2013, IEEE NER Proc., 585
Rosenfalck P, 1969, Acta Physiol. Scand. Suppl., 47, 239

Sorted by citations

Items 101–200 of 400

Non‐invasive estimation of muscle fibre size from high‐density electromyography
Casolo, A., Maeo, S., Balshaw, T. G., Lanza, M. B., Martin, N. R. W., Nuccio, S., Moro, T., Paoli, A., Felici, F., Maffulli, N., et al. (2023) · The Journal of Physiology · doi:10.1113/jp284170 · Cited by 29
Self-citation
Wrist Torque Estimation via Electromyographic Motor Unit Decomposition and Image Reconstruction
Yu, Y., Chen, C., Sheng, X., & Zhu, X. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3041861 · Cited by 28
Changes in neural drive to calf muscles during steady submaximal contractions after repeated static stretches
Mazzo, M. R., Weinman, L. E., Giustino, V., McLagan, B., Maldonado, J., & Enoka, R. M. (2021) · The Journal of Physiology · doi:10.1113/jp281875 · Cited by 28
Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors
Guerra, I. M., Barsakcioglu, D. Y., Vujaklija, I., Wetmore, D. Z., & Farina, D. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac5f1a · Cited by 25
Self-citation
Resistance exercise training and the motor unit
Herda, T. J. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-04983-7 · Cited by 27
Unlocking the full potential of high‐density surface EMG: novel non‐invasive high‐yield motor unit decomposition
Grison, A., Guerra, I. M., Clarke, A. K., Muceli, S., Ibáñez, J., & Farina, D. (2025) · The Journal of Physiology · doi:10.1113/jp287913 · Cited by 26
Self-citation
Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points
Mohebbian, M. R., Marateb, H. R., Karimimehr, S., Mañanas, M. Á., Kranjec, J., & Holobar, A. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00014 · Cited by 26
Self-citation
High-Density Surface EMG Decomposition: Achievements, Challenges, and Concerns
Chen, M., & Zhou, P. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3551630 · Cited by 25
Human motor unit discharge patterns reveal differences in neuromodulatory and inhibitory drive to motoneurons across contraction levels
Škarabot, J., Beauchamp, J. A., & Pearcey, G. E. P. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00249.2025 · Cited by 25
High-density surface EMG decomposition allows for recording of motor unit discharge from proximal and distal flexion synergy muscles simultaneously in individuals with stroke
McPherson, L. M., Thompson, C. K., Negro, F., Heckman, C. J., Farina, D., & Dewald, J. P. A. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944832 · Cited by 25
Self-citation
Estimation of Muscle Co-Activations in Wrist Rehabilitation After Stroke is Sensitive to Motor Unit Distribution and Action Potential Shapes
Potočnik, B., Divjak, M., Urh, F., Frančič, A., Kranjec, J., Šavc, M., Cikajlo, I., Matjačić, Z., Zadravec, M., & Holobar, A. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2980440 · Cited by 25
Self-citation
Modulation of Neural and Muscular Adaptation Processes During Resistance Training by Fish Protein Ingestions in Older Adults
Watanabe, K., Holobar, A., Mita, Y., Tomita, A., Yoshiko, A., Kouzaki, M., Uchida, K., & Moritani, T. (2020) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glz215 · Cited by 24
“The pulse-to-noise ratio, introduced by (29), was used as an indicator of the motor unit identification accuracy (29) and only motor units with pulse-to-noise ratio > 30 dB (corresponding to an accuracy of motor unit firing identification > 90%) were used for further analysis, whereas all other motor units were discar…”
MethodSelf-citation
Common synaptic inputs and persistent inward currents of vastus lateralis motor units are reduced in older male adults
Guo, Y., Jones, E. J., Škarabot, J., Inns, T. B., Phillips, B. E., Atherton, P. J., & Piasecki, M. (2024) · GeroScience · doi:10.1007/s11357-024-01063-w · Cited by 28
Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive
