surface emg models properties and applications - 2000

Journal of Electromyography and Kinesiology 10 (2000) 313–326www.elsevier.com/locate/jelekin

Surface EMG models: properties and applications

Dick F. Stegemana, c, d,*, Joleen H. Bloka, c, Hermie J. Hermensb, Karin Roeleveldd, e

a Department of Clinical Neurophysiology, Institute of Neurology, University Medical Centre, PO Box 9101, 6500 HB Nijmegen,The Netherlands

b Roessingh Research and Development, Enschede, The Netherlandsc Institute for Fundamental and Clinical Human Movement Sciences, The Netherlands

d Motor Research Group, Institute of Pathological Physiology, Friedrich-Schiller University, Germanye Department of Sport Sciences NTNU, Dragvoll, Trondheim, Norway

Abstract

After a general introduction on the kind of models and the use of models in the natural sciences, the main body of this paperreviews potential properties of structure based surface EMG (sEMG) models. The specific peculiarities of the categories (i) sourcedescription, (ii) motor unit structure, (iii) volume conduction, (iv) recording configurations and (v) recruitment and firing behaviourare discussed. For a specific goal, not all aspects conceivable have to be part of a model description. Therefore, finally an attemptis made to integrate the ‘question level’ and the ‘model property level’ in a matrix providing direction to the development andapplication of sEMG models with different characteristics and varying complexity. From this overview it appears that the leastcomplex are models describing how the morphological muscle features are reflected in multi-channel EMG measurements. Themost challenging questions in terms of model complexity are related to supporting the diagnosis of neuromuscular disorders.2000 Elsevier Science Ltd. All rights reserved.

Keywords:Surface EMG; Modelling; Volume conduction; Motor unit; Intracellular action potential

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314

2. Possible users and questions for sEMG model applications . . . . . . . . . . . . . . . . . . . . . 315

3. Possible elements of structure based sEMG models . . . . . . . . . . . . . . . . . . . . . . . . . . 3153.1. General concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3153.2. Source functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3153.2.1. Single muscle fibres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3153.2.2. Structure of the motor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3183.2.3. Muscle source function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3183.2.4. Miscellaneous remarks on source descriptions . . . . . . . . . . . . . . . . . . . . . . . . . 318

3.3. Volume conduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3183.3.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3183.3.2. Infinite volume conductor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3183.3.3. Inhomogeneities and frequency dependency of tissue . . . . . . . . . . . . . . . . . . . . . 3193.3.4. Recording configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

3.4. Motor unit recruitment and firing behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3203.4.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3203.4.2. Motor unit interpulse intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320

* Corresponding author.E-mail address:[email protected] (D.F. Stegeman).

1050-6411/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved.PII: S1050-6411 (00)00023-7

314 D.F. Stegeman et al. / Journal of Electromyography and Kinesiology 10 (2000) 313–326

3.4.3. Mean interpulse intervals across motor units . . . . . . . . . . . . . . . . . . . . . . . . . . 3213.4.4. Motor unit synchronisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

4. Related remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3224.1. Descriptions in the frequency domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3224.2. Combination and interrelations of model properties . . . . . . . . . . . . . . . . . . . . . . . . 3224.3. Inverse modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3224.4. Computing principles and efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322

5. Model design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

1. Introduction

The use of computer models is inevitable in almostall areas of the natural sciences. Often, model design isthe key task in problem solving. Models are used in twoways. First, they attempt to extract the essentials ofreality for the problem at hand and second, they permita control over variables not easily achieved in reality.The ultimate goal behind the use of models is also two-fold. They not only serve a better understanding of reallife processes by the reductional aspect, but they alsoallow the estimation of internal process characteristicsby their so-called ‘inverse use’, i.e. the reproduction ofexperimental results by adapting the model’s para-meters [84].

A model can approach reality at different levels [68].It may have adescriptivecharacter with limited validity.The often-assumed proportionality between EMG ampli-tude and muscle force can be considered an example ofa descriptive model. Second, a model can operate at aphenomenologicallevel, i.e. the model output mimicsreal world behaviour under a wide range of conditions,but the model is not or only by chance coupled to anyunderlying ‘real world’ elements evoking the observedoutcomes. Finally, a model can bestructure based,which means that it selectively takes elements of the realsystem’s structure into account in a reductional way inorder to represent the system’s important elements. Inthis discussion, we are dealing with the latter type ofmodels.

Research and clinical studies using surface EMG(sEMG) address a variety of problems. The way inwhich models play a role in sEMG is largely dependenton the specific question asked. We begin with a briefdiscussion regarding the various sEMG applications. Aschematic representation of the possible configuration ofa structure based sEMG model is provided in Fig. 1.The primary focus of this discussion is to the potentialproperties of such sEMG models in the broadest sense.

Fig. 1. Schematic representation of possible elements of a structurebased sEMG model as they will be discussed in this paper.

The ‘question-to-solve level’ and the ‘model-propertylevel’ is then integrated in a matrix giving direction tothe development and application of sEMG models to dif-ferent purposes and varying complexity.

