Can muscle coordination be precisely studied by surface electromyography?  François Hug  Journal of Electromyography and Kinesiology  Volume 21, Issue 1, Pages 1-12 (February 2011) DOI: 10.1016/j.jelekin.2010.08.009 Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 1 Example of surface electromyographic signal processing to study muscle coordination. Panel (A) depicts a raw EMG signal recorded in the vastus lateralis during pedaling. The linear envelope is first computed by rectification and low-pass filtering (at 9Hz). All cycles are then extracted on basis of trigger delivered at a crank angle of 0° (Panel (B)) and time normalized. Finally, a representative EMG profile per muscle is obtained by averaging those linear envelopes for a number of consecutive cycles (C). Onset and offset values are determined from this averaged pattern using an EMG threshold (here fixed at 20% of the peak EMG recorded during the cycle; horizontal dashed line). Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 2 Individual example of the extraction of muscle synergies. (A) Initial EMG patterns from 10 lower limb muscles (for clarity, only one pedaling cycle is depicted). (B) EMG patterns were processed by a non-negative matrix factorization algorithm, which applied an iterative optimization procedure to best reconstruct the initial EMG patterns using a small set of muscle synergies. For each muscle synergy, the adjusted parameters include the muscle synergy vectors (i.e., the relative weighting of each muscle within each synergy) and the synergy activation coefficients (i.e., the relative contribution of the muscle synergy to the overall muscle activity pattern). The contribution of any muscle synergy to a muscle EMG pattern is the product of the muscle weighting for this synergy, times the synergy activation coefficient. (C) For each muscle, the EMG pattern is reconstructed by adding the contribution of each muscle synergy. GMax, Gluteus maximus; SM, Semimembranosus; BF, Biceps femoris; VM, Vastus medialis; RF, Rectus femoris; VL, Vastus lateralis; GM, Gastrocnemius medialis; GL, Gastrocnemius lateralis; SOL, Soleus; and, TA, Tibialis anterior. Reprinted from Hug et al. (2010) with permission from the American Physiological Society. Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 3 Intensity pattern resulting from the wavelet analysis of the EMGs for five lower limb muscles. For each muscle the intensities are shown for the frequency bands covered by the wavelets as indicated by their center frequencies on the right ordinate. The intensities were obtained from 40 men running at 4m/s. Reprinted from Von Tscharner and Nigg (2008) with permission from the American Physiological Society. Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 4 Illustration of crosstalk in respiratory muscles. EMG activity of two inspiratory muscles is recorded during inspiratory loading in an healthy subject; Scal i corresponds to the intramuscular EMG activity of the anterior scalene muscle (fine wire electrode); SCM i corresponds to the intramuscular EMG activity of the sternomastoid muscle (fine wire electrode); Scal s represents the surface EMG activity of the anterior scalene muscle (surface electrodes); SCM S represents the surface EMG activity of the sternomastoid muscle. The vertical dashed lines denote the beginning of the inspiratory phase. Sternomastoid intramuscular electrode was consistently silent. This example clearly shows that the surface electrodes aimed at recording the sternomastoid pick up a cross-talk signal from other muscles, among which the scalene is probably the most important. Reprinted from Chiti et al. (2008) with permission from Elsevier. Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 5 Individual example of the influence of the low-pass cut-off frequency on the number of extracted muscle synergies. The material used to elaborate this example has been published in Hug et al. (2010). Panel (A) depicts the EMG pattern of the Rectus femoris obtained during pedaling. Four different low-pass cut-off frequency have been used: 3Hz as used by Winter and Yack (1987); 9Hz as recommended by Shiavi et al. (1998); 20Hz as used by Li and Caldwell (1998) and 40Hz as used by Guidetti et al. (1996). These filters have been applied for all the ten recorded muscles. By selecting the least number of synergies that provided 90% of the VAF, we found that the number of muscle synergies varies between three (for a low-pass cut-off frequency fixed at 3 and 9Hz) and five (for a low-pass cut-off frequency fixed at 40Hz). Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 6 An artificial difference between two subjects induced by normalizing the time scale in percentage of the cycle. The rowing cycle corresponds to the period between two successive catches and is divided into recovery and drive (or propulsive) phases. Panel (A) depicts a schematic EMG pattern obtained in the vastus lateralis during rowing for two subjects. Although the duration of the two phases (i.e., the recovery and the drive phase) differs between the subjects, the EMG pattern is identical. However, by normalizing the rowing cycle with respect to the total cycle (i.e., by taking into account only the period between two successive catches), a difference in the timing of activity occurs. This difference can be explained by the fact that 50% of the rowing cycle does not correspond to the same phase in the two subjects. In subject A, it corresponds to the transition between the recovery phase and the drive phase, and, in subject B, it corresponds to the recovery phase. For a better understanding of the timing changes, kinematics data should be analysis in complement to the EMG. Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions

Fig. 7 Illustration of potential differences between onset/offset and the coefficient of cross-correlation determination. Example curves of Gastrocnemius medialis EMG linear envelopes obtained during pedaling in two different body positions (i.e., dropped posture, DP and upright posture, UP) are depicted. Dashed lines indicate the threshold for onset and offset at 20% of the peak EMG. Offset appears 20° earlier in UP condition whereas the onset is not modified. By taking into account the two complete EMG profiles the cross-correlation technique and kmax calculation lead to a total shift of 4° from DP to UP. Reprinted from Hug and Dorel (2009) with permission from Elsevier. Journal of Electromyography and Kinesiology 2011 21, 1-12DOI: (10.1016/j.jelekin.2010.08.009) Copyright © 2010 Elsevier Ltd Terms and Conditions