surface electromyography to study muscle coordination · surface electromyography to study muscle...

Surface Electromyography to Study MuscleCoordination

François Hug and Kylie Tucker

AbstractElectromyography (EMG) records the electrical activity that is generated asaction potentials propagate along the length of muscle fibers. As such surfaceEMG is the research tool that is used in a vast majority of the works that assessmuscle coordination in health and disease. Although surface EMG recordings canprovide valuable information regarding the neural activation of a muscle by thenervous system, there are multiple factors that need to be considered to ensurethat the interpretation of the data is accurate. In this chapter, we have highlightedcrosstalk, signal cancellation, normalization, computation signal, detection ofthe onset/offset times, and the misinterpretation of EMG to infer torque as six ofthe most significant factors that need to be considered when recording and theninterpreting EMG data. These factors need to be considered before data iscollected, to determine if EMG is the right tool and/or which processing methodsmay best provide insight into the research question.

KeywordsEMG • Motor control • Force • Torque • Force sharing • Crosstalk • Signalcancellation • Normalization • Movement • Pattern • Profile • Activation • Motorunit • Electrodes • Contraction

F. Hug (*)Laboratory “Movement, Interaction, Performance” (EA4334), University of Nantes, Nantes, France

NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School ofHealth and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD, Australiae-mail: [email protected]

K. TuckerNHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School ofHealth and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD, Australia

School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australiae-mail: [email protected]

# Springer International Publishing AG 2016B. Müller, S.I. Wolf (eds.), Handbook of Human Motion,DOI 10.1007/978-3-319-30808-1_184-1

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mailto:[email protected]

mailto:[email protected]

ContentsIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2State-Of-The-Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Factors that Will Influence the Recorded Surface EMG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Crosstalk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Signal Cancellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

EMG Processing to Assess Muscle Coordination Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Normalization of the EMG Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7From the Raw EMG Signal to the EMG Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Sequence of Activation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Some Basic Misinterpretations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13EMG Amplitude Is Not Muscle Force/Torque . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13EMG Onset Is Not Torque Onset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Relationship Between Muscle Activation and Muscle Torque-Generating Capacity . . . . . . . 14Neuromuscular Fatigue Cannot Be Accurately Determined by Changes in EMGAmplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Introduction

Movement is critical for our successful interaction with the world. Movementfacilitates basic and vital functions, as well as complex daily activities. However,understanding how movement is controlled remains one of the main challenges formany scientific fields. Part of the underlying complexity of human movementoriginates from redundancy within our musculoskeletal system. Coordinationbetween multiple effectors at different levels (e.g., between limbs, between joints,between individual muscles, and perhaps even between regions of a muscle) isrequired to achieve the vast majority of motor tasks, even those considered thesimplest of tasks. As such, many different muscle coordination1 strategies aretheoretically possible to achieve the same goal (Bernstein 1967); although recentworks provide evidence that the range of strategies used is much smaller thanpossible given the redundant nature of our motor control system (Valero-Cuevaset al. 2015). This is because the coordination strategies used depend largely on neuraland mechanical constraints (Valero-Cuevas et al. 2015). A comprehensive under-standing of muscle coordination has direct applications in robotics, ergonomics, andhuman movement sciences, as well as informing the development of innovative andindividualized prevention and rehabilitation strategies for people with physicaldisability.

In the simplest form, for a muscle to produce torque, the muscle fibers within asingle motor unit (that comprises of a motoneuron, its motor axon, and all of the

1Here we consider muscle coordination as the distribution of muscle activation or force amongindividual muscles to produce a given motor task.

2 F. Hug and K. Tucker

muscle fibers it innervates) must receive a neural command (an action potential)from their motoneuron. In healthy individuals, there is a one-to-one relationshipbetween the generation of an action potential in the motoneuron and the generationof an action potential in the innervated muscle fibers. The generation of this motorunit action potential begins the physiological process of actin and myosin cross-bridge attachment within the muscle fiber sarcomeres and is therefore directly relatedto the production of force. As electromyography (EMG) records the electricalactivity that is generated as action potentials propagate along the length of musclefibers, EMG is the research tool that is used in a vast majority of the works that assessmuscle coordination in health and disease. Although surface EMG recordings canprovide valuable information regarding the neural activation of muscle, there aremany factors that can influence the recorded signal. Further, there are importantlimitations to consider when interpreting EMG recordings within a laboratory orclinical environment.

