EMGLAB: an interactive EMG decomposition program.

By K. McGill, Z. Lateva, H. Marateb. Journal of Neuroscience Methods, 2005

320 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/ff0f99e2ae28a30c0aae0ddf978d9cd659456e78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Dario Farina
2. Ales Holobar
3. Roger M. Enoka
4. Roberto Merletti
5. François Hug
6. S. Dorel

In Cellular and Molecular Neuroscience

1. Dario Farina
2. Ales Holobar

In Cognitive Neuroscience

1. Dario Farina
2. Ales Holobar
3. Roger M. Enoka
4. Roberto Merletti
5. François Hug
6. S. Dorel

Citations in the corpus

• The extraction of neural strategies from the surface EMG: an update. (2014, unknown): “B: surface and the intramuscular signals were independently decomposed using the CKC algorithm (51) (grey spike trains) and the EMG Lab method (76) (black spike trains), respectively.”
• Decoding the neural drive to muscles from the surface electromyogram. (2010, methodology): “Intramuscular EMG signals were concurrently recorded with two pairs of wire electrodes and single motor unit action potentials were identified by decomposition (McGill et al. 2005).”