CBS-TOYOTA Collaboration Center in the Nagoya Science Park Research and Development Center, Intelligent Behaviour Control Unit (RIKEN), Nagoya, Aichi, Japan.
Brain Machine Interface Systems Lab from Miguel Hernández University (UMH), Parque Cientifico UMH, Edificio Innova, Elche, Alicante, Spain.
Physiol Rep. 2022 May;10(10):e15296. doi: 10.14814/phy2.15296.
Superficial Electromyography (sEMG) spectrum contains aggregated information from several underlying physiological processes. Due to technological limitations, the isolation of these processes is challenging, and therefore, the interpretation of changes in muscle activity frequency is still controversial. Recent studies showed that the spectrum of sEMG signals recorded from isotonic and short-term isometric contractions can be decomposed into independent components whose spectral features recall those of motor unit action potentials. In this paper sEMG spectral decomposition is tested during muscle fatigue induced by long-term isometric contraction where sEMG spectral changes have been widely studied. The main goals of this work are to validate spectral component extraction during long-term isometric muscle activation and the quantification of energy exchange between the low- and high-frequency bands of sEMG signals during muscle fatigue.
表面肌电图（sEMG）频谱包含来自几个潜在生理过程的综合信息。由于技术限制，这些过程的隔离具有挑战性，因此，肌肉活动频率变化的解释仍然存在争议。最近的研究表明，从等速和短期等长收缩中记录的 sEMG 信号的频谱可以分解为独立的分量，其频谱特征类似于运动单位动作电位。在本文中，在广泛研究 sEMG 频谱变化的长期等长收缩引起的肌肉疲劳期间，测试了 sEMG 频谱分解。这项工作的主要目标是验证长期等长肌肉激活期间的频谱分量提取，以及量化肌肉疲劳期间 sEMG 信号的低频和高频带之间的能量交换。