State-Of-The-Art

In studies focusing on muscle coordination, noninvasive recordings through surfaceelectrodes are generally preferred to the invasive fine-wire recordings. This isprimarily because the volume of muscle from which signals can be recorded isrelatively small when using fine-wire electrodes and thus may not be representativeof the whole muscle. Further, the insertion of fine-wires is invasive and thus requiresappropriate training of the operators, and fine-wires may cause discomfort duringmovement (although this is not common). Surface EMG recordings remain the mostcommon technique used to provide insight into muscle coordination, and as aconsequence, the present chapter will summarize works using primarily thismodality.

The recorded EMG signal represents the summation of all of the motor unit actionpotentials that are present at a given time, within the region of muscle from which theelectrical activity can be detected given the recording electrode configuration, i.e.,the electrode recording zone (Fig. 1). The motor unit action potentials that are beingrecorded may be generated from muscle fibers at various distances from the elec-trode, and the shape of each motor unit action potential is distorted by the low-passfiltering effect of the volume conductor, i.e., the surrounding tissue.

The signal-to-noise ratio is an important factor to consider when recordingsurface EMG signals. It describes the ratio of the energy of the real EMG signalrelative to the energy of the unwanted signals (i.e., the noise) within the recording.This noise may originate from various sources such as (i) electromagnetic radiationsfrom the external environment, (ii) the electronic components of the recordingequipment, or (iii) movements of the recording electrodes or cables, i.e., motionartifacts. Noise in the signal may also originate from within the body, with theheartbeat often observed in surface EMG recordings of trunk, back, and some limbmuscles. Any noise within the recording can result in an alteration of the amplitudeand the relative contribution of the frequency components of the recorded signal.

Surface Electromyography to Study Muscle Coordination 3

Much of the noise from an EMG signal can be reduced by applying appropriatefilters [e.g., high-pass filter at 20 Hz; (De Luca et al. 2010)].

EMG amplifiers have greatly evolved over the last 30 years such that an excellentsignal-to-noise ratio is now more regularly observed. In addition, recent mobile/wireless systems have reduced the potential for movement artifacts, in part byremoving the wires/cables and by using pre-amplifiers embedded within the elec-trodes. New technologies, such as screen printing of recording electrodes on a softsupport (Fig. 2), are being used to improve the recording quality of a surface EMGsignal by improving skin to electrode contact and thereby reducing impedance(Bareket et al. 2016). Such a system might allow for long-term recordings offeringnew applications to EMG such as activity monitoring throughout a 24-h (or longer)period.

Factors that Will Influence the Recorded Surface EMG Signal

Surface EMG signals are influenced by many physiological (e.g., fiber membraneproperties, motor unit properties) and nonphysiological factors (e.g., detectionsystem, conductivity of the tissue) [for review, see Farina et al. (2004)]. Althoughthe influence of some of these factors on the recorded signal can be overcome byfollowing recommendations for recording EMG (e.g., SENIAM project: http://www.seniam.org/), others cannot be overcome when using traditional surface EMGrecordings. When designing a study and interpreting the results, we believe thatcrosstalk and signal cancellation represent the two most significant factors that needto be considered.

Fig. 1 Generation of the surface EMG signal. The single motor units receive a neural commandfrom their motoneuron, leading to the generation of muscle fiber action potentials. The recordedEMG signal represents the summation of all of the motor unit action potentials that are present at agiven time, within the region of muscle from which the electrical activity can be detected given therecording electrode configuration. MU motor unit


Fig. 2 Example of temporary tattoo for long-term EMG recordings. (a) Fabrication scheme.(b) Electrodes placed on a hand. (c-top) EMG recording of the first dorsal interosseous (electrode1 versus 3). Arrows indicate (1) rest position followed by (2) force application (isometric contrac-tions) on the first dorsal interosseous for 2.5 s and 1 s (repeated three times). (3) Flexion of the indexfinger toward the thumb, (4) from the thumb, and (5) pointing up. (c-bottom) electrode 1 versus6. (d) Same as (c), 3 h after placement (From Bareket et al. (2016))


Crosstalk

Crosstalk is defined as the contamination of the EMG signal by the myoelectricactivity generated in a nearby muscle. For example, crosstalk has been demonstratedbetween leg muscles (tibialis anterior and peroneus longus) during gait (Campaniniet al. 2007). This study showed that the shape of the EMG signal recorded over thetibialis anterior muscle varied depending on the location of the electrodes. Moreprecisely, the electrodes placed closer toward the boundary of the peroneus longusrecorded a second burst of activity that was not present in the other locations. Thisburst likely originated from the peroneus longus. Such an effect of crosstalk on theEMG signals could explain, at least in part, the high variability of activation profilesof the lower leg muscles often reported between participants during pedaling (Huget al. 2008, 2010) or during gait (Guidetti et al. 1996); although true differences inthe motor patterns of individuals are also highly possible.

