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J Appl Physiol 96: 1486–1495, 2004;
10.1152/japplphysiol.01070.2003.
Invited Review
HIGHLIGHTED TOPIC
Neural Control of Movement
The extraction of neural strategies from the surface EMG
Dario Farina,1 Roberto Merletti,1 and Roger M. Enoka2
1
Laboratorio di Ingegneria del Sistema Neuromuscolare, Dipartimento di
Elettronica, Politecnico di Torino, Torino 10129, Italy; and 2Department
of Integrative Physiology, University of Colorado, Boulder, Colorado 80309-0354
motor unit; spectral analysis; recruitment strategies; synchronization; amplitude
cancellation; electromyogram
(EMG) comprises the sum of the
electrical contributions made by the active motor units (MUs)
as detected by electrodes placed on the skin overlying the
muscle. The information extracted from the surface EMG is
often considered a global measure of MU activity, because of
the inability of the traditional (2 electrode) recording configuration to detect activity at the level of single MUs. The global
characteristics of the surface EMG, such as its amplitude and
power spectrum, depend on the membrane properties of the
muscle fibers as well as on the timing of the MU action
potentials. Thus the surface EMG reflects both peripheral and
central properties of the neuromuscular system.
Two approaches are available to study the relations between
the surface EMG and the properties of the neuromuscular
system: one forward and the other inverse. The forward approach, such as can be accomplished with modeling, allows us
to predict the effect of various physiological processes on
features of the surface EMG. The inverse approach uses the
EMG to identify the underlying physiology. The inverse approach, however, requires simplifications to reduce the number
of parameters and multiple solutions that influence the association. The relation between average conduction velocity of the
muscle fibers and spectral frequencies of the surface EMG
during isometric contractions sustained at a constant force (3,
8, 56) is an example of a forward association; the physiology
THE SURFACE ELECTROMYOGRAM
Address for reprint requests and other correspondence: D. Farina, Dipartimento di Elettronica, Politecnico di Torino, Corso Duca degli Abruzzi 24,
Torino 10129, Italy (E-mail: dario.farina@polito.it).
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determines the EMG characteristics. The inverse problem involves estimating changes in the average conduction velocity
from the characteristic spectral frequencies and can be solved
by approximating this relation with a linear equation. A limitation of inverse models is that approximations are valid for
specific conditions and cannot be applied to more general
situations, which can result in misleading conclusions. The
situation is confounded when some of the factors that influence
features of the signal have counterintuitive or unexpected
effects.
The application of mathematical models (10, 27, 31) has
proven useful in characterizing the sensitivity of the surface
EMG to the parameters of the systems involved in the generation and detection of the signal. Structure-based models, for
example, can now describe the generation of the surface EMG
in complex volume conductors (31) as a consequence of
various control strategies (35, 46, 84). Although the use of
these tools has clarified the limitations associated with the
measurement and interpretation of the surface EMG, these
limitations are often not appreciated by the experimentalist.
The aim of this brief review was to characterize the strengths
and weaknesses of some of the methods used to infer central
control strategies from bipolar recordings of the surface EMG.
The review focuses on the global surface EMG and does not
relate, except for a brief note, to more advanced methods for
extracting information on single MUs from noninvasive recordings, which have been addressed in recent reviews (19, 54,
55, 85, 86). The review is not exhaustive, but it examines
topical issues related to the interpretation of the surface EMG
8750-7587/04 $5.00 Copyright © 2004 the American Physiological Society
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Farina, Dario, Roberto Merletti, and Roger M. Enoka. The extraction of
neural strategies from the surface EMG. J Appl Physiol 96: 1486–1495, 2004;
10.1152/japplphysiol.01070.2003.—This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG)
recordings. Among the many uses of the surface EMG in studying the neural
control of movement, the review critically evaluates only some of the applications.
The focus is on the relations between global features of the surface EMG and the
underlying physiological processes. Because direct measurements of motor unit
activation are not available and many factors can influence the signal, these
relations are frequently misinterpreted. These errors are compounded by the
counterintuitive effects that some system parameters can have on the EMG signal.