Yeung, D., Negro, F., & Vujaklija, I. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad33b0 · Cited by 24
Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue
Bingham, A., Arjunan, S. P., Jelfs, B., & Kumar, D. (2017) · Entropy · doi:10.3390/e19120697 · Cited by 24
Increased HCN channel driven inward rectification in benign cramp fasciculation syndrome
Czesnik, D., Howells, J., Negro, F., Wagenknecht, M., Hanner, S., Farina, D., Burke, D. J., & Paulus, W. (2015) · Brain · doi:10.1093/brain/awv254 · Cited by 23
Self-citation
On the Prediction of Motor Unit Filter Changes in Blind Source Separation of High-Density Surface Electromyograms During Dynamic Muscle Contractions
Kramberger, M., & Holobar, A. (2021) · IEEE Access · doi:10.1109/access.2021.3099015 · Cited by 23
Self-citation
Blockade of 5-HT2receptors suppresses rate of torque development and motor unit discharge rate during rapid contractions
Goodlich, B. I., Horan, S. A., & Kavanagh, J. J. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00470.2021 · Cited by 23
Real time identification of active regions in muscles from high density surface electromyogram
Mesin, L. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.10.017 · Cited by 23
Acute changes in motor unit discharge property after concentric versus eccentric contraction exercise in knee extensor
Hirono, T., Kunugi, S., Yoshimura, A., Holobar, A., & Watanabe, K. (2022) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2022.102704 · Cited by 23
Self-citation
Motor unit tracking using blind source separation filters and waveform cross-correlations: reliability under physiological and pharmacological conditions
Goodlich, B. I., Vecchio, A. D., & Kavanagh, J. J. (2023) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00271.2023 · Cited by 22
Altered Neuromodulatory Drive May Contribute to Exaggerated Tonic Vibration Reflexes in Chronic Hemiparetic Stroke
McPherson, J. G., McPherson, L. M., Thompson, C. K., Ellis, M. D., Heckman, C. J., & Dewald, J. P. A. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00131 · Cited by 22
Sex differences in laterality of motor unit firing behavior of the first dorsal interosseous muscle in strength-matched healthy young males and females
Nishikawa, Y., Watanabe, K., Holobar, A., Kitamura, R., Maeda, N., & Hyngstrom, A. S. (2024) · European Journal of Applied Physiology · doi:10.1007/s00421-024-05420-7 · Cited by 21
Self-citation
One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor
Dideriksen, J. L., Gallego, J. Á., Holobar, A., Rocón, E., Pons, J. L., & Farina, D. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046019 · Cited by 19
Self-citation
Neuromodulatory Contribution to Muscle Force Production after Short-Term Unloading and Active Recovery
Martino, G., Valli, G., Sarto, F., Franchi, M. V., Narici, M. V., & VITO, G. D. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003473 · Cited by 19
Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control
Chen, C., Yu, Y., Sheng, X., Meng, J., & Zhu, X. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3260209 · Cited by 20
Neuromorphic Decoding of Spinal Motor Neuron Behaviour During Natural Hand Movements for a New Generation of Wearable Neural Interfaces
Tanzarella, S., Iacono, M., Donati, E., Farina, D., & Bartolozzi, C. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3295658 · Cited by 25
Self-citation
The Effects of Spinal Manipulation on Motor Unit Behavior
Robinault, L., Holobar, A., Crémoux, S., Rashid, U., Niazi, I. K., Holt, K. R., Lauber, J., & Haavik, H. (2021) · Brain Sciences · doi:10.3390/brainsci11010105 · Cited by 19
Self-citation
Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations
Chen, C., Ma, S., Yu, Y., Sheng, X., & Zhu, X. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3192272 · Cited by 20
Can local vibration alter the contribution of persistent inward currents to human motoneuron firing?