In more extreme cases, crosstalk can make a muscle appear to be generatingmyoelectric activity when it is not. For example, during quiet breathing, Chiti et al.(2008) observed significant myoelectrical activity recorded by surface EMG elec-trodes placed over the sternomastoid muscle when fine-wire recordings insertedwithin the same muscle recorded no activity. More recently, an EMG amplitude ofapproximately 20% of the maximal amplitude observed during a maximalplantarflexion was recorded from the plantarflexor muscles when they acted asantagonists during a maximal dorsiflexion. This significant EMG amplitude wasrecorded while no other evidence of an active contraction of the plantarflexormuscles was detected through ultrasound imaging (Raiteri et al. 2015) orelastography (Raiteri et al. 2016).

Crosstalk remains one of the most important sources of error in interpretingsurface EMG because (i) it is difficult to quantify the amount of crosstalk bynoninvasive methods, and (ii) while crosstalk can be reduced, it cannot be totallyremoved (Farina et al. 2004). Although cross-correlation has been used to quantifycrosstalk (Winter et al. 1994), results from simulation provided evidence that thismethod is not accurate (Lowery et al. 2003). Attempts to quantify crosstalk usingselective electrical muscle stimulation have also been made (De Luca and Merletti1988). However, due to the different strategies of motor unit recruitment betweenvoluntary and electrically elicited contractions, results obtained through applicationof electrical stimulation may not be easily extended to voluntary contractions(Lowery et al. 2003). As an accurate method to quantify crosstalk does not exist,efforts should be made to minimize its influence on the recorded EMG signal. To thisend, a double-differential electrode configuration (van Vugt and van Dijk 2001)associated with a low skin-electrode impedance (Mesin et al. 2009) is recommended.Crosstalk may be further reduced by a proper localization of the surface electrodeson the muscle (Hermens et al. 2000) and reducing the inter-electrode distance. Careshould be taken to place the electrodes in the center of the belly of the muscle, awayfrom the borders, although this may be challenging for the smaller muscles. Most


importantly, if it remains likely that crosstalk will influence the data being recorded,this should be acknowledged within the manuscript discussion and reflected in theconclusions drawn.

Signal Cancellation

Signal cancellation refers to the cancellation of positive and negative phases ofmotor unit action potentials (Farina et al. 2004). As such, estimation of EMGamplitude from a raw EMG signal is less than that obtained by summing theamplitudes of the individual motor unit action potentials. Signal cancellation canlead to an underestimation of the neural drive by up to 62 % of maximal drive, duringa maximal voluntary contraction (Keenan et al. 2005). This phenomenon thereforeconfounds the interpretation of neural drive from absolute EMG amplitude. Impor-tantly, signal cancellation is also increased with fatigue (Keenan et al. 2005). This isbecause the conduction velocity of the motor unit action potentials along musclefiber’s decreases as the muscle fatigues, and therefore the duration of the actionpotentials increases. This increased duration leads to greater overlap between poten-tials recorded. That means that the increase in drive with fatigue (and therefore thegreater number of motor unit action potentials) is likely to be underestimated ifconsidering only the amplitude of the EMG signal. Signal cancellation can thereforepartly explain why EMG amplitude at the end of the limit time to exhaustion remainssignificantly lower than that measured during an isometric maximal voluntarycontraction (Fuglevand et al. 1993).

EMG Processing to Assess Muscle Coordination Strategies

Given that muscle coordination may be defined as the distribution of muscleactivation or force among individual muscles to produce a given motor task,studying muscle coordination with EMG should address one or both of the followingquestions: (i) Which muscles are active and what is the magnitude of this activation?(ii) When are these muscles active during the task being performed?