315D.F. Stegeman et al. / Journal of Electromyography and Kinesiology 10 (2000) 313–326

2. Possible users and questions for sEMG modelapplications

Electromyography is used in areas of human move-ment studies and neuromuscular diagnostics. It is mostfrequently used in the field of clinical neurophysiologyor electrodiagnostic medicine [21]. Almost every patientwith complaints that possibly concern the neuromuscularsystem will have a clinical EMG investigation. Whendealing with voluntary muscle contractions, the clinicalneurophysiologist is mainly interested in the recruitmentand firing behaviour of single motor units (MUs). Thisinformation is usually obtained with needle EMG elec-trodes and not with sEMG. In recent years, a number ofstudies on the possibilities of using sEMG also for thestudy of the single MU behaviour appeared [17,59,72].Another, not yet widely used, sEMG application, relatedto single MUs, is the technique of MU number esti-mation (MUNE, for a review see [60]). For this appli-cation, the motor nerve is stimulated with increasingintensity in order to estimate the ‘electrical’ size and thenumber of individual MUs that can be recognised in themuscle. In the context of neuromuscular disorders, itshould finally be noted that pathological muscle fatigueand various ‘channelopathies’ (functional disturbancesof the sarcolemmal ionic channels), can also be studiedwith sEMG [18,93]. In particular, the estimation of mus-cle fibre conduction velocity (MFCV) continues toreceive attention [19,92].

The kinesiological disciplines form the major groupof sEMG users regarding voluntary muscle contractions.The rehabilitation sciences are the largest field of kinesi-ology where sEMG is used [47]. Closely related is thestudy of labour circumstances and ergonomics [50]. Inthese areas, sEMG is used to estimate the activity ofindividual muscles in terms of their contribution to com-plex, mostly deviant and/or harmful co-ordination pat-terns and associated reactions to fatigue [58]. Exerciseand sport physiologists also routinely use sEMG in theirscientific work [51]. In this area, the above co-ordinationaspect in terms of movement optimisation, as well asmuscle fatigue, is estimated by means of sEMG para-meters.

The above may lead to a list of non-trivial questions,whose answers may be obtained using a model of thesEMG signal. It will be shown that any of those ques-tions puts different requirements with respect to theinformation needed from sEMG models. The variety ofapplications necessarily has consequences for the designof models.

3. Possible elements of structure based sEMGmodels

3.1. General concepts

Surface EMG recorded during voluntary muscleaction can be considered as a signal where the contri-

butions of all active MUs are intermingled in a so-calledinterference pattern. The term ‘interference’ suggeststhat the contributions of the individual MUs can barelybe recognised in the signal. Nevertheless, the sEMG sig-nal characteristics are largely dependent on the proper-ties of the contributing MUs, their firing patterns andtheir interdependence.

A complete model for the sEMG regards the inter-ference pattern as a linear summation of the motor unitaction potential (MUAP) trains. MUAP trains can bedescribed as the mathematical convolution of the firingmoments with the MUAP wave shape. An sEMG modelthat describes the interference pattern should thereforeconsider both the firing behaviour and the MUAP waveshapes. The MUAP characteristics, i.e. shapes and distri-bution of amplitude and duration, are determined bymorpho-functional properties of the activated musclefibres and MUs, together with passive and active bio-electric phenomena. The firing patterns reflect the motorcontrol of the central nervous system.

The major model elements concerning both theMUAP wave shapes and the MU firing patterns (Fig. 1)will be discussed in the next sections.

3.2. Source functions

3.2.1. Single muscle fibresThe bio-electric source of all neuromuscular activity

is found in the outer fibre membrane, i.e. the sarcolemmain the case of the muscle fibre. The description of theion channel activity (e.g. Na+, K +, Cl2, Ca2+) in the sar-colemma and the T-tubuli [87,88] is beyond the scopeof most EMG models. Usually, EMG models start at thelevel of the intracellular and transmembrane as obtainedfrom experimental data or from active membrane mod-els. The following assumptions are rather common.

O The measured potential field is the linear summationof the potential fields of the contributing musclefibres. An important requirement underlying thisassumption is that muscle fibre sources do not interact(ephaptic transmission) at the level of the sarcolemmaor at the level of the terminal motor nerve branches.This assumption can be questioned for pathologicalconditions [22,44].

O From the sEMG point of view (extracellular recordingat larger distance), each muscle fibre can be con-sidered a line source, i.e. its diameter can be neg-lected. This approximation has been extensively vali-dated in one of the early model studies of EMG[1,73]. Although apparently conflicting with resultsshown by Boom and Wallinga [3], an almost inevi-table consequence of this assumption is that the fibretransmembrane ionic current can be described as the2nd spatial derivative of the intracellular action poten-tial (IAP) profile.


The above two assumptions imply that a description ofthe IAP wave shape suffices as a source definition. Thiswave shape can be described in several ways. A compre-hensive overview of choices made so far is given byDimitrova [15]. The following simple mathematical IAPdescription (1a) with 2nd derivative (1b) is often used:

IAP(z)5Az3e−lz2B (1a)

Im(z)5sia2

d2IAP(z)dz2 ~lz(626lz1l2z2)e−lz (1b)

in which Im(z) is the transmembrane ionic current perunit membrane area as a function of the spatial positionz along the fibre of a fibre with radiusa and intracellularconductivitysi. Note that these functions are essentiallyspatially defined; their temporal appearance is related tothe propagation of the IAP. By the choice of the para-metersA, Bandl, the IAP can be properly shaped [[73],Fig. 2(a,b)]. Another, further simplified, version of theIAP is a triangular wave shape [67], reducing the trans-membrane current to the so-called tripole or linear quad-rupole [two dipoles in line in opposite direction, Fig.2(c,d)].

