ASPIRE Centre for Disability Sciences, Institute of Orthopedics and Musculoskeletal Science, University College London, Brockley Hill, Stanmore, London HA7 4LP, UK.
J Electromyogr Kinesiol. 2011 Feb;21(1):128-35. doi: 10.1016/j.jelekin.2010.09.006. Epub 2010 Nov 9.
The purposes of this study were: (1) to apply wavelet and principal component analysis to quantify the spectral properties of the surface EMG and MMG signals from biceps brachii during isometric ramp and step muscle contractions when the motor units are recruited in an orderly manner, and (2) to compare the recruitment patterns of motor unit during isometric ramp and step muscle contractions. Twenty healthy participants (age = 34 ± 10.7 years) performed step and ramped isometric contractions. Surface EMG and MMG were recorded from biceps brachii. The EMGs and MMGs were decomposed into their intensities in time-frequency space using a wavelet technique. The EMG and MMG spectra were then compared using principal component analysis (PCA) and ANCOVA. Wavelet combined PCA offers a quantitative measure of the contribution of high and low frequency content within the EMG and MMG. The ANCOVA indicated that there was no significant difference in EMG total intensity, EMG(MPF), first and second principal component loading scores (PCI and PCII) between ramp and step contractions, whereas the MMG(MPF) and MMG PCI loading scores were significantly higher during ramp contractions than during step contractions. These findings suggested that EMG and MMG may offer complimentary information regarding the interactions between motor unit recruitment and firing rate that control muscle force production. In addition, our results support the hypothesis that different motor unit recruitment strategy was used by the muscle when contracting under different conditions.
(1) 应用小波和主成分分析来量化等长斜坡和阶跃肌肉收缩期间肱二头肌表面肌电 (EMG) 和肌电声信号 (MMG) 的光谱特性,此时运动单位按顺序募集;(2) 比较等长斜坡和阶跃肌肉收缩期间运动单位的募集模式。20 名健康参与者(年龄 = 34 ± 10.7 岁)进行了阶跃和斜坡等长收缩。从肱二头肌记录表面 EMG 和 MMG。使用小波技术将 EMG 和 MMG 分解为它们在时频空间中的强度。然后使用主成分分析 (PCA) 和协方差分析 (ANCOVA) 对 EMG 和 MMG 光谱进行比较。小波结合 PCA 提供了一种定量测量 EMG 和 MMG 中高频和低频内容贡献的方法。ANCOVA 表明,在斜坡和阶跃收缩之间,EMG 总强度、EMG(MPF)、第一和第二主成分载荷得分 (PCI 和 PCII) 没有显著差异,而 MMG(MPF) 和 MMG PCI 载荷得分在斜坡收缩期间显著高于阶跃收缩期间。这些发现表明,EMG 和 MMG 可能提供关于运动单位募集和放电率之间相互作用的补充信息,从而控制肌肉力量产生。此外,我们的结果支持以下假设:当肌肉在不同条件下收缩时,使用了不同的运动单位募集策略。