Neuromuscular Biomechanics Laboratory, Graduate Program in Physical Therapy and Rehabilitation Science, University of Iowa, Iowa City, IA 52242-1190, USA.
J Biomech. 2011 Jan 4;44(1):202-5. doi: 10.1016/j.jbiomech.2010.09.008. Epub 2010 Sep 25.
The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, "true" EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2-40% maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low.
表面肌电图（EMG）信号常受到一定程度的基线噪声的干扰。科学家们通常会在进一步分析之前，从测量的 EMG 信号中减去基线噪声，这是基于基线噪声线性添加到观察到的 EMG 信号的假设。然而，基线和 EMG 信号的随机性可能使该假设无效。或者，基线噪声可能对“真实”的 EMG 信号产生最小或非线性的影响。当信噪比（SNR）可能最低时，这些信息在低收缩强度时特别相关。因此，这项模拟研究的目的是研究不同水平的基线噪声（约为最大 EMG 幅度的 2-40%）对平均 EMG 爆发幅度的影响，并评估最佳的信号噪声处理方法。模拟结果表明，基线噪声对最大收缩时的平均 EMG 活动影响最小，但随着噪声水平的增加和信号幅度的降低，非线性增加。因此，当估计低强度 EMG 爆发期间的平均活动时，简单的基线噪声减去会导致大量误差。相反，将 EMG 信号校正为基线和测量信号幅度的非线性函数，提供了 EMG 幅度的高度准确估计。这种新颖的非线性误差建模方法对 EMG 信号处理具有潜在影响，特别是在评估拮抗肌的共同激活或 SNR 可能较低的小幅度收缩时。