This description has been widely adopted because ofits simplicity and ease of interpretation. The tripoledescription becomes valid for sufficiently large obser-vation distances, a condition that is amply met in sEMG.There is evidence that the repolarisation phase of theIAP has a much longer tail (after-potential) than can berepresented with Eq. (1a) or with the tripole concept[23,53]. This behaviour cannot be neglected for a num-

Fig. 2. Intracellular muscle fibre action potential (IAP) (a) and its second derivative (b) which is proportional to the transmembrane current waveshape according to Eqs. (1a) and (1b). The time and frequency scales are related by a propagation velocity of 4 m/s (after [73]). (c) Indicates atriangular IAP estimate. The transmembrane current then reduces to a linear quadrupole or tripole (d). Note the central pole (dashed bar) as thesum of two elements of both constituent dipoles.

ber of phenomena concerning sEMG, but is hardly usedin models as yet.

A transmembrane ionic source has a triphasic charac-ter. In combination with volume conduction (see Section3.3), this leads to overlapping positive and negativepotential components of almost but not completelysynchronous sources of different fibres. This is a wellknown phenomenon in electrophysiology. It is the causeof so-called ‘phase cancellation’. Models may play acrucial role in the quantitative prediction and under-standing of this phase cancellation phenomenon.

The final consideration with respect to the modeldescription of the muscle fibre as a source of bio-electricactivity, is related to the genesis and the extinction ofthe muscle fibre action potential. A proper descriptionof these aspects is essential in almost any question to ansEMG model (Table 1) [70,71]. The IAP is generated atthe end-plate, usually somewhere in the middle of themuscle fibre; it then propagates in two opposite direc-tions away from this end-plate, and finally extinguishesat the muscle fibre–tendon transition. This extinction ofthe action potential at the tendon is important becausethe transmembrane source then effectively turns into adipolar source. During action potential propagation, thesource is of the ‘less powerful’ quadrupolar type. Thedipole has a strong effect, especially at a large recordingdistance as is the case for sEMG [31]. To our knowl-edge, the primary recognition of the relevance of thisphenomenon originated from Gydikov (see [35]).


Table 1Model characteristics and questions to SEMG.×: necessary or minimal,s: possibly usefula

Model characteristics/questions to SEMG 1 2 3 4 5 6 7 8 9 10 11 12 13

Source functionsArbitrary IAP (membrane current=2nd derivative) × × s s s

Arbitrary membrane current s s

Tripole or Rosenfalck source description × × × × × × × × × × ×SF source fibre type dependency s ×Genesis at motor endplate × × × s × s s × × s ×Extinction at tendon × × × s s s × × × × s

MUAP from library s s s s

Number of fibres × × × × × × ×Fibres radially (perpendicular to fibre) distributed × s × s × s s s

Endplates distributed × × s s s × × ×Tendons distributed s × s s s s ×MFCV distributed × × s × × s s s s

Neuromuscular jitter s s

MU source fibre type dependency × s × s

Arbitrary number of motor units × × × × × × × s

MU radius distributed s s s

MU location distributed × × × × × ×Endplate distributed × × s s

Tendon distributed × s s

MFCV distributed × × × × s

Fibre type distributed × s × s s

Force prediction per fibre or MU s s × s s

Volume conductionInfinite volume conductor × × × × × × × ×Anisotropy s × × × s s s × s ×Finite volume conductor s × × s s × × ×Skin and fat layer s s × × s s × × ×Arbitrary shape of volume conductor s s s s

Spatially extended electrodes s s × s s s × s

Multiple electrodes × × × s s × s s × s s × ×Recruitment and firingRecruitment × × × × × s × ×Rate coding s × × s s × ×Within-MU firing rate variability s × s s × s

Between-MU firing rate variability × × s s s × ×Synchronisation s s s s s × ×Stimulation s s s s

a The columns indicate the following questions to pose to the SEMG:1. What can be deduced from the sEMG with respect to neuromuscular disorders?;2. How many MUs are active in a specific task?;3. What does the sEMG tell about the size of MUs in a patient?;4. Which muscle force is predicted from the sEMG?;5. What is the influence of muscle composition (in terms of fibre types)?;6. How can the MFCV distribution be calculated from an interference pattern?;7. What do sEMG amplitude and frequency changes tell about muscle fatigue?;8. Which central drive changes can be estimated from the sEMG?;9. Where within the muscle does the sEMG signal originate?;10. What is the optimal placement of the electrodes?;11. What is the position of the tendon or what is the fibre length respectively?;12. How large is the cross-talk from other muscles?;13. What is the position of the end-plate zone?

3.2.2. Structure of the motor unitThe previous section describes the possible compo-

nents of a single muscle fibre action potential as electri-cal source. This conceptualisation has to be followed bya description of the collective activity of muscle fibresin a MU. This translation from single fibre to MUAP is

essential because of the variation of position, of propa-gation velocity and of individual differences betweenfibres within a MU. In other words, a MUAP of a MUwith N fibres is not simply the SFAP wave shape withan N times larger amplitude.

The differences between individual fibres have conse-


quences for the spatial and temporal properties of theirSFAP contributions. In this context, anatomical regionssuch as (i) end-plate positions, (ii) fibre endings, (iii)relative positions of muscle fibres within a MU and (iv)the location of MUs within a muscle are all significant.Fig. 3 demonstrates a representation of anatomicalregions of the muscle fibre population within a singleMU. Functional information on propagation in the ter-minal motor axon branches and the distribution of propa-gation velocities of fibres within a MU have to added inconjunction with these anatomical data [4,31,33,64,61].