Lapole, T., Mesquita, R. N. O., Baudry, S., Souron, R., Brownstein, C. G., & Rozand, V. (2023) · The Journal of Physiology · doi:10.1113/jp284210 · Cited by 19
Acute intermittent hypoxia increases maximal motor unit discharge rates in people with chronic incomplete spinal cord injury
Pearcey, G. E. P., Afsharipour, B., Holobar, A., Sandhu, M. S., & Rymer, W. Z. (2024) · The Journal of Physiology · doi:10.1113/jp285049 · Cited by 19
Self-citation
Tremor severity in Parkinson's disease and cortical changes of areas controlling movement sequencing: A preliminary study
Benito‐León, J., Serrano, J. I., Louis, E. D., Holobar, A., Romero, J. P., Povalej‐Bržan, P., Bermejo‐Pareja, F., del Castillo, M. D., Posada, I. J., & Rocón, E. (2018) · Journal of Neuroscience Research · doi:10.1002/jnr.24248 · Cited by 19
Self-citation
A wearable neural interface for detecting and decoding attempted hand movements in a person with tetraplegia
Ting, J. E., Farina, D., Weber, D. J., Vecchio, A. D., Friedenberg, D. A., Liu, M. F., Schoenewald, C., Sarma, D., Collinger, J. L., Colachis, S. C., et al. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856483 · Cited by 18
Self-citation
Robust Identification of Motor Unit Discharges From High-Density Surface EMG in Dynamic Muscle Contractions of the Tibialis Anterior
Yokoyama, H., Sasaki, A., Kaneko, N., Saito, A., & Nakazawa, K. (2021) · IEEE Access · doi:10.1109/access.2021.3107283 · Cited by 16
Association between effective neural drive to the triceps surae and fluctuations in plantar‐flexion torque during submaximal isometric contractions
Mazzo, M. R., Holobar, A., & Enoka, R. M. (2022) · Experimental Physiology · doi:10.1113/ep090228 · Cited by 18
Self-citation
A Novel and Efficient Surface Electromyography Decomposition Algorithm Using Local Spatial Information
Xu, Y., Yu, Y., Xia, M., Sheng, X., & Zhu, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3210019 · Cited by 18
Beta inputs to motor neurons do not directly contribute to volitional force modulation
Zicher, B., Ibáñez, J., & Farina, D. (2022) · The Journal of Physiology · doi:10.1113/jp283398 · Cited by 18
Self-citation
Distinct Neural Drives along the Semitendinosus Muscle
Sahinis, C., Amiridis, I. G., Kannas, T., Farina, D., Enoka, R. M., & Kellis, E. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003530 · Cited by 17
Self-citation
Neural determinants of the increase in muscle strength and force steadiness of the untrained limb following a 4 week unilateral training
Lecce, E., Amoruso, P., Vecchio, A. D., Casolo, A., Felici, F., Farina, D., & Bazzucchi, I. (2025) · The Journal of Physiology · doi:10.1113/jp288954 · Cited by 20
Self-citation
Properties of Motor Units of Elbow and Ankle Muscles Decomposed Using High-Density Surface EMG
Hassan, A. S., Kim, E. H., Khurram, O. U. H., Cummings, M., Thompson, C. K., McPherson, L. M., Heckman, C. J., Dewald, J. P. A., & Negro, F. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857475 · Cited by 18
Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics
Borzelli, D., Vieira, T., Botter, A., Gazzoni, M., Lacquaniti, F., & d’Avella, A. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00199.2023 · Cited by 17
Effect of milk fat globule membrane supplementation on motor unit adaptation following resistance training in older adults
Watanabe, K., Holobar, A., Tomita, A., & Mita, Y. (2020) · Physiological Reports · doi:10.14814/phy2.14491 · Cited by 17
Self-citation
High-threshold motor unit firing reflects force recovery following a bout of damaging eccentric exercise
Macgregor, L. J., & Hunter, A. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195051 · Cited by 17
Neural mechanisms underlying the recovery of voluntary control of motoneurons after paralysis with spinal cord stimulation
Balaguer, J.-M., Prat-Ortega, G., Ostrowski, J., Borda, L., Verma, N., Yadav, P., Sorensen, E., de Freitas, R., Ensel, S., Donadio, S., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.023 · Cited by 17
Differential behavior of distinct motoneuron pools that innervate the triceps surae