Normalization of the EMG Amplitude

Amplitude of the EMG signal is one of the most straightforward and commonly usedmeasures of muscle activation. Normalization of EMG amplitude is a generallyconsidered a requirement for studies that aim to compare the level of activationbetween muscles or between participants (Winter and Brookes 1991; Soderberg andKnutson 2000). To this end, EMG amplitude is often expressed relative to thatmeasured during a brief isometric maximal voluntary contraction (IMVC) performedat a given muscle length (Dubo et al. 1976; Arsenault et al. 1986). However, there aremultiple limitations to this method that must be considered. First, the region of


muscle under a surface electrode will change when the length of a muscle changes,e.g., during dynamic contractions or during isometric contractions at different jointangles. To illustrate this point, amplitude of an M-wave (muscle compound actionpotential) recorded using surface electrodes is affected by joint position and thusmuscle length (Frigon et al. 2007). Second, the ability to voluntarily activate somemuscles is influenced by joint angle/muscle length. For example, maximal voluntaryactivation of the quadriceps muscle is higher when the knee is positioned at 90�

compared to a more flexed or more extended position (Becker and Awiszus 2001).However this length-dependent change in voluntary activation is not observed forother muscle groups such as the biceps brachii (Leedham and Dowling 1995).Therefore, the maximal EMG amplitude recorded during an IMVC performed at aspecific muscle length does not systematically represent the maximal EMG ampli-tude that would be obtained at other muscle lengths. This is in line with EMGamplitude values higher than that obtained during the IMVC that are often observedduring a dynamic task. For example, Hautier et al. (2000) reported an activity levelof 126.2% IMVC for Vastus lateralis during a brief maximal pedaling exercise, andJobe et al. (1984) reported an EMG activity up to 226% IMVC during a baseballpitch. In addition to the region of muscle being recorded changing with changes inmuscle length, and the ability to fully activate a muscle changing with joint anglelimiting the usefulness of this normalization method for some studies, it is importantto consider the time required to obtain reliable IMVCs. Not all participants have thesame ability to maximally activate their muscles during standardized tasks (Dorelet al. 2012), and some participants require repeated training sessions to be able toproduce a consistent IMVC. This is particularly problematic in clinical populationsas pain may be associated with decreased voluntary drive during maximal tasks(Salomoni et al. 2016). It is therefore recommended to verify that participantsproduce true maximal efforts by using the twitch interpolation technique (Huget al. 2015a). Further, muscle coordination is often studied by recording a largenumber of muscles simultaneously, and as such IMVCs must be performed on eachmuscle/muscle group with adequate rest time given between each maximalcontraction.

To reduce the time needed for the normalization procedures and to consider thespecificity of the dynamic task, some authors suggested normalizing EMG amplitudeto that measured during a maximal dynamic exercise similar to the task beingstudied. For example, Rouffet and Hautier (2007) recommended the normalizationof the EMG amplitude measured during a submaximal cycling task to that measuredduring an all-out sprint cycling task. This approach makes the assumption that anall-out sprint requires maximal muscle activation, which is true for some but not allmuscles (Dorel et al. 2012). Indeed, while the quadriceps muscle group is maximallyactivated during an all-out cycling sprint, hamstrings and hip extensors are not(Dorel et al. 2012). Furthermore, not all of the participants have the same ability tomaximally activate all the muscles during such a specific exercise (Dorel et al. 2012).This latter point is important because it could lead to misinterpretations of theinterindividual variability of the normalized EMG values.


Submaximal isometric or dynamic tasks are also used to normalize the EMGsignal (Dankaerts et al. 2004; Chapman et al. 2010). The underlying assumption isthat the relationship between muscle activation and contraction intensity is linear,such that 20% of maximal EMG amplitude should correspond to 20% of maximalforce. Despite being true for some muscles during specific tasks [e.g., flexordigitorum interosseous during isometric abduction of the index finger; (Lawrenceand De Luca 1983)], there is considerable evidence that the relationship betweenmuscle activation and joint torque is not linear for other muscles such as the bicepsbrachii and the deltoı̈d (Lawrence and De Luca 1983). This is because contributionof individual muscles to the joint torque may change with contraction intensityduring a task in which multiple synergist muscles are involved (Bouillard et al.2012). As a consequence, normalization to submaximal tasks may lead to misinter-pretation of the role of some muscles in a given task, as illustrated in Fig. 3. Thesubmaximal normalization method is further compromised when used in studies thatinclude clinical populations as the motor coordination strategies used to completesubmaximal tasks are often altered in people with pain and/or musculoskeletaldisease (Hodges and Tucker 2011; Hodges et al. 2013).