3.2.3. Muscle source functionThe MUAP is the basic building block of the sEMG.

Thus, an adequate MUAP model can be used to answerquestions regarding the potentials as recorded from theskin surface. However, because the sEMG is comprisedof contributions from all active MUs in the muscle, adescription of the sEMG is incomplete when it does notconsider the variability between MUs and their respect-ive recruitment and firing behaviour (see Section 3.4).The variability of the MUAP wave shape is mostlyrelated to differences in MU anatomy: the size of theMUs differs, as does their position. Also, differences intheir physiology (fibre type, MFCV) may be takeninto account.

3.2.4. Miscellaneous remarks on source descriptionsThe prediction of the force added by a MU or by a

single muscle fibre is functionally the final property thatmay be included in a muscle model [29,79]. Althoughmany theories and models, even more than for sEMG,regarding the muscle’s mechanical properties weredescribed, it is difficult to acknowledge the state of theart in both domains (EMG, muscle mechanics) in a sin-gle comprehensive model [41,91].

Since most information related to MUs cannot begained by methods other than the interpretation of EMGrecordings, it is especially difficult to obtain independentconfirmation for the choices made on a single MU level.Only a limited number of animal experiments [34],advanced electrophysiological techniques using multi-

Fig. 3. Basic structural elements of a MU as they can be taken into account. The muscle fibres are randomly scattered in a restricted territory(intermingled with the fibres of other MUs). Their end-plates and fibre–tendinous transitions are randomly scattered in a relatively thin, but skew,region. The muscle fibres are all excited by the sameα-motor neuron, via its axon and terminal branches.

electrodes [6] and stereotactics (scanning EMG; [32,78])are available to support choices at a MU level.

3.3. Volume conduction

3.3.1. GeneralTissue acts as a ‘volume conductor’. Volume conduc-

tion is the sole reason that extracellular electrophysiolog-ical measurements are possible even at some distancefrom the actual source. However, volume conduction isa complex and often a counterintuitive phenomenon,which can hardly be understood quantitatively withoutmodelling. Moreover, the volume conduction mech-anism is not the type of problem where the average neur-ologist, neurophysiologist or human movement scientistis going after primarily. He or she is interested in neuro-physiological processes, not in the laws of electrostatics.Nevertheless, many questions posed to electrophysiolog-ical signals remain unanswered without proper knowl-edge of volume conduction and its implementation inmodels. The lack of intuition for volume conductionphenomena might be regarded as the main reason formisinterpretation of the sEMG signal and (thus) the mainreason for most model developments in EMG.

3.3.2. Infinite volume conductorIn terms of a model description, it is most straightfor-

ward to assume that the volume conductor is infinite,isotropic, homogeneous and purely resistive. The lastassumption (see Section 3.3.3) leads to quasi-stationar-ity, i.e. the potential field at any moment is determinedby the sources at that same instant only. The first inevi-table correction is to acknowledge that muscle tissue isanisotropic with higher conductivity in muscle fibre(axial) direction than in the (radial) direction perpendicu-lar to the fibres. This is a simple adaptation, since it maybe introduced by a co-ordinate scaling in one of thesemain directions [73]. Another simple adaptation is toassume an infinite flat plane to represent the skin surface.For sEMG, this implies a doubling of the predictedpotentials by virtue of the method of images [43]. Thesolution for this simple description of the problem has


been published several times. The fundamental conceptis that the extracellular potential at axial sitez and radialdistancer from the muscle fibre is supported by sourceelement Im(s) at site s along the fibre by an amountwhich is inversely proportional to the distance (adaptedby the anisotropy) of this element to the observation site.In cylindrical co-ordinates (r, z):

fe(r,z)521

4psrEL

2L

2paIm(s)

!r2sz

sr

+(z−s)2

ds (2)

wheresz andsr are the electrical conductivities in fibredirection (z) and perpendicular to that (r), respectively.The variablea is the effective fibre radius;a is muchsmaller thanr, so thatr<r±a justifies the line sourceassumption. The constant of 2 at the beginning of theright hand side of Eq. (2) represents the skin surfaceimaging mentioned. As stated, this description or smallvariations of it is by far the most dominant and oldest[4,33,73] volume conductor description in EMG models.Fig. 4 gives an example of the monopolarly recordedpotential waveforms with such an infinite volume con-ductor model simulated over one half of the MU’s mus-cle fibres, utilising a number of model concepts (tripolesource, finite muscle fibre length) into the model.

Fig. 4. A simulated set of MU action potentials, as monopolarlyrecorded in the main fibre direction with an electrode shift of 20 mmbetween the traces. Note the genesis of the action potential at timet=0ms, the propagation of the main negative deflection (velocity 4 m/s),and finally the extinction of the action potentials at about 23 ms forone and at about 31 ms for another muscle–tendon transition (the endplate region is not centrally placed). The volume conductor is infiniteand anisotropic. A tripolar source configuration is used (from [61]).

