Patterns of motor recruitment can be determined using surface EMG |
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| Authors: | James M Wakeling |
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| Institution: | 1. Sobell Department of Motor Neuroscience & Movement Disorders, University College London Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK;2. Neuromed Institute IRCCS, Via Atinense 18, 86077 Pozzilli, IS, Italy;3. Boston University, Neurology Department, Boston, MA, USA;4. Department of Cell Sciences, St George’s University of London, Cranmer Terrace, London SW17, UK;1. NeuroMuscular Research Center, Boston University, Boston, MA 02215, USA;2. Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA;3. Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA;4. Department of Neurology, Boston University, Boston, MA 02215, USA;5. Department of Physical Therapy, Boston University, Boston, MA 02215, USA;6. Delsys Inc., Natick, MA 01760, USA;1. Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria;2. Department of Neurology, Franz Tappeiner Hospital, Merano, Italy;3. Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria;4. Department of Mathematics, Paris Lodron University, Salzburg, Austria;5. Department of Neurological, Neuropsychological, Morphological and Movement Sciences, Section of Clinical Neurology, University of Verona, Italy |
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| Abstract: | Previous studies have reported how different populations of motor units (MUs) can be recruited during dynamic and locomotor tasks. It was hypothesised that the higher-threshold units would contribute higher-frequency components to the sEMG spectra due to their faster conduction velocities, and thus recruitment patterns that increase the proportion of high-threshold units active would lead to higher-frequency elements in the sEMG spectra. This idea was tested by using a model of varying recruitment coupled to a three-layer volume conductor model to generate a series of sEMG signals. The recruitment varied from (A) orderly recruitment where the lowest-threshold MUs were initially activated and higher-threshold MUs were sequentially recruited as the contraction progressed, (B) a recurrent inhibition model that started with orderly recruitment, but as the higher-threshold units were activated they inhibited the lower-threshold MUs (C) nine models with intermediate properties that were graded between these two extremes. The sEMG was processed using wavelet analysis and the spectral properties quantified by their mean frequency, and an angle θ that was determined from the principal components of the spectra. Recruitment strategies that resulted in a greater proportion of faster MUs being active had a significantly lower θ and higher mean frequency. |
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