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 accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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 discarded (29).”
From Modulation of Neural and Muscular Adaptation Processes During Resistance Training by Fish Protein Ingestions in Older Adults (2020) · cites this paper as [29]
In the citing paper
“Only motor units with PNR > 30 dB (corresponding to an accuracy of motor unit firing identification > 90%) were used for further analysis, and all other motor units were discarded (Holobar et al., 2014).”
From Effect of resistance training and fish protein intake on motor unit firing pattern and motor function of elderly (2018) · cites this paper as Holobar et al., 2014
Citations
Cites
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
(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).”
MethodInfluentialMultichannel 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-citationKukulka, 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
Research works printed but not linked: 2
- Holobar A, 2013, IEEE NER Proc., 585
- Rosenfalck P, 1969, Acta Physiol. Scand. Suppl., 47, 239
Cited by
Sorted by citations
Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation
Negro, F., Muceli, S., Castronovo, A. M., Holobar, A., & Farina, D. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/2/026027 · Cited by 648
Self-citationThe extraction of neural strategies from the surface EMG: an update
Farina, D., Merletti, R., & Enoka, R. M. (2014) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00162.2014 · Cited by 590
Self-citationDel Vecchio, A., Casolo, A., Negro, F., Scorcelletti, M., Bazzucchi, I., Enoka, R., Felici, F., & Farina, D. (2019) · The Journal of Physiology · doi:10.1113/jp277250 · Cited by 436
Self-citationTutorial: Analysis of motor unit discharge characteristics from high-density surface EMG signals
Vecchio, A. D., Holobar, A., Falla, D., Felici, F., Enoka, R. M., & Farina, D. (2020) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2020.102426 · Cited by 373
Self-citationVecchio, A. D., Negro, F., Holobar, A., Casolo, A., Folland, J. P., Felici, F., & Farina, D. (2019) · The Journal of Physiology · doi:10.1113/jp277396 · Cited by 349
Self-citation
Metadata
- 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
- 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
- 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
- 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