3.3.3. Inhomogeneities and frequency dependency oftissue

The above homogeneity assumption should be con-sidered with care. First, layers of various tissues betweena muscle and the electrodes can be taken into accountin a volume conduction model. Subcutaneous fat layersare known to have a low conductivity. This aspect wasincorporated in an EMG model by Gootzen et al. [31]and later by Disselhorst-Klug et al. [17]. From morerecent studies, it appeared that the effect of the subcut-aneous layer is only represented adequately if one alsoconsiders the presence of the relatively high conductivityof the skin layer into account [24,70] (Fig. 5). The effectof this combination of layers (increased spread of currentand, therefore, of potential fields) is similar to the so-called ‘blurring’ of the EEG over the head because ofthe skull–skin combination [55]. Schneider et al. [75]pointed to the possibility of more subtle EMG signaldistortions by local tissue inhomogeneities as the pres-ence of local blood vessels or connective tissue.

An often reappearing point of discussion is the fre-quency dependence of EMG signals caused by the pres-ence of capacitive effects in layered muscle tissue. Thiswould directly affect the quasi-stationarity aspect men-tioned above. No definitive answer has been givenalthough it is made plausible that for high frequencies(over several kHz), as are present in EMG recorded withneedle or wire electrodes, a pertinent influence of capaci-tive aspects of the muscle tissue has to be expected[30,74,86]. As far as we know, it has not been clarifiedin how far these influences, observed extracellularlyclose to the source, also influence the sEMG noticeably.Such aspect has never been incorporated in an sEMGmodel.

3.3.4. Recording configurationClosely related to volume conduction is the influence

of electrode geometry and the recording configuration.The EMG signal is strongly dependent on the placementof the electrodes [42]. A general physical principle ofan electrophysiological recording is that the electroderecords the average potential under its area. This insightis based on the fact that a relatively high impedance elec-trochemical double layer is formed between the metal ofthe electrode and the conducting tissue [89]. Conse-quently, the influence of the size of the electrode on thesEMG signal can be estimated relatively easily. Whenthe EMG potential field under a certain electrode is notconstant, the difference between potentials recorded withthat electrode and those recorded with a smaller one canbe substantial. Some experimental studies in which theelectrode size was varied, show a considerable effect[37,54]. It may be necessary in many cases to include afinite electrode in an sEMG model [16,29]. A similarreasoning applies to the effect of different lead-off mon-tages (e.g. bipolar, double differential, Laplacian, elec-


Fig. 5. A schematic presentation of (a) a one, (b) a two and (c) a three layer volume conductor model in which muscle tissue (good conductivity,horizontal hatching), subcutaneous fat layer (bad conductivity, oblique hatching) and a skin surface layer (good conductivity, vertical hatching) areindicated. The amplitude distribution (curves above the hatching) of the radial (perpendicular to fibre direction) decline of the amplitude of theMUP amplitude of a MU (black circle) as dependent on the differences between the configurations is also schematically indicated. Note that theeffect of the 2-layer model is opposite to that of the 3-layer model (after [70]).

trode-set orientation with respect to the main fibredirection). Here also, the spatial properties of the poten-tial field in the volume conductor determine the effecton the recording [69].

The location of the muscle regions contributing to ansEMG signal, measured with a specific lead-off montage[71], including the influence of neighbouring muscles(cross-talk, e.g. [48]) belongs to the subjects where aproper model study can be of substantial help.

3.4. Motor unit recruitment and firing behaviour

3.4.1. GeneralThere is a large variability between muscles with

respect to MU recruitment and MU firing rate coding inorder to obtain the required muscle force. These differ-ences are related to the specific task aspects of the mus-cle (e.g. fine or coarse motor tasks) and thus to its sizeand composition. The recruitment and firing behaviourof MUs within the muscle must be defined in a modelfor the interference pattern. The ‘classical’ paradigm isthe so-called Henneman size principle [28,38,62], whichstates that with increasing muscle force, progressivelylarger MUs are recruited. That this, functionally con-vincing, principle is not generally valid becomes moreand more clear from recent studies. In the arm muscles,for example, the order of recruitment appears stronglydependent on the specific task requirements, especiallymovement speed and direction [82]. This complexbehaviour is a challenge for sEMG analysis in general,but is a burden for (inverse) models unravelling thesEMG interference pattern into the contributions ofMUs. One has to deal with a near endless set of possibleactivation patterns.

Finally, the recruitment of each new MU and a changein its firing frequency has consequences for the mechan-ical output of the muscle [28,36,91]. Here also the Hen-neman principle could be a leading factor again with therisk of over-simplification.

In the context of this overview on model applications,

only the modelling of the firing patterns in a stationarycondition (constant isometric load), assuming a constantactivation of the MU pool, will be discussed. The MUfiring processes (statistics and mutual dependency of fir-ing events) then can be described by the following para-meters:

O Distribution of the interpulse interval statistics of asingle MU

O Distribution of the interpulse interval statisticsbetween different active MUs

O Interdependency of the firing moments of the differentMUs (synchronisation)

3.4.2. Motor unit interpulse intervalsThe general principle describing a MU firing process

in terms of consecutive interpulse intervals (IPIs) is toconsider them as independent samples of a random vari-able. In the literature various distribution functions havebeen selected to most appropriately fit the IPI histogramsderived from experimental data. In most cases, a Gaus-sian distribution function was confirmed[8,26,27,49,63,65], but also a Weibull distribution[9,11], a Poisson distribution [5] or a Gamma distri-bution [77] were proposed.

Person and Kudina [65] and Kranz and Baumgartner[49] found that at low firing rates the IPI histogramswere slightly skewed and became more symmetrical athigher firing rates. Based on the observation that thecharacter of the distribution only has minor influences onthe sEMG spectral content [90], a Gaussian distributionappears justifiable to model the consecutive IPIs of aMU.