Cohen, J. W., Vieira, T., Ivanova, T. D., & Garland, S. J. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00336.2022 · Cited by 16
A peel-off convolution kernel compensation method for surface electromyography decomposition
Chen, C., Ma, S., Sheng, X., & Zhu, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104897 · Cited by 15
Longitudinal changes in intrinsic motoneuron excitability in amyotrophic lateral sclerosis are dependent on disease progression
Trajano, G. S., Orssatto, L. B. R., McCombe, P., Rivlin, W., Tang, L., & Henderson, R. D. (2023) · The Journal of Physiology · doi:10.1113/jp285181 · Cited by 15
Motor unit modes in the calf muscles during a submaximal isometric contraction are changed by brief stretches
Weinman, L. E., Vecchio, A. D., Mazzo, M. R., & Enoka, R. M. (2024) · The Journal of Physiology · doi:10.1113/jp285437 · Cited by 15
Motor unit discharge rate modulation during isometric contractions to failure is intensity‐ and modality‐dependent
Valenčič, T., Ansdell, P., Brownstein, C. G., Spillane, P., Holobar, A., & Škarabot, J. (2024) · The Journal of Physiology · doi:10.1113/jp286143 · Cited by 19
Self-citation
Improving Botulinum Toxin Efficiency in Treating Post-Stroke Spasticity Using 3D Innervation Zone Imaging
Zhang, C., Chen, Y.-T., Liu, Y., Magat, E., Verduzco‐Gutierrez, M., Francisco, G. E., Zhou, P., Li, S., & Zhang, Y. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065721500076 · Cited by 15
High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions
Bradford, J. C., Tweedell, A. J., & Leahy, L. (2023) · Scientific Data · doi:10.1038/s41597-023-02114-1 · Cited by 15
A New EMG Decomposition Framework for Upper Limb Prosthetic Systems
Wu, W., Jiang, L., Yang, B., Gong, K., Peng, C., & He, T. (2023) · Journal of Bionic Engineering · doi:10.1007/s42235-023-00407-0 · Cited by 14
High-Density Surface EMG Decomposition by Combining Iterative Convolution Kernel Compensation With an Energy-Specific Peel-off Strategy
Zheng, Y. P., Ma, Y., Liu, Y., Houston, M., Guo, C., Lian, Q., Li, S., Zhou, P., & Zhang, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3309546 · Cited by 14
Quercetin ingestion modifies human motor unit firing patterns and muscle contractile properties
Watanabe, K., & Holobar, A. (2021) · Experimental Brain Research · doi:10.1007/s00221-021-06085-w · Cited by 15
Self-citation
Improve computational efficiency and estimation accuracy of multi-channel surface EMG decomposition via dimensionality reduction
Ning, Y., Dias, N., Li, X., Jie, J., Li, J., & Zhang, Y. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103372 · Cited by 14
Neurophysiological correlates of force control improvement induced by sinusoidal vibrotactile stimulation
Germer, C. M., Vecchio, A. D., Negro, F., Farina, D., & Elias, L. A. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab5e08 · Cited by 14
Self-citation
Supraspinal control of motoneurons after paralysis enabled by spinal cord stimulationPreprint
Balaguer, J.-M., Prat-Ortega, G., Verma, N., Yadav, P., Sorensen, E., de Freitas, R., Ensel, S., Borda, L., Donadio, S., Liang, L., et al. (2023) · medRxiv · doi:10.1101/2023.11.29.23298779 · Cited by 13
Footedness but not dominance influences force steadiness during isometric dorsiflexion in young men
Petrovic, I., Amiridis, I. G., Kannas, T., Tsampalaki, Z., Holobar, A., Sahinis, C., Kellis, E., Stanković, D., & Enoka, R. M. (2023) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2023.102828 · Cited by 13
Self-citation
A Bi-GRU-attention neural network to identify motor units from high-density surface electromyographic signals in real time
Lin, C., Chen, C., Cui, Z., & Zhu, X. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1306054 · Cited by 13
Identification of the laterality of motor unit behavior in female patients with parkinson's disease using high‐density surface electromyography
Nishikawa, Y., Watanabe, K., Holobar, A., Maeda, N., Maruyama, H., & Tanaka, S. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.15099 · Cited by 16
