Effects of time windowing on the estimated EMG parameters

Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series

“…The PSD of a random signal can be estimated using classical methods (periodogram or Blackman-Tukey estimators) or modern parametric model methods (autoregressive (AR), moving average (MA), autoregressive moving average (ARMA)) [1,12]. However, PSD estimation unavoidably introduces a number of factors (method for PSD estimation, implementation algorithm, order of parametric model, shape and size of the analysis window) that directly affect the estimates of the spectral variables [9,10,15,35,38]. Furthermore, the derivation of meaningful, statistically-significant spectral parameters requires some assumptions regarding the characteristics of the signal [13].…”

Prolonged muscle vibration reduces maximal voluntary knee extension performance in both the ipsilateral and the contralateral limb in man

“…Electrodes were attached over the distal third of the distance between the patella and the greater trochanter for vastus lateralis recordings and electrodes were placed midway between the patella and the iliac crest for the rectus femoris recordings. Raw EMG signals were pre-amplified (B&L Engineering), and further amplified (1902, CED, Cambridge, UK) to improve signal to noise ratio, before being passed through an A/D converter (1401, CED, Cambridge, UK) at a sampling rate of 1,000 Hz, which was sufficient to capture the characteristics of the EMG signals from the muscles (Waly et al 1996).…”

Understanding the effect of window length and overlap for assessing sEMG in dynamic fatiguing contractions: A non-linear dimensionality reduction and clustering

“…However, this approach has limitations due to the low sensitivity for the motor unit's discharge rate (Rampichini et al, 2020), EMG amplitude cancelation (Cifrek et al, 2009;Rampichini et al, 2020), frequency leakage (Tan and Jiang, 2019), and time-frequency resolution problems. The STFT time-frequency resolution limitations can be overcome by using more modern methods such as wavelets (Cifrek et al, 2009;Costa et al, 2010;Waly et al, 1996). However, physiological information is available in the periodograms to be used for muscle fatigue (Costa et al, 2010).…”