Graupe D, Salahi J, Zhang D S
J Biomed Eng. 1985 Jan;7(1):18-29. doi: 10.1016/0141-5425(85)90004-4.
This paper is concerned with a stochastic time-series analysis of the temporal signatures of myoelectric (ME) signals including the determination of model order and sampling rate. The paper considers the use of time-series parameters for the activation of artificial limbs for high-level amputees, of stimulation electrodes or of powered braces for paralysed persons, in several degrees of freedom, from a single or two surface-electrode pairs at locations where considerable ME cross-talk exists. The multifunctional capability from a single site is based on the differences between the time-series (TS) parameters for different muscle activation patterns at the same ME site, these differences being thus used for limb function discrimination via easily trainable muscle activation patterns at the vicinity of the electrode site. Specifically, the analysis is in terms of identifying the AR parameters of a time-domain autoregressive (AR) signature model both for the complete ME spectrum and for parts thereof, and in terms of the autocorrelation of the signal and of the models residual. Determination of sampling rate and of model orders is discussed in detail. It is shown that, using online real-time analysis, differences in the AR time-series parameters can be observed for different trainable patterns of muscle activation, at the same electrode location, even at the same ME power levels, as long as considerable cross-talk exists at the electrode site. These parameter differences can be accentuated if one considers the AR parameters for lower-frequency spectral windows. A case is made in this paper for employing TS analysis to squeeze out information in a distinct but low-level ripple of the low frequency spectrum of the signal. This information tends to be ignored in frequency domain, but is all that the AR parameters care for in TS analysis, since they are not concerned, with a flat-average low-frequency spectrum, i.e., its white-noise-like part, which is the residual term of the AR Model and not an AR parameter. Discrimination between different functions from a single electrode-site, at even the same power level, is thus shown to require considerable cross-talk at the given site, and to require the consideration of only the low-frequency part of the spectrum.
本文关注肌电（ME）信号时间特征的随机时间序列分析，包括模型阶数和采样率的确定。本文考虑将时间序列参数用于为高位截肢者激活假肢、为瘫痪者激活刺激电极或动力支架，在存在显著ME串扰的位置，从单个或两个表面电极对来实现多个自由度。来自单个位点的多功能能力基于同一ME位点不同肌肉激活模式的时间序列（TS）参数之间的差异，这些差异因此通过电极位点附近易于训练的肌肉激活模式用于肢体功能辨别。具体而言，分析涉及识别完整ME频谱及其部分的时域自回归（AR）特征模型的AR参数，以及信号和模型残差的自相关。详细讨论了采样率和模型阶数的确定。结果表明，使用在线实时分析，即使在相同的ME功率水平下，只要电极位点存在显著串扰，在同一电极位置，对于不同的可训练肌肉激活模式，AR时间序列参数的差异也是可以观察到的。如果考虑低频频谱窗口的AR参数，这些参数差异会更加明显。本文提出了一个案例，即采用TS分析来提取信号低频频谱中独特但低水平波动中的信息。该信息在频域中往往被忽略，但在TS分析中却是AR参数所关注的全部内容，因为它们不关心平坦平均的低频频谱，即其类似白噪声的部分，这是AR模型的残差项而非AR参数。因此，即使在相同功率水平下，从单个电极位点区分不同功能也表明需要在给定位点存在显著串扰，并且仅需考虑频谱的低频部分。