The phenomenon of crosstalk is used as an example of these problems. The review
describes the limitations of techniques used to infer the level of muscle activation,
the type of motor unit recruited, the upper limit of motor unit recruitment, the
average discharge rate, and the degree of synchronization between motor units.
Although the global surface EMG is a useful measure of muscle activation and
assessment, there are limits to the information that can be extracted from this signal.
Invited Review
INTERPRETATION OF THE SURFACE EMG
by focusing on the limitations of some methods as they are
used to extract information from the surface EMG.
FACTORS THAT INFLUENCE THE SURFACE EMG
The features of the EMG signal depend on many “nonphysiological” factors (Table 1). The influence of these factors has been
measured, simulated, and discussed (22, 29, 30, 41, 49, 71). Some
of these effects are not intuitive and vary with experimental
conditions. Nonetheless, useful information can be extracted from
the surface EMG, especially when the experimental protocol
permits some of these factors to be minimized. The influence of
some of these factors can be reduced significantly by appropriate
placement of the electrodes. Because different electrode locations
over the same muscle can provide signals with significantly
different features, some locations are preferred over others (41,
71). For this reason, placement of the electrodes should always be
reported in EMG studies.
Of the nonphysiological factors listed in Table 1, crosstalk
provides an example of how intuitive considerations can be
incorrect when dealing with phenomena that are influenced by
Table 1. Factors that influence the surface EMG
Factors That Influence the Surface EMG
Nonphysiological
Anatomic
Detection system
Geometrical
Physical
Physiological
Fiber membrane properties
Motor unit properties
Shape of the volume conductor
Thickness of the subcutaneous tissue layers
Tissue inhomogeneities
Distribution of the motor unit territories in
the muscle
Size of the motor unit territories
Distribution and number of fibers in the
motor unit territories
Length of the fibers
Spread of the endplates and tendon
junctions within the motor units
Spread of the innervation zones and tendon
regions among motor units
Presence of more than one pinnation angle
Skin-electrode contact (impedance, noise)
Spatial filter for signal detection
Interelectrode distance
Electrode size and shape
Inclination of the detection system relative
to muscle fiber orientation
Location of the electrodes over the muscle
Muscle fiber shortening
Shift of the muscle relative to the detection
system
Conductivities of the tissues
Amount of crosstalk from nearby muscles
Average muscle fiber conduction velocity
Distribution of motor unit conduction
velocities
Distribution of conduction velocities of the
fibers within the motor units
Shape of the intracellular action potentials
Number of recruited motor units
Distribution of motor unit discharge rates
Statistics and coefficient of variation for
discharge rate
Motor unit synchronization
EMG, electromyogram.
J Appl Physiol • VOL
the properties of the volume conductor. Crosstalk refers to a
signal recorded over one muscle that is actually generated by a
nearby muscle and conducted through the intervening volume
to the recording electrodes (15). Many investigators assume
that a crosstalk signal has “a lower frequency spectrum because
it originates further away and will be subject to additional
low-pass filtering due to spatial filtering” (14). According to
this rationale, high-pass filtering should reduce crosstalk; however, this is not a general finding. Recordings of muscle fiber
action potentials are influenced by two events: propagation
along the fiber and extinction at the end of the fiber. With an
increase in the distance between the recording electrodes and
the active muscle fibers, the nonpropagating components due
to extinction of the action potentials (40) dominate those due to
propagation (29). Because the high-frequency components of
the nonpropagating signals are greater than those for the
propagating signals, crosstalk signals can have a broader bandwidth than signals detected directly over an active muscle (17,
29). Because of this effect, high-pass filtering can have no
effect on crosstalk (Fig. 1).
Alternatively, the presence of crosstalk has been estimated
by calculating the cross-correlation coefficient between the
signals detected from two muscles (1, 59, 61, 83). Journals that
specialize in EMG measurements, for example, often recommend the use of cross-correlation analysis, as suggested by
Winter et al. (83), as the standard for detecting crosstalk.
However, the cross-correlation analysis is not a valid measure
of the presence or absence of crosstalk (29, 51), as indicated in
Fig. 1C.
