PURPOSE: This study investigated muscle coordination while pedaling at 150 and 300 W with a cadence of 90 rpm. Changes in the variability of the electromyographic (EMG) signals were quantified in 14 subjects. METHODS: Principal component analysis was used to find correlated EMG patterns among seven leg muscles that reflect neuromuscular strategies while pedaling. Sample entropy was used to assess the regularity of the short-term fluctuations of the EMG. Signal structure relates to the autocorrelation and to the information in the phase of the signal. This study used the information encrypted in the phase to quantify neuromuscular control and compared the results to phase-randomized surrogate data. RESULTS: Although the pattern remained similar, the correlation between individual muscles showed effort-dependent differences. Increased workload altered the overall neuromuscular strategy indicated by changes in the contribution of individual muscles to the movement. Additionally, the executed strategy was characterized by increased structure. Regularity of the short-term fluctuations in the EMG increased significantly with effort level. Both experimental conditions showed more structure in the phase of the EMG compared to the surrogate data. CONCLUSIONS: This increased structure in the EMG signal may represent a less random and more orderly recruited firing pattern during the pedaling task at higher effort levels.
PURPOSE: This study investigated muscle coordination while pedaling at 150 and 300 W with a cadence of 90 rpm. Changes in the variability of the electromyographic (EMG) signals were quantified in 14 subjects. METHODS: Principal component analysis was used to find correlated EMG patterns among seven leg muscles that reflect neuromuscular strategies while pedaling. Sample entropy was used to assess the regularity of the short-term fluctuations of the EMG. Signal structure relates to the autocorrelation and to the information in the phase of the signal. This study used the information encrypted in the phase to quantify neuromuscular control and compared the results to phase-randomized surrogate data. RESULTS: Although the pattern remained similar, the correlation between individual muscles showed effort-dependent differences. Increased workload altered the overall neuromuscular strategy indicated by changes in the contribution of individual muscles to the movement. Additionally, the executed strategy was characterized by increased structure. Regularity of the short-term fluctuations in the EMG increased significantly with effort level. Both experimental conditions showed more structure in the phase of the EMG compared to the surrogate data. CONCLUSIONS: This increased structure in the EMG signal may represent a less random and more orderly recruited firing pattern during the pedaling task at higher effort levels.
Authors: Hendrik Enders; Filomeno Cortese; Christian Maurer; Jennifer Baltich; Andrea B Protzner; Benno M Nigg Journal: J Neurophysiol Date: 2015-11-04 Impact factor: 2.714
Authors: Cristian F Pasluosta; Simon Steib; Sarah Klamroth; Heiko Gaßner; Julia Goßler; Julius Hannink; Vinzenz von Tscharner; Klaus Pfeifer; Juergen Winkler; Jochen Klucken; Bjoern M Eskofier Journal: Front Aging Neurosci Date: 2017-09-27 Impact factor: 5.750
Authors: Julio Cézar Lima da Silva; Maria M Ekblom; Olga Tarassova; Eva Andersson; Gustaf Rönquist; Helene Grundström; Anton Arndt Journal: PLoS One Date: 2018-08-02 Impact factor: 3.240