With respect to the variability of the firings, often con-stant values are chosen, irrespective of the chosen meanvalue. Typical values for the coefficient of variationreported are about 0.1 up to 0.33 (,sd=10–30 ms at10 Hz).

Sometimes (e.g. [19]) a refinement has been


implemented in the sense that the standard deviation isrelated to the mean interpulse interval. This is based onthe experimental finding [8] that when a MU is firing ata higher rate, it fires more regularly [7].

3.4.3. Mean interpulse intervals across motor unitsDifferences in mean IPI of the MUs reflect the differ-

ences in level of activity and, consequently, the forceoutput of the MUs. Once a MU has been recruited, itsfiring rate will increase with increasing force. It has beenshown that MUs recruited at a higher force level showa higher initial firing rate, although those MUs usuallyfire at a lower rate than the earlier recruited MUs at thesame force level [10]. Crudely, MUs tend to increasetheir firing rate approximately linearly with the forceoutput of the muscle.

In contrast to the IPI within a single MUAP train, nosystematic assessment could be found in the literature todescribe the distribution of mean IPIs of the whole poolof active MUs. However, visual inspection of some fig-ures in published articles show that firing rates of simul-taneously active MUs, never differ by more than 10 Hz[45]. The variability seems to decrease with increasingforce level [10]. At this moment, there still is no reliableexperimental method available to study the firing pat-terns of the majority of the MUs in a muscle.

3.4.4. Motor unit synchronisationStrongly related to the previous aspects is the question

to what extent the sEMG pattern is influenced by a poss-ible effect of synchronisation between active MUs. Thisquestion often appears in relation to the observation ofdeclining frequency content of the sEMG in musclefatigue. After many years of discussion, it is still a matterof controversy. A declining MFCV (see below) onlypartly explains the frequency decrease and an increasedsynchronisation is an appreciated explanation for this‘frequency gap’ (e.g. [57]).

Data as presented for instance by Farmer et al. [25]confirm that an IPI histogram of two MU patterns showa small but significant correlation between the two fir-ing patterns.

The synchronisation of the firing events between dif-ferent MUs can be implemented by generating the firinginstants of a first train and then link the firing instantsof consecutive trains to them. In early studies [52,66] aconstant delay was used, which is mathematically con-venient but not very realistic for a biophysical process.Weytjens and Steenberghe [90] and later Hermens [39]used a Gaussian distribution to model firing synchronic-ity. This distribution is placed on the firings of a firsttrain. Firings of synchronised MUs are then drawn fromthis distribution function. This choice is based upon theexperimental work of Kirkwood and Sears [46]. Yao etal. [91] define a random fraction of the firings of a cer-

tain MU which is synchronised to a random fraction ofthe nearby MU firings.

It was shown [2,40] that synchronised MU activityindeed is able to decrease the median frequency con-siderably (Fig. 6), depending on the number of MUs thathave synchronised activity and the extent to which theyare synchronised.

Most experimental synchronisation studies concernlow level contractions. Synchronisation at higher(30%MVC) contraction strengths in different muscles isdescribed in [12]. So, although the existence of MUsynchronisation is beyond debate, there still is no clearconsensus about its precise character and on its role insEMG characteristics.

An ‘extreme’ form of synchronised activity betweenMUs and muscle fibres occurs when the MUs are simul-taneously electrically activated via their motor axon.This form of activation can also be implemented in ansEMG model [61]. The simultaneity of the individualMUAPs in the CMAP must be defined in the modelbecause it is an essential factor in its interpretation.Because of different delays in different motor axons andmuscle fibres, a temporal dispersion of arrival times alsotakes place in the electrically stimulated situation, withnon-trivial consequences for amplitude and frequencybehaviour of the CMAP.

4. Related remarks

4.1. Descriptions in the frequency domain

As most of the readership is accustomed to filteringof signals, it might be clarifying to look at the effect of

Fig. 6. The dependence of the median frequency (MF) on the levelof synchronisation in a MU pool (ss is the standard deviation of the‘freedom of firing’ of the MUs with respect to each other in percent-ages of the mean firing interval). Forss=0, a perfect synchronisationof MUs indicated. Largess values denote independent firing behaviourbetween the MUs. Note that the median frequency increases for lowand for high levels of synchronicity. The percentage indicated at thedifferent curves denotes the fraction of the MUs being ‘time connec-ted’ (from [40]).


firing behaviour, MU structure, volume conduction andthe recording configuration in the frequency domain,more concretely in terms of filtering in thespatial fre-quency domain [13]. This was already illustrated in anearly study of Lindstrom [56]. When the IAP is con-sidered as the basic source of EMG activity, a first fil-tering operation is the second spatial derivative (e.g.from Eq. (1a) to Eq. (1b)), a strong high pass effect. Thescattered position of fibres in a MU results in a spatialsmoothing (low-pass filtering) of the source signal. Vol-ume conduction can also be regarded as a spatial low-pass phenomenon [81]. For instance, the effect of Eq.(2) is a low pass filtering from the input signalIm(z) asa function ofz to the output signalfe(r,z) as a functionof z. Special care should be taken not to confuse thesespatial filtering effects with temporal filtering. Thereonly is a clear-cut relation between the spatial volumeconduction and temporal filtering when the sourcepropagates with a constant velocity. Time (t) and space(z) are then connected through the constant propagationvelocity (U): z=u.t. It should be realised, however, thatthis relation does not hold for the genesis and the extinc-tion of the IAP. The non-propagating dipolar source atthe extinction of the action potential at the tendon [31]is the best example in sEMG where spatial volume con-duction filtering does not have consequences for the tem-poral signal characteristics.