Self-citation
Standard intensities of transcranial alternating current stimulation over the motor cortex do not entrain corticospinal inputs to motor neurons
Ibáñez, J., Zicher, B., Brown, K. E., Rocchi, L., Casolo, A., Vecchio, A. D., Spampinato, D. A., Vollette, C.-A., Rothwell, J. C., Baker, S. N., et al. (2022) · The Journal of Physiology · doi:10.1113/jp282983 · Cited by 13
Self-citation
Motor unit distribution and recruitment in spastic and non-spastic bilateral biceps brachii muscles of chronic stroke survivors
Liu, Y., Chen, Y., Zhang, C., Zhou, P., Li, S., & Zhang, Y. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac86f4 · Cited by 14
Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo
Gogeascoechea, A., Ornelas-Kobayashi, R., Yavuz, U. Ş., & Sartori, M. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3319959 · Cited by 13
I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humansPreprint
Rossato, J., Hug, F., Tucker, K. J., Gibbs, C., Lacourpaille, L., Farina, D., & Avrillon, S. (2023) · eLife · doi:10.7554/elife.88670 · Cited by 13
Self-citation
The History and Future of Neuromusculoskeletal Biomechanics
Lloyd, D. G., Jonkers, I., Delp, S. L., & Modenese, L. (2023) · Journal of Applied Biomechanics · doi:10.1123/jab.2023-0165 · Cited by 13
Common synaptic input between motor units from the lateral and medial posterior soleus compartments does not differ from that within each compartment
Aeles, J., Sarcher, A., & Hug, F. (2022) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00587.2022 · Cited by 13
A comparison of neural control of the biarticular gastrocnemius muscles between knee flexion and ankle plantar flexion
Hamard, R., Aeles, J., Avrillon, S., Dick, T. J. M., & Hug, F. (2023) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00075.2023 · Cited by 12
Caffeine does not influence persistent inward current contribution to motoneuron firing
Mackay, K., Orssatto, L. B. R., Polman, R. C. J., van der Pols, J. C., & Trajano, G. S. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00350.2023 · Cited by 12
Greater motor unit discharge rate during rapid contractions in chronically strength-trained individuals
Škarabot, J., Casolo, A., Balshaw, T. G., Maeo, S., Lanza, M. B., Holobar, A., Farina, D., Folland, J. P., & Vecchio, A. D. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00017.2024 · Cited by 14
Self-citation
Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients
Puttaraksa, G., Muceli, S., Barsakcioglu, D. Y., Holobar, A., Clarke, A. K., Charles, S. K., Pons, J. L., & Farina, D. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3158606 · Cited by 13
Self-citation
Myolink: A 128-Channel, $\text{18 nV}/\sqrt{\text{Hz}}$, Embedded Recording System, Optimized for High-Density Surface Electromyogram Acquisition
Koutsoftidis, S., Barsakcioglu, D. Y., Petkos, K., Farina, D., & Drakakis, E. M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3170025 · Cited by 14
Self-citation
Person-Specific Biophysical Modeling of Alpha-Motoneuron Pools Driven by in vivo Decoded Neural Synaptic Input
Ornelas-Kobayashi, R., Gogeascoechea, A., & Sartori, M. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3247873 · Cited by 11
Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection
Xu, Y., Yu, Y., Zhao, Z., Chen, C., & Sheng, X. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3309662 · Cited by 12
Simulations of high-density surface electromyograms in dynamic muscle contractions
Glaser, V., Farina, D., & Holobar, A. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037599 · Cited by 12
Self-citation
Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms
Šavc, M., Glaser, V., Kranjec, J., Cikajlo, I., Matjačić, Z., & Holobar, A. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2869426 · Cited by 12
Self-citation
Networks of common inputs to motor neurons of the lower limb reveal neural synergies that only partly overlap with muscle innervationPreprint