In summary, when focusing on submaximal isometric contractions, accurateinformation regarding the level of muscle activation can be obtained by normalizingthe EMG amplitude to that measured during an IMVC performed at the same musclelength. However, it is more challenging when studying a dynamic task for whichthere is no agreement on the best normalization procedure to be used (Burden andBartlett 1999). Importantly, information about the level of muscle activation is notalways needed. To study the shape of the EMG envelope, to determine muscleactivation timing, or to extract muscle synergies, EMG signals can be normalizedto the peak [named peak dynamic method; (Ryan and Gregor 1992)] or to the mean[named mean dynamic method; (Winter and Yack 1987)] value measured over acycle. Despite being appropriate for these purposes, these normalization proceduresprovide no information about the degree of muscle activation relative to the maximalmuscle activation.

From the Raw EMG Signal to the EMG Profile

During isometric tasks, muscle coordination can be assessed by determining thedistribution of activation between muscles. To this end, the normalized EMGamplitude is averaged over a given time period to provide a representative EMGamplitude value for the task being performed. However, when focusing on adynamic task, the mean EMG amplitude over a cycle or a specific part of themovement may conceal important information about coordination strategies. Asillustrated in Fig. 4, two EMG signals obtained in two different conditions mayexhibit the same mean EMG amplitude over the cycle, while the EMG profilesdemonstrate some alterations in coordination strategies. For a deeper understandingof muscle coordination strategies during dynamics tasks, the computation of thelinear envelope and the EMG profile is therefore recommended.


To represent the linear envelope, the raw EMG signal is generally rectified andlow-pass filtered (Kleissen 1990; Shiavi et al. 1998; Hug and Dorel 2009). Onemajor methodological consideration when computing linear envelope of the EMGsignal is the choice of the smoothing level (Hug et al. 2012), which is mainlydetermined by the low-pass cutoff frequency, the movement velocity, and to a lesserextent, by the number of cycles that are averaged (Fig. 5). In biomechanical studiesthat use EMG as an estimate of muscle force, a relatively low cutoff frequency(below 4–10 Hz depending on the movement velocity) is often used. However, thechoice of the low-pass filter is less trivial in neurophysiological studies where EMGis used as an indicator of the input to the motoneurons. The level of smoothingshould be chosen such that there is a neurophysiological basis for interpreting theEMG linear envelope. Awide range of low-pass filters has been used in the literature,e.g., from 3 Hz in Winter and Yack (1987) during walking to 40 Hz in Guidetti et al.(1996) during running. Results from Shiavi et al. (1998) suggested that a cutofffrequency of about 9 Hz is appropriate for gait analysis at fast walking speed (about1.8 m.s�1). The logic behind this recommendation is that the frequency of musclecontrol cannot lie outside the frequency of movement. It means that to compare thelinear envelope of a movement performed at different velocities, the low-pass filter

Fig. 3 Drawbacks associated with the normalization of EMG amplitude to submaximalvalues. Here we have the theoretical relationship between EMG amplitude and muscle force fortwo muscles (red and blue). If EMG measured during a contraction performed at 60% of maximaljoint torque is normalized to that measured at 20% of maximal joint torque, the muscle in redwouldexhibit a normalized EMG amplitude of 300%, and the muscle in blue would exhibit a normalizedEMG amplitude of 600%. However, the muscle in blue would be actually activated at a much lowerintensity. This is because of a different EMG amplitude-force relationship of these muscles. Notethat this same limitation exists between individuals for the same muscle (i.e., red and blue linescould represent the same muscle but from different individuals) as it does between muscles withinan individual


should be adapted to each velocity condition. For instance, considering that 9 Hz isappropriated for a cycle movement performed at 1.8 Hz, 5 Hz should be used for aslower cycle movement performed at 1 Hz.

The EMG profile is calculated by ensemble averaging the linear envelope for anumber of cycles or trials (Shiavi et al. 1981; Hug and Dorel 2009; Hug et al. 2010).First, mechanical events (e.g., foot switches, force signal) are used to determine thestart and the end of each cycle. Second, the cycles interpolated such that they have anequal number of points. Finally, all the interpolated cycle are averaged together. Notethat this averaging process may also improve the signal-to-noise ratio by reducingthe amplitude of occasional or noisy events that is not occurring in synchrony withthe motor task being performed (Bruce et al. 1977).