Finally, the signal characteristics of a firing MU arebest understood in the temporal frequency domain wherethe description of the convolution is reflected as a multi-plication of the spectrum of the firing pattern and thespectrum of the MUAP wave shapes [2,52,83].

4.2. Combination and interrelations of modelproperties

The above enumeration of different aspects of a struc-ture based EMG model might conceal the fact thatalmost all of these elements are interrelated. Forinstance, details in the source description can only berecognised in the sEMG when the observation distanceis small enough. A relevant example of interactionsbetween source and volume conductor is related to thementioned dipolar components in the single fibre sourceduring extinction of the action potentials at the tendon.It appears that in combination with a finite volume con-ductor, these dipolar source components can evoke so-calledfar-fieldswhich do not decline with distance fromthe source [31,80]. Another obvious interrelationshipbetween different aspects of a model is that between therecruitment behaviour of a MU and its size [91].

It should further be noted that a model result is notnecessarily based completely on calculated predictions.It can be a hybrid in the sense that part of the aspectsare not model generated but obtained from experimentaldata. For instance, Hermens [39] used measured MUAP

waveforms in his model in which the central issue wasthe influence of individual MUAP firing patterns andtheir interrelations. Or, an IAP can be based on experi-mental data [23] so as to predict extracellular potentialwaveforms on the basis of experimental IAP obser-vations.

4.3. Inverse modelling

One might argue that the ultimate aim of all modellingattempts is inverse modelling, i.e. the estimation ofunderlying process parameters from experimental obser-vations. In general the ‘inverse problem’ is ill-posed[85]. The interrelationships between different modelaspects indicated above play a crucial role. For instance,different assumptions with respect to underlying clustersof active single fibres may lead to exactly the same skinpotential field distribution and thus cannot be discernedfrom one another. A method straightforwardly searchingfor details in the underlying activity of MUs will, there-fore, fail. One key here is to implement as much a prioriknowledge as possible. In the case of sEMG such knowl-edge may concern, for instance, the depth of the muscleinvolved and its main fibre direction. Again, filtering isa way to look at the ‘loss-of-information’ involved. Thespatial filtering induced by the electrode, by the volumeconductor (low-pass) and by the montage chosen (mostlyhigh-pass) give wanted, but also unwanted and unavoid-able loss of information on details of the underlyingactivity, which can never be reconstructed or only withadequate use of a priori knowledge.

4.4. Computing principles and efficiency

As to now, most EMG models are analytical modelsfor which a relatively simple co-ordinate system mustbe defined (e.g. cylindrical), which means that they arebased on an analytical mathematical expression (e.g.Eqs. (1a, 1b) and (2)) which can be easily evaluated withcomputers. Alternatively, purely numerical methods canbe used, for which the volumes or their boundary sur-faces are covered with the necessary precision (finiteelements method, boundary elements method). Morecomplex source and/or volume conductor configurationsask for the latter type of approach.

Despite the increasing power of computers, it is stillwise to consider the computational principles applied.This is of importance especially in inverse model appli-cations where the most frequently applied method is arepeated use of a forward model with advanced para-meter optimisation algorithms. Care should be taken thatcalculations are made in an efficient order, so as to avoidunnecessary repetitions. Often such considerations ofefficiency also increase the insight in the problem tosolve. An example of such reasoning can be found in[14,15].


5. Model design

Table 1 is meant to guide the reader through the useof sEMG existing models or, when necessary, to decidewhich properties a model should minimally have. In thistable, the ‘×’ symbol denotes a preferable or minimallynecessary aspect in the model concerning a specificquestion. The ‘s’ symbol marks aspects that are poss-ibly useful, but in our view are not core elements of amodel for that question. Table elements without a sym-bol denote that the concerning element can be taken intoa model, but would probably make it unnecessarily com-plicated.

This table is organised along a list of possible ques-tions to the sEMG. The questions are ordered (1–13)along an estimated decrease of the minimally necessaryaspects (×) of an sEMG model. Apparently, the numberof model elements, strictly necessary in any one of thequestions, never exceeds half the total number listed.The table has to be considered as a way to guide thediscussion of sEMG model use and development. Thereader should not read it as ‘the whole truth’. Of coursehe or she should make his own thoughts with respect tothe specific questions at hand.

6. Conclusion

This paper deals with the properties of structure basedmodels describing sEMG patterns and the assumptionsbehind them. Since the mid 1970s, a considerableamount of papers dealing with the use and developmentof EMG models was published. Models were certainlyhelpful in obtaining insight in the basic elements of sur-face (and also needle) EMG characteristics. In such asituation it is a valid task to search for a uniformapproach. Considering the variety of questions posed tothe EMG and thus to the models, such a goal will prob-ably never be reached in full. The present overview andthe attempt to guide the user through possible and neces-sary model aspects hopefully meets the objective of giv-ing insight in the possibilities and limitations of sEMGmodels for a large variety of applications.

