School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
Brain Res. 2021 Feb 1;1752:147221. doi: 10.1016/j.brainres.2020.147221. Epub 2020 Dec 23.
Electroencephalogram (EEG) and electromyogram (EMG) signals during motion control reflect the interaction between the cortex and muscle. Therefore, dynamic information regarding the cortical-muscle system is of significance for the evaluation of muscle fatigue. We treated the cortex and muscle as a whole system and then applied graph theory and symbolic transfer entropy to establish an effective cortical-muscle network in the beta band (12-30 Hz) and the gamma band (30-45 Hz). Ten healthy volunteers were recruited to participate in the isometric contraction at the level of 30% maximal voluntary contraction. Pre- and post-fatigue EEG and EMG data were recorded. According to the Borg scale, only data with an index greater than 14<19 were selected as fatigue data. The results show that after muscle fatigue: (1) the decrease in the force-generating capacity leads to an increase in STE of the cortical-muscle system; (2) increases of dynamic forces in fatigue leads to a shift from the beta band to gamma band in the activity of the cortical-muscle network; (3) the areas of the frontal and parietal lobes involved in muscle activation within the ipsilateral hemibrain have a compensatory role. Classification based on support vector machine algorithm showed that the accuracy is improved compared to the brain network. These results illustrate the regulation mechanism of the cortical-muscle system during the development of muscle fatigue, and reveal the great potential of the cortical-muscle network in analyzing motor tasks.
脑电图（EEG）和肌电图（EMG）信号在运动控制期间反映了皮层和肌肉之间的相互作用。因此，皮质-肌肉系统的动态信息对于评估肌肉疲劳具有重要意义。我们将皮层和肌肉视为一个整体系统，然后应用图论和符号传递熵在β波段（12-30 Hz）和γ波段（30-45 Hz）建立有效的皮质-肌肉网络。招募了 10 名健康志愿者参与 30%最大自主收缩水平的等长收缩。记录了疲劳前和疲劳后的 EEG 和 EMG 数据。根据 Borg 量表，仅选择指数大于 14<19 的数据作为疲劳数据。结果表明，肌肉疲劳后：（1）产生力的能力下降导致皮质-肌肉系统的 STE 增加；（2）疲劳时动态力的增加导致皮质-肌肉网络活动从β波段转移到γ波段；（3）同侧半脑内参与肌肉激活的额区和顶区具有代偿作用。基于支持向量机算法的分类表明，与脑网络相比，精度有所提高。这些结果说明了皮质-肌肉系统在肌肉疲劳发展过程中的调节机制，并揭示了皮质-肌肉网络在分析运动任务中的巨大潜力。