A recent experimental study (29) and simulation analyses
(17, 28, 51) suggest several conclusions about crosstalk: 1)
signals detected far from the source are mainly due to the
extinction of the action potentials at the ends of the fibers; 2)
because of differences in the sources of the propagating and
nonpropagating signals, the cross-correlation coefficient is
generally not indicative of the amount of crosstalk; 3) the
frequency content of an EMG signal does not identify the
presence of crosstalk; 4) temporal high-pass filtering of surface
EMG can fail to reduce crosstalk signals; and 5) the use of
spatial filtering as a method to decrease crosstalk remains to be
validated because the theoretical analyses of spatial filters have
been limited to propagating signals (18, 69, 70).
INFERRING NEURAL CONTROL STRATEGIES FROM THE
SURFACE EMG
Various methods have been used to infer details about the
signals discharged from the spinal cord to activate muscle.
These techniques, however, have various limitations that are
often not appreciated.
Amplitude of the Surface EMG
The amplitude of the surface EMG can be estimated by a
scheme of demodulation, smoothing, and relinearization. In
this process, demodulation rectifies the EMG and then raises
the result to a power (e.g., 1 for the average rectified value or
2 for the root mean square value), smoothing filters the signal,
and relinearization inverts the power law applied during the
demodulation stage and returns the signal to units of EMG
amplitude.
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Crosstalk
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Invited Review
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INTERPRETATION OF THE SURFACE EMG
The amplitude of the surface EMG is related to the net MU
activity; that is, the recruitment and the discharge rates of the
active MUs. Because of this relation, some investigators use
EMG amplitude as an index of the level of activation provided
by the spinal cord. As listed in Table 1, however, EMG
amplitude is influenced by such factors as electrode location,
thickness of the subcutaneous tissues, distribution of MU
conduction velocities, and the detection system used to obtain
the recording. Although some of these effects can be reduced
by appropriate placement of the electrodes (30, 41), there
remains a mismatch between the output of the spinal cord and
the EMG amplitude.
Amplitude cancellation. The surface EMG underestimates
the activation signal sent from the spinal cord to muscle as a
result of the cancellation of positive and negative phases of
MU action potentials (13) (Fig. 2). The amount of signal
cancellation can be quantified by comparing the signal amplitude obtained by summing MU action potentials before and
after the rectification of each potential (13, 44). Cancellation is
present when the unrectified action potentials are summed but
not when the rectified potentials are summed. These results
indicate that, although the amplitude of the surface EMG
J Appl Physiol • VOL
increases monotonically with the neural drive to a muscle, a
general relation cannot be determined.
Amplitude cancellation depends on many of the factors
listed in Table 1 (44) and indicates that comparable levels of
average rectified EMG, either in different subjects or on
different occasions, do not necessarily indicate similar levels of
output from the spinal cord (Fig. 2). Furthermore, changes in
the average rectified or root mean square EMG values after an
intervention may not rigorously reflect altered levels of neural
drive to the muscle. Similarly, the so-called measure of neuromuscular efficiency (16, 58), which corresponds to the ratio
between an exerted force and the amplitude of a surface EMG,
and other such indexes suffer from the dependence of amplitude cancellation on factors that can have nothing to do with
the amplitude of the activation signal (44). The extensive use
of such indexes in basic and clinical studies underscores the
lack of appreciation for the significance of signal cancellation.
Estimation of muscle force. Both the force exerted by a
muscle and the amplitude of the surface EMG depend on the
number of recruited MUs and the discharge rate of each active
MU. Accordingly, it is reasonable to expect that muscle force
can be estimated from the surface EMG. Because of amplitude
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Fig. 1. The influence of the distance between the recording electrodes and active muscle fibers on the surface electromyogram. A:
section of a volume conductor comprised of bone, muscle, fat, and skin layers (31) with pairs of electrodes placed on the skin at
2 locations. The muscle comprised 150 motor units (MUs) with the innervation number ranging from 50 to 800. B: the surface
electromyogram was recorded with a pair of electrodes 10 mm apart and placed between the center of the innervation zone and the
tendon junction. The muscle fibers were 110 mm in length, and there was a scatter of 5 mm for both the endplates and the tendon
endings. C: simulated surface EMG signals as detected over (location 1) and far (location 2) from the active muscle. Signals
detected concurrently at the two recording sites are reported as interference data (without any filtering) and after differentiation with
respect to time, as suggested in Winter et al. (83) (sampling frequency 1,024 Hz). The amplitude of the signals is indicated in
arbitrary units (au), with a scale of 1 au for the signals detected at location 1 and 0.05 au for those detected at location 2. The
correlation coefficient (CC) between the recordings for the 2 locations was low, both before and after time differentiation.