Sequence of Activation

Clinical interpretation of EMG signals is often based on the determination of the onsetand offset times. For example, delayed EMG onset of the medial head of the quadricepsrelative to the lateral head of the quadriceps (of�20ms) has been reported in individualswith patellofemoral pain during various tasks such as stepping (Crossley et al. 2002) andpostural tasks (Cowan et al. 2002). This delayed activity of the medial head is thought tocontribute to patellar maltracking and development of patellofemoral pain.

There are various methods that can be used detect EMG onset and offset time. Themost common method used involves determining at what time the EMG amplitudeincreases past a pre-determined EMG amplitude threshold. This threshold is often fixed

Fig. 4 EMG profiles obtained during a pedaling task. The two EMG profiles were obtainedfrom the same muscle in two different experimental conditions and are depicted as a function ofcrank angle. Although the EMG amplitude averaged over the cycle does not change betweenconditions (dashed lines), the EMG profiles exhibit some differences demonstrating an alterationof muscle coordination strategies. This example demonstrates that mean EMG amplitude value mayconceal important information about coordination strategies


at 15–25% of the peak EMG or 1, 2, or 3 standard deviations above the baseline EMGamplitude measured during a resting condition (Staude 2001). This threshold method isalso often associated with a minimum duration criterion during which the EMGamplitude must remain above the threshold. Despite being easy to implement inprocessing routines, this method provides results that are highly dependent on thesignal-to-noise ratio and the choice of the amplitude/duration thresholds. As suchcomparisons between studies may be difficult. To overcome these drawbacks, morecomplicated statistical methods have been developed to differentiate EMG from back-ground noise (Staude and Wolf 1999). Further, because EMG frequency contentchanges occur at the onset of muscular activation, other methods based on local analysisof the EMG frequency content using wavelet transform have been proposed (Merloet al. 2003). Li and Aruin (2005) proposed a method that takes advantage of the Teager-Kaiser energy operator (TKEO) to decrease the background noise of the signal.Although this method shows a higher accuracy than classical methods when the signal-to-noise ratio of the raw EMG signal is low, it does induce a time delay of the onsetdetection of about 20 ms (Li and Aruin 2005).

Although numerous automatic methods have been proposed to detect EMG onset/offset times, it should be kept in mind that they provide different outcomes (Fig. 6). Assuch, care should be taken when comparing results obtained with different methods.Further, as proposed by Hodges and Bui (1996), visual inspection of the detected onset/offset times remains important following any automatic detection process, to reduce thepotential for an unusual occurrence of artefact/noise in the EMG signal to influence thereported data.

Fig. 5 Individual exampleof the influence of thelow-pass cutoff frequency onthe EMG envelope. Threelow-pass filters (4, 10, and15 Hz) were applied on arectified EMG signal recordedduring pedaling. This examplehighlights the effect of thelow-pass filter on thesmoothing level of the EMGenvelopes (From Hug et al.2012 – reproduced withpermission from Elsevier)


Some Basic Misinterpretations

EMG Amplitude Is Not Muscle Force/Torque

A strong linear relationship between EMG amplitude and joint torque has beendemonstrated for some (but not all) muscles (Lawrence and De Luca 1983). Becauseof this, EMG amplitude normalized to a maximum voluntary isometric contraction isoften considered as an index of torque. As such, if two synergist muscles A and B areactivated at 30% and 70% of their maximum, respectively, it is often assumed thatmuscle B contributes more than muscle A to the global joint torque. However,beyond limitations inherent to the surface EMG technique to accurately quantifyneural drive, muscle torque cannot be inferred from only neural drive. This isbecause torque also depends on several biomechanical factors such as the physio-logical cross-sectional area (PCSA), the force-length and force-velocity relation-ships, the specific tension, and the moment arm. For example, for the same activationlevel, a muscle with a bigger PCSAwill produce more force than a smaller muscle.In addition, because of the well-known force-length and force-velocity relationship,higher muscle force can be produced for the same activation level if the muscleoperates at a more optimal length and/or velocity. Although moment arms and PCSAcan be estimated through imaging techniques, in vivo estimation of both the specifictension (maximal force per unit area) and force-length and force-velocity relation-ships is not a trivial task. This is mainly because there is no experimental techniqueto estimate individual muscle force (Erdemir et al. 2007).

