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首页> 外文期刊>Journal of Neuroscience Methods >Using factor analysis to identify neuromuscular synergies during treadmill walking.
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Using factor analysis to identify neuromuscular synergies during treadmill walking.

机译：在跑步机行走过程中使用因子分析来识别神经肌肉协同作用。

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摘要

Neuroscientists are often interested in grouping variables to facilitate understanding of a particular phenomenon. Factor analysis is a powerful statistical technique that groups variables into conceptually meaningful clusters, but remains underutilized by neuroscience researchers presumably due to its complicated concepts and procedures. This paper illustrates an application of factor analysis to identify coordinated patterns of whole-body muscle activation during treadmill walking. Ten male subjects walked on a treadmill (6.4 km/h) for 20 s during which surface electromyographic (EMG) activity was obtained from the left side sternocleidomastoid, neck extensors, erector spinae, and right side biceps femoris, rectus femoris, tibialis anterior, and medial gastrocnemius. Factor analysis revealed 65% of the variance of seven muscles sampled aligned with two orthogonal factors, labeled 'transition control' and 'loading'. These two factors describe coordinated patterns of muscular activity across body segments that would not be evident by evaluating individual muscle patterns. The results show that factor analysis can be effectively used to explore relationships among muscle patterns across all body segments to increase understanding of the complex coordination necessary for smooth and efficient locomotion. We encourage neuroscientists to consider using factor analysis to identify coordinated patterns of neuromuscular activation that would be obscured using more traditional EMG analyses.
机译：神经科学家通常对将变量分组以促进对特定现象的理解感兴趣。因子分析是一种强大的统计技术，可以将变量分组为概念上有意义的簇，但由于其复杂的概念和过程，神经科学研究者仍未充分利用它。本文说明了因子分析在跑步机行走过程中识别全身肌肉激活协调模式的应用。十名男性受试者在跑步机上以6.4 km / h的速度行走20 s，在此期间，从左侧胸锁乳突肌，颈部伸肌，竖直脊柱和右侧股二头肌，股直肌，胫骨前胫骨获得了表面肌电图（EMG）活动，和腓肠肌内侧。因子分析显示，与两个正交因子（标为“过渡控制”和“负荷”）对齐的七块肌肉的变异率为65％。这两个因素描述了跨身体各个部分的肌肉活动的协调模式，这通过评估单个肌肉模式不会很明显。结果表明，因子分析可有效地用于探索所有身体部位的肌肉模式之间的关系，以增进对平稳有效运动所需的复杂协调的理解。我们鼓励神经科学家考虑使用因子分析来确定神经肌肉激活的协调模式，而这种模式会被更传统的EMG分析所掩盖。

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