Moreover, high-pass filtering (differentiation) increased the amount of crosstalk, as indicated by the ratio of the average rectified
value (ARV) of the signal detected over the muscle (ARVmus) relative to that far away (ARVcro). D: an action potential for a single
MU as detected at the 2 locations. The shape of the potential recorded at location 1 is dominated by the propagating component,
whereas the shape recorded at location 2 is largely due to the nonpropagating (extinction) component.
Invited Review
INTERPRETATION OF THE SURFACE EMG
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Spectral Analysis of the Surface EMG
cancellation, however, the amplitude change of the surface
EMG actually underestimates the associated change in MU
activity underlying the modulation of muscle force.
Many factors influence the relation between EMG amplitude
and force (Table 1). When muscles and subjects are compared,
these factors include the thickness of subcutaneous tissue, the
recruitment strategy, the peak discharge rates of the different
MUs (35), and so on. Moreover, the same control strategy may
generate signals with different amplitude trends depending on
the locations of the active MUs within the muscle (24). Because of the many factors that can influence this relation, there
is no reason to expect that a specific EMG amplitude-force
relation should have general validity. This relation should be
identified on a subject-by-subject and muscle-by-muscle basis.
J Appl Physiol • VOL
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Fig. 2. Simulation of amplitude cancellation. The simulations involved 2 of
the MUs from the study described in Fig. 1 (nu ⫽ normalized units). An
interference signal was simulated with each MU independently. Contraction
intensity (Excitation level) was varied by repeating the number of times (from
50 to 1,000 in steps of 50) that the MU action potential occurred within a 5-s
interval. Occurrence times were distributed randomly. A: the 2 MU potentials
and representative signals generated at different levels of excitation with MU
2. Even at excitation level 2 (100 MU action potentials), examples of phase
cancellation are clearly visible. B: the average rectified value of the signals,
normalized relative to the first excitation level, is plotted against the excitation
level. The signal amplitude does not increase linearly with excitation level
because of the cancellation of positive and negative phases of the waveforms.
Moreover, amplitude cancellation depended on the waveform selected and the
assigned conduction velocity (3 or 5 m/s). At the maximal level of excitation,
for example, amplitude could differ by ⬃30% in the different conditions,
although the amount of excitation was the same in all cases. The magnitude of
this effect is greater in an actual muscle (13).
Spectral analysis of surface EMG signals has been used to
study muscle fatigue (56) and to infer changes in MU recruitment (6, 7, 73). Characteristic spectral frequencies can be
computed by the classic periodogram (57) and autoregressivebased approaches (66) or by advanced methods such as Cohen’s class time-frequency distributions (12) and wavelet analysis (43). The latter techniques have been used with dynamic
(5, 12) and isometric contractions (42) and may be more
appropriate than the classic approaches when the signals are
nonstationary. However, the critical limitations in the spectral
analysis of the surface EMG are intrinsic to the properties of
the surface EMG signals and not the type of signal analysis.
Relation between spectral frequencies and conduction velocity. The relation between the average conduction velocity of
the muscle fibers (4) and power spectral frequencies is used to
study muscle fatigue, to identify the type of MUs recruited, and
to describe the pattern of MU activity. The results, however,
are only relevant when specific conditions are met. The relative
changes in the spectral frequencies and conduction velocity
during sustained isometric contractions at moderate to high
intensities, for example, can be approximated by a linear
relation (3, 56). Indeed, under ideal conditions, it can be
shown that conduction velocity scales the power spectrum
(50). Nonetheless, it is inappropriate to use this relation
when the number of active MUs changes significan …
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