Fig. 6 Example of EMG onset detection obtained using different automatic methods. The rawEMG signal recorded during an elbow flexion was rectified and subsequently smoothed with a50-Hz, fourth-order zero-lag low-pass Butterworth filter. Different automatic methods were used todetect the onset time. The results greatly differ depending on the detection method used. SDstandard deviation


EMG Onset Is Not Torque Onset

As mentioned above, there is some evidence that the relative onset of muscleactivation is altered in people with painful musculoskeletal conditions. For example,there is a positive correlation between delayed onset of vastus medialis EMGcompared to vastus lateralis EMG during gait, and the magnitude of patella tiltobserved with MRI during weight bearing, in a subgroup of people withpatellofemoral pain who were classified as having abnormal patella tilt (Pal et al.2011). However, the classical interpretation that altered EMG onset is associatedwith altered onset of torque production requires some considerations (Fig. 7). First,the onset of the recorded EMG signal is dependent on both the onset of muscleactivation and the time it takes for the motor unit action potentials to reach therecording volume (Hug et al. 2011). EMG onset is therefore dependent on theposition of the recording electrodes relative to the muscles motor point. Assumingan average conduction velocity of the action potentials of 5 m/s, for every 1 cm therecording electrodes are from the motor point, a 2 ms delay will be erroneouslyintroduced into the recorded data. Second, there is also a time lag (referred to aselectromechanical delay) between onset of neural drive to a muscle and torqueproduction. This electromechanical delay depends on both electrochemical (e.g.,excitation-contraction coupling) and mechanical (e.g., force transmission along theconnective tissues) processes that can greatly vary between muscles and individuals(Nordez et al. 2009). Taking the aforementioned example of patellofemoral pain, therelatively small delayed onset of vastus medialis EMG compared to vastus lateralis(about 20 ms), and the highly variable nature of this outcome measure as reportedbetween studies could be explained by different electrode positioning in respect tothe motor points and/or different electromechanical delay between the muscles (Huget al. 2015b).

Relationship Between Muscle Activation and Muscle Torque-Generating Capacity

The two previous paragraphs emphasize the need to consider the torque-generatingcapacity of individual muscles for biomechanical interpretation of the EMG signal.Surprisingly, little is known about the coupling between muscle activation andmuscle torque-generating capacity. There is preliminary evidence that the mechan-ical advantage of a muscle (due to its moment arm) and the neural drive it receivesduring a voluntary task are coupled (De Troyer et al. 2005; Hudson et al. 2009). Forexample, respiratory-related muscles such as the intercostal muscles are recruitedduring the breathing cycle according to their relative mechanical advantages(De Troyer et al. 2005). Similarly, during a task where the mechanical advantageof the flexor digitorum interosseous is increased by modifying thumb position andthus the moment arm, there is an adaptive increase in the neural drive to that muscle(Hudson et al. 2009). However, it remains unclear how the nervous system accountsfor the force-generating capacity of individual agonist muscles surrounding the same


joint. There are three possible alternatives for how neural drive of synergists may beregulated (Fig. 8):

1. Neural drive is adjusted to balance force between synergist muscles of differingforce-generating capacities; thus the muscle with the lower force-generatingcapacity would receive greater neural drive.

2. Neural drive is targeted to reduce the overall energy cost; thus the muscle with thehigher force-generating capacity would receive greater drive.

3. There is no relationship between neural drive and force-generating capacity.

Fig. 7 Schematic representation of the influence of electromechanical delay on the onset offorce production. (a) The time lag between onset of myoelectrical activity and force production(electromechanical delay) reflects both electrochemical processes (i.e., synaptic transmission,excitation-contraction coupling) and mechanical processes (i.e., force transmission along themuscle and tendon). (b) Although the electromechanical delay has been shown to be similarbetween the VM and VL in pain-free participants (about 25 ms), longer delay has been reportedin the VM (about 38 ms) than in the VL (about 18 ms) in people with patellofemoral pain (Chenet al. 2012). With consideration of this difference, this example illustrates that an absence ofdifference of EMG onset times might still lead to difference in onset of force production. VL vastuslateralis, VM vastus medialis (From Hug et al. 2015b – reproduced with permission)


