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Updated: Jun 9, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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列德的多项式基于拟合的变换提供了对疲劳对表面电肌图 (sEMG) 信号复杂性的影响的新见解

Meryem Jabloun1, Olivier Buttelli1, Philippe Ravier1

  • 1Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France.

Entropy (Basel, Switzerland)
|October 25, 2024
PubMed
概括

局部莱根德尔多项式基于匹配的变量 (LPPE) 量化了时间序列的复杂性. 这项研究表明,LPPE可以在表面肌电图 (sEMG) 信号中评估肌肉疲劳,更高的维度可以改善疲劳歧视.

关键词:
列德的多项式建模.疲劳 疲劳 疲劳 疲劳 疲劳 疲劳多尺度变量的.表面电力学图 (surface electromyography) 是一种表面电力学图.时间序列时间序列

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