Hug, F., Avrillon, S., Sarcher, A., Vecchio, A. D., & Farina, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.13.460524 · Cited by 12
Self-citation
Antagonism of 5‐HT2 receptors attenuates self‐sustained firing of human motor units
Goodlich, B. I., Pearcey, G. E. P., Vecchio, A. D., Horan, S. A., & Kavanagh, J. J. (2024) · The Journal of Physiology · doi:10.1113/jp285867 · Cited by 11
Neuromuscular characteristics of front and back legs in junior fencers
Watanabe, K., Yoshimura, A., Holobar, A., Yamashita, D., Kunugi, S., & Hirono, T. (2022) · Experimental Brain Research · doi:10.1007/s00221-022-06403-w · Cited by 12
Self-citation
Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion
Úbeda, A., Vecchio, A. D., Sartori, M., Méndez, S. T. P., Torres, F., Azorín, J. M., & Farina, D. (2017) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2017.8009223 · Cited by 11
Self-citation
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms
Holobar, A., Gallego, J. Á., Kranjec, J., Rocón, E., Romero, J. P., Benito‐León, J., Pons, J. L., & Glaser, V. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00879 · Cited by 11
Self-citation
On the Selection of Neural Network Architecture for Supervised Motor Unit Identification from High-Density Surface EMG
Urh, F., Strnad, D., Clarke, A., Farina, D., & Holobar, A. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176294 · Cited by 10
Self-citation
On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms
Glaser, V., & Holobar, A. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036854 · Cited by 11
Self-citation
Motor Unit Discharge Characteristics and Conduction Velocity of the Vastii Muscles in Long-Term Resistance-Trained Men
Škarabot, J., Folland, J. P., FORSYTH, J., VAZOUKIS, A., Holobar, A., & Vecchio, A. D. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003105 · Cited by 10
Self-citation
Influence of the variability in motor unit discharge times and neural drive on force steadiness during submaximal contractions with a hand muscle
Tvrdy, T. J., Henry, M., & Enoka, R. M. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00333.2024 · Cited by 11
Task-specific motor units in the extrinsic hand muscles control single- and multidigit tasks of the human hand
Oßwald, M., Cakici, A. L., de Oliveira, D. S., Braun, D. I., Farina, D., & Vecchio, A. D. (2025) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00911.2024 · Cited by 10
Self-citation
Cumulative Spike Train Outperforms the Root-Mean-Square Metric in Muscle Excitation Estimation from Dynamic High-Density EMG
Holobar, A., & Glaser, V. (2018) · Biosystems & biorobotics · doi:10.1007/978-3-030-01845-0_228 · Cited by 10
Self-citation
Improved Assessment of Muscle Excitation From Surface Electromyograms in Isometric Muscle Contractions
Kranjec, J., & Holobar, A. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2922453 · Cited by 10
Self-citation
Postural Muscle Unit Plasticity in Stroke Survivors: Altered Distribution of Gastrocnemius' Action Potentials
Vieira, T., Lemos, T., de Oliveira, L. A. S., Horsczaruk, C. H. R., de Freitas, G. R., Tovar-Moll, F. F., & Rodrigues, É. C. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00686 · Cited by 10
Fish Protein Ingestion Induces Neural, but Not Muscular Adaptations, Following Resistance Training in Young Adults
Watanabe, K., Holobar, A., Uchida, K., & Mita, Y. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.645747 · Cited by 10
Self-citation
The effects of α-lactalbumin supplementation and handgrip contraction on soleus motoneuron excitability
Phillips, K. M., Orssatto, L. B. R., Polman, R. C. J., van der Pols, J. C., & Trajano, G. S. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-05101-3 · Cited by 10
Longitudinal development of muscle strength and relationship with motor unit activity and muscle morphological characteristics in youth athletes
Okudaira, M., Hirono, T., Takeda, R., Nishikawa, T., Ueda, S., Mita, Y., Holobar, A., Yoshimura, A., & Watanabe, K. (2023) · Experimental Brain Research · doi:10.1007/s00221-023-06590-0 · Cited by 10
Self-citation