Hug et al. (2015a) determined the relationship between the ratio of activationduring an isometric task and the ratio of muscle force-generating capacity betweentwo synergist muscles [vastus lateralis (VL) and vastus medialis (VM)]. Neitherforce capacity nor activation was balanced between VL and VM. There was a strongcorrelation between the ratio of VL/VM EMG amplitude and the ratio of VL/VMPCSA. These results provided evidence that muscle activation is biased by muscleforce-generating capacity; the greater the force-generating capacity of VL comparedto VM, the stronger bias of the activation to the VL. Despite providing evidence of apositive relationship between neural drive and muscle force-generating capacity,these results cannot explain the origin of this relationship. It is possible that anindividual’s muscle morphology and architecture underlies the relationship, suchthat the nervous system develops/adapts optimally to bias drive to the muscle withlarger physiological cross-sectional area. Alternatively, muscle coordination strate-gies may underpin muscle morphology and architecture, such that the muscle withgreater drive leads to biased development of PCSA.

Neuromuscular Fatigue Cannot Be Accurately Determined byChanges in EMG Amplitude

If we consider a submaximal force-matched task that involves only one muscle (e.g.,little finger abduction or index finger abduction), an increase in the EMG amplitudemay be considered as a sign of neuromuscular fatigue. In this case, the increased

Fig. 8 Relationship between the neural drive and force-generating capacity (determined herefrom muscle physiological cross-sectional area, PCSA). There are three possible alternatives forhow neural drive of synergists may be regulated: (1) neural drive is adjusted to balance forcebetween synergist muscles of differing force-generating capacities; thus the muscle with the lowerforce-generating capacity would receive greater neural drive (panel 1); (2) neural drive is targeted toreduce the overall energy cost; thus the muscle with the higher force-generating capacity wouldreceive greater drive (panel 2); or (3) there is no relationship between neural drive and force-generating capacity (panel 3)


EMG amplitude might be explained by (i) an additional recruitment of motor units(MU) to compensate for the decrease in the force of contraction that occurs in fatiguedmuscle fibers (Edwards and Lippold 1956), (ii) an increased firing frequency and/orsynchronization of motor unit recruitment (Gandevia 2001), and/or (iii) the slowing ofmuscle fiber action potential conduction velocity (Linstrom et al. 1970). However, thisinterpretation of increased EMG amplitude as a sign of neuromuscular fatigue doesnot hold true during tasks that involve synergist and antagonist muscles (Hug 2011).This is because it is difficult to dissociate the effects of neuromuscular fatigue and thepossible changes of muscle coordination strategies. For example, an increased EMGamplitude of a lower leg muscle during a constant-load pedaling exercise could beexplained by (i) a change of muscle coordination strategy that consists of an increasein muscle force produced by this muscle to compensate for fatigue in other muscles(in this case the recorded muscle does not fatigue); (ii) a change of motor unitsrecruitment such as an additional recruitment, an increased firing frequency, andsynchronization and slowing of muscle fiber action potential conduction velocity(in this case the recorded muscle exhibits fatigue); or (iii) both. In order to betterisolate the direct effects of neuromuscular fatigue from the changes of musclecoordination, it is possible to measure neural (M-wave, voluntary activation, maximalactivation level) and contractile (muscular twitch) properties of a muscle group atvarious instants of a fatiguing exercise. Unfortunately, this information can beobtained simultaneously only from a limited number of muscles.

Concluding Remarks

EMG is a powerful research tool that has provided many remarkable insights into thecontrol of movement. To facilitate the appropriate reporting and interpretation ofdata, and drive new discoveries in human movement studies, it is essential thatresearchers and clinicians understand the limitations to this technique. There aremany excellent technical reviews on EMG recording and processing that have comebefore this (Merlo et al. 2003; Farina et al. 2004, 2014). Rather than replicate thisinformation, we have highlighted crosstalk, signal cancellation, normalization,computation signal, detection of the onset/offset times, and the misinterpretation ofEMG to infer torque as six of the most significant factors that need to be consideredwhen recording and then interpreting EMG data. These factors need to be consideredbefore data is collected, to determine if EMG is the right tool and/or which pro-cessing methods may best provide insight into the research question. Further, thesefactors may be important to consider when determining the validity of the conclu-sions made from previously published research.

Cross-References

▶EMG Activity in Gait: The Influence of Motor Disorders▶Optimal Control Strategies for Human Movement


http://link.springer.com/EMG Activity in Gait: The Influence of Motor Disorders

http://link.springer.com/Optimal Control Strategies for Human Movement

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