A segmentation approach to long duration surface EMG recordings

Time- and frequency-domain monitoring of the myoelectric signal during a long-duration, cyclic, force-varying, fatiguing hand-grip task

“…Since different motor units may be active at different force levels and/or rates of change of force, it is important to test for EMG parameter changes using the same conditions (i.e., force level, force rate of change) that have elicited the fatigue (Madeleine et al, 2002;Sjogaard et al, 2000;Sogaard et al, 2003). Taken together, these physiologic and methodological confounds are likely the reason for the absence of consistent trends in amplitude and spectral EMG parameters over the time period observed in the present study and for the conflicting results in the literature (El Falou et al, 2005;Farina, 2006;Farina et al, 2004;Farina et al, 2006;Madeleine et al, 2002;McLean et al, 2000;Sjogaard et al, 2000;Sogaard et al, 2003). Recent studies have suggested the need for more advanced time-frequency signal processing methods to extract parameters from dynamic contractions (Bonato et al, 2001;Broman et al, 1985;Karlsson et al, 2000;Roy et al, 1998).…”

“…Recently, MacIsaac et al (2001) refocused discussion on the conventional frequency-domain analysis techniques, using a parametric model of the EMG power spectrum to show that modulation of EMG amplitude (over the physiologic range of modulation frequencies) has no substantial influence on MNF estimates computed using the STFT. In addition to more complex methods, time trends of spectral parameters from long-duration, dynamic contractions have not shown the consistent results characteristic of short-duration, high-effort, static tasks (El Falou et al, 2005;McLean et al, 2000). Many confounds exist, including processing methodology, constrained vs. unconstrained subject postures, experiment duration and muscle(s) evaluated.…”

Signal processing of the surface electromyogram to gain insight into neuromuscular physiology

“…A general approach is to detect and reject outliers of observations (time samples) within a signal. For a singlechannel sEMG, Falou et al (2005) presented a method based on a regression technique, where the normalized signal spectrum is compared with an exponential model during a signal segment.…”

Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings

“…Other studies have used high frequency spectral components to estimate the white noise contribution in SEMG signals [12]. Low frequency noise in SEMG recordings is usually eliminated using high pass filtering.…”

“…Some previous studies have compared the EMG spectra to theoretical models such as the Shwedyck model or the exponential model [12]. Another method is to use autoregressive modeling (AR), which has been widely used for EMG spectral estimation [15], as the poles of the AR model of the EMG are strongly related to the SEMG spectral form.…”