Surface EMG Decomposition
Holobar, A., Farina, D., & Zazula, D. (2016) · doi:10.1002/9781119082934.ch07 · Cited by 10
Self-citation
Projection of Cortical Beta Band Oscillations to a Motor Neuron Pool across the Full Range of Recruitment
Abbagnano, E., Valdunciel, A. P., Zicher, B., Ibáñez, J., & Farina, D. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0453-25.2025 · Cited by 9
Self-citation
Three-dimensional amplitude characteristics of masseter motor units and representativeness of extracted motor unit samples
Lapatki, B. G., Eiglsperger, U., Schindler, H. J., Radeke, J., Holobar, A., & van Dijk, J. P. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2018.12.008 · Cited by 9
Self-citation
A Lower Limb Phantom for Simulation and Assessment of Electromyography Technology
Schlink, B. R., & Ferris, D. P. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2944297 · Cited by 9
Transcutaneous innervation zone imaging from high-density surface electromyography recordings
Liu, Y., Zhang, C., Dias, N., Chen, Y., Li, S., Zhou, P., & Zhang, Y. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab673e · Cited by 9
Motor unit firing patterns on increasing force during force and position tasks
Kunugi, S., Holobar, A., Kodera, T., Toyoda, H., & Watanabe, K. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00299.2021 · Cited by 9
Self-citation
Horizontal foot orientation affects the distribution of neural drive between gastrocnemii during plantarflexion, without changing neural excitability
Crouzier, M., Avrillon, S., Hug, F., & Cattagni, T. (2024) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00536.2023 · Cited by 7
Ageing does not impair motor neuron adaptations: comparable motor unit responses to strength training in young and older adults
Casolo, F., Vecchio, S. D., Goodlich, B. I., Schrader, B., Nuccio, S., Lecce, E., Bazzucchi, I., Angius, L., Felici, F., Schrader, J., et al. (2026) · The Journal of Physiology · doi:10.1113/jp290541 · Cited by 8
Self-citation
Identification of Motor Unit Firings in H-Reflex of Soleus Muscle Recorded by High-Density Surface Electromyography
Kalc, M., Škarabot, J., Divjak, M., Urh, F., Kramberger, M., Vogrin, M., & Holobar, A. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3217450 · Cited by 8
Self-citation

Authors: Holobar, A., Minetto, M. A., & Farina, D.
Affiliations: University of Maribor (SI)
University of Turin (IT)
Bernstein Center for Computational Neuroscience Göttingen (DE)
Corresponding author: Holobar, A
Minetto, M A
Farina, D
ORCID: Holobar, A
Minetto, M A
Farina, D
Venue: Journal of Neural Engineering
Publisher: IOP Publishing
Published: 2014-01-08
Volume: 11
Issue: 1
Pages: 016008
ISSN: 1741-2552 (ISSN-L)
1741-2560
DOI: 10.1088/1741-2560/11/1/016008
OpenAlex: W2042861111
Cited by: 400
References: 31
Citations by year: 2026: 33 · 2025: 56 · 2024: 48 · 2023: 52 · 2022: 41 · 2021: 40 · 2020: 29 · 2019: 28 · 2018: 20 · 2017: 15 · 2016: 16 · 2015: 9 · 2014: 13
FWCI: 9.17
Type: journal article
Language: en
Open access: green
dk.um.si
Licence: other-oa
iopscience.iop.org/info/page/text-and-data-mining (tdm)
iopscience.iop.org/page/copyright (vor)
Locations: Journal of Neural Engineering (publishedVersion)
PubMed (publishedVersion)
University of Maribor digital library (University of Maribor) (publishedVersion, other-oa)
GRO (submittedVersion)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Health Sciences › Medicine › Clinical Neurology › Peripheral Nerve Disorders
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
Keywords: motor unit identification, high-density surface electromyography, motor unit firing patterns, signal-to-noise ratio, muscle contraction force, motor unit decomposition
MeSH terms: Algorithms, Data Interpretation, Statistical, Electromyography, Humans, Isometric Contraction, Motor Neurons, Muscle Contraction, Signal Processing, Computer-Assisted, Reproducibility of Results, Linear Models, Muscle, Skeletal, Muscle Fibers, Skeletal, Signal-To-Noise Ratio

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks