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POINT-COUNTERPOINT
Human Performance Laboratory
University of Calgary
Alberta, Canada
e-mail: vincent{at}kin.ucalgary.ca
POINT: SPECTRAL PROPERTIES OF THE SURFACE EMG CAN CHARACTERIZE MOTOR UNIT RECRUITMENT STRATEGIES AND MUSCLE FIBER TYPE
Definitions
Fiber types. Human muscles have different fiber types such as fast- and slow-twitch (1, 10) or type I and II fibers. The different fibers have different force-generating capacities (5) and are task specific. Since shortening speed does not always reflect the ATPase activity, the discrimination between type I and II fibers is based on the rate of myofibrilar ATPase reaction. Human MHC isoforms are considered as types I, IIa, and IIx (12, 13). A refined subdivision of fibers was proposed correlating fiber types with EMG spectral properties (4).
Recruitment pattern. A pulse train in the motoneurons is generated by the central nervous system activating the muscle fibers through the neuromuscular end plates. The fibers belonging to one motoneuron form a motor unit (MU). Different MUs can have different number of fibers usually of the same type. The recruitment pattern indicates which MUs are selected and by what pulse train they are activated. Modern technologies allow detecting the rate coding pattern of a pulse train (9). Rate-coding patterns controlling the intensity of the EMG do not coincide in time with the selection of MUs (17). The recruitment strategy changes the recruitment pattern over time and controls force and timing of MUs required to perform the task at hand.
Muscle tasks. Not all muscle fibers will be involved in every task. Specific muscle fibers will be used for specific tasks like lock and hold, lock and release, pull and release (length change), and reflex tasks. The task-specific MU groups will most likely show different power spectra indicating when a different group has been recruited.
The EMG Spectrum
An understanding of how the EMG signal is generated and which properties are reflected in the power spectrum obtained by the Fourier transform is essential for this discussion (6, 20). Stegeman et al. (14) modeled the motor unit action potential (MUAP) as a convolution of individual muscle fiber action potentials with a window encompassing the end plate zone. We obtained a modeled EMG by convolving these MUAPs with a pulse train of 
-pulses. Our model computations and results from other publications show the following.
Because of 6, a power spectrum reflecting the superimposed spectra of selected MUs requires averaging or filtering. The wavelet analysis represents an optimized filter bank for smoothing the power spectrum considering the time resolution required for observing muscle activation (16). The results can be displayed as a pattern encoding the intensity by a grayscale for each time point (abscissa) and frequency band (ordinate). The patterns represent the filtered power spectra at each time point during a task.
Tasks and Power Spectra
Power spectra of EMG signals depend primarily on the task and are subsequently modulated by factors such as length change, lactate accumulation, and volume conduction. Results of a study with EMG measurements on 80 subjects running at 4 m/s (17–19) are used to illustrate the relationship between task and power spectra. The EMG activities of the five muscles (gastrocnemius medialis, tibialis anterior, hamstring, rectus femoris, and vastus medialis) are illustrated with intensity patterns (Fig. 1).
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A similar hold and lock task could be observed for the gastrocnemius muscle used during cocontraction before landing (hold and lock corresponding to low frequencies). However, during initial ground contact, the gastrocnemius muscle activated MUs of LFG and HFG, resulting in low and high frequencies.
The pattern of the quadriceps muscles showed a pull and release task, related to the changes in knee angle during ground contact, which corresponded to a primarily slow muscle action, resulting in relatively low frequencies.
Thus the multi-muscle intensity pattern reflected the recruitment strategy used during the different muscle tasks performed while running. The recruitment strategies were highly reproducible for men and women. Details of the interplay of LFG and HFG were reported earlier (19). The generating spectra represented was most likely a mix of the spectra of the selected group of fiber types. However, the generating spectra were similar to those expected for slow- and fast-twitch fibers, indicating that the LFG most likely contained slow-twitch fibers, whereas the HFG most likely contained fast-twitch fibers.
Actual measurements of spectra relating to fiber types were performed in fish, where the two types of fibers are anatomically separated and the individual power spectra were measured (22). In rats, a procedure similar to the one used by Solomonow et al. (11) was applied using a stimulating pulse train activating the motoneurons and a second signal blocking the pulse propagation (23). When the amplitude of the block was reduced to just below threshold, only the action potentials of the slowest motor units reached the muscle.
Thus it was possible to measure the spectra of the isolated slowest MU and then observe the higher frequency components generated by the faster MUs. The observation produced different frequency bands for these two types of MUs.
Conclusion
The major statements of this publication are that 1) muscle activities are task dependent, 2) task-dependent spectral changes can be observed if the tasks actually change, and 3) power-spectral changes are associated with changes in the selected muscle fiber types.
Examples for experiments where task-dependent changes occurred include long-distance running and paw shaking of cats (21, 22). Examples where MU composition changes also generate large-frequency shifts, e.g., in atrophic muscles caused by osteoarthritis (15). In these experiments, substantial changes in the power spectra were observed. However, one should not expect to see substantial changes in power spectra when studying single-task experiments such as an isometric ramped increase in force (4), selective recruitment of fast-twitch muscle fibers during lengthening contractions (2), and/or EMG activity during an explosive contraction (8). In such experiments, the power spectrum is determined by the single task studied and substantial changes in the power spectra should not be expected.
Furthermore, one should expect controversial results in the power spectra when studying activities where synergistic activation of MUs is produced (8) because synergistic activities will downshift the power spectrum. Thus single tasks and synergistic experiments are not suited to reveal or reject changes in spectral properties resulting from changes in recruitment pattern. Single-task and synergistic experiments do provide insight in the modulations affecting spectra during individual muscle tasks.
REFERENCES
This article has been cited by other articles:
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  G. Layec, A.él. Bringard, Y. Le Fur, C. Vilmen, J.-P. Micallef, S.ép. Perrey, P. J. Cozzone, and D. Bendahan Effects of a prior high-intensity knee-extension exercise on muscle recruitment and energy cost: a combined local and global investigation in humans Exp Physiol, June 1, 2009; 94(6): 704 - 719. [Abstract] [Full Text] [PDF]
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 Rebuttal from Farina J Appl Physiol, November 1, 2008; 105(5): 1675 - 1675. [Full Text] [PDF]
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R. M. Enoka, P. Bawa, J. M. Wakeling, D. A. Gabriel, G. Kamen, F. Meyer, D. F. Stegeman, T. van Wessel, M. J. Zwarts, A. Rainoldi, et al. Spectral properties of the surface EMG can characterize motor unit recruitment strategies. J Appl Physiol, November 1, 2008; 105(5): 1676 - 1677. [Full Text] [PDF]
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V. von Tscharner and B. M. Nigg Last Word on Point:Counterpoint: Spectral properties of the surface EMG can characterize/do not provide information about motor unit recruitment strategies and muscle fiber type J Appl Physiol, November 1, 2008; 105(5): 1682 - 1682. [Full Text] [PDF]
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