Acknowledgements

This paper could be conceptualised and worked outthanks to all the contributors to the SENIAM workshopin Nijmegen in February 1998. Essential was the helpof those colleagues who did respond to our questionnairewith respect to the properties of their EMG models (A.Fuglevand et al. [28,91]; V. Hermans et al.; D. Zazulaet al.; C. Disselhorst-Klug et al. [17]; J. Hogrel and J.Duchene [20]; C. Baten [2]; R. Merletti et al. [61]; W.Wallinga et al. [88]; G. Dimitrov and N. Dimitrova

[14,15]). It had direct impact on the paragraph on poss-ible elements in EMG models. Some of these modelsare available on the SENIAM CD-ROM [76], availablethrough the first author or via the website: www.rrd.nl.Daniel Dumitru, MD, PhD is acknowledged for his com-ments to the manuscript.

References

[1] Andreassen S, Rosenfalck A. Relationship of intracellular andextracellular action potentials of skeletal muscle fibres. CRC CritRev Bioeng 1981;6:267–306.

[2] Baten C, Hermens HJ. Effects of changes in muscle control strat-egy on the surface EMG amplitude and frequency parameters. In:Hermens HJ, Stegeman DF, Blok J, Freriks B, editors. The stateof the art on modelling methods for surface electromyography,deliverable 6 of the SENIAM project. Enschede: RoessinghResearch and Development, 1998:117–24.

[3] Boom HBK, Wallinga W. Source characteristics from inversemodelling of EMG signals. In: Gath I, Bar GF, editors. Advancesin processing and pattern analysis of biological signals. NewYork: Plenum Press, 1996:319–38.

[4] Boyd DC, Lawrence PD, Bratty PJ. On modelling the singlemotor unit action potential. IEEE Trans Biomed Engng1978;25:236–43.

[5] Brody G, Scott RN, Balasubramanian R. A model for myoelectricsignal generation. Med Biol Eng 1974;12:29–41.

[6] Buchthal F, Guld C, Rosenfalck P. Multielectrode study of theterritory of a motor unit. Acta Physiol Scand 1959;39:83–104.

[7] Clamann HP. Statistical analysis of motor unit firing patterns ina human skeletal muscle. Biophys J 1969;9:1233–51.

[8] Clamann HP. Activity of single motor units during isometric ten-sion. Neurology 1970;20:254–60.

[9] De Luca CJ. Physiology and mathematics of myoelectric signals.IEEE Trans Biomed Engng 1979;26:313–25.

[10] De Luca CJ, Erim Z. Common drive of motor units in regulationof muscle force. Trends Neurosci 1994;17:299–305.

[11] De Luca CJ, Forrest WJ. Some properties of motor unit actionpotential trains recorded during constant force isometric contrac-tions in man. Kybernetik 1973;12:160–8.

[12] De Luca CJ, Roy AM, Erim Z. Synchronisation of motor unitfirings in several human muscles. J Neurophysiol1993;70:2010–23.

[13] De Weerd JPC. Volume conduction and electromyography. In:Notermans SLH, editor. Current practice of electromyography.Amsterdam: Elsevier, 1984:9–28.

[14] Dimitrov GV, Dimitrova NA. Precise and fast calculation of themotor unit potentials detected by a point and rectangular plateelectrode. Med Eng Phys 1998;20:374–81.

[15] Dimitrova NA. Precise and fast MUP as as prerequisite forinverse EMG modelling. In: Hermens HJ, Stegeman DF, Blok J,Freriks B, editors. The state of the art on modelling methods forsurface electromyography, deliverable 6 of the SENIAM project.Enschede: Roessingh Research and Development, 1998:45–60.

[16] Dimitrova NA, Dimitrov GV, Chikhman VN. Effect of electrodedimensions on motor unit potentials. Med Eng Phys1999;21:479–85.

[17] Disselhorst-Klug C, Silny J, Rau G. Estimation of the relationshipbetween the noninvasively detected activity of single motor unitsand their characteristic pathological changes by modelling. JElectromyogr Kinesiol 1998;8:323–35.

[18] Drost G, Zwarts MJ, Stegeman DF, Van Dijk JP. Muscle weak-ness in Becker’s disease: a multi-channel surface EMG study.Clin Neurophysiol 1999;110(Suppl. 1):S87.


[19] Duchene J, Hogrel JY. SEMG simulation for MFCV distributionestimates assessment. In: Hermens HJ, Hagg G, Freriks B, edi-tors. European applications on surface electromyography, Pro-ceedings of the Second General SENIAM Workshop, Stockholm,Sweden, June 1997. Enschede: Roesssingh Research and Devel-opment, 1997:122–8.

[20] Duchene J, Hogrel JY. A model of EMG generation. IEEE TransBiomed Engng 2000;47:192–201.

[21] Dumitru D. Electrodiagnostic medicine. Philadelphia: Hanley andBelfus, 1995.

[22] Dumitru D, King JC. Varied morphology of spontaneous singlemuscle fibre discharges. Am J Phys Med Rehab 1998;77:128–39.

[23] Dumitru D, King JC, Rogers WE, Stegeman DF. Positive sharpwave and fibrillation potential modelling. Muscle Nerve1999;22:242–51.

[24] Farina D, Rainoldi A. Compensation of the effect of sub-cutaneous tissue layers on surface EMG: a simulation study. MedEng Phys 1999;21:487–97.

[25] Farmer SF, Halliday DM, Conway BA, Stephens JA, RosenbergJR. A review of recent applications of cross-correlation method-ologies to human motor unit recording. J Neurosci Meth1997;74:175–87.

[26] Freund HJ, Budingen HJ, Dietz V. Activity of single motor unitsfrom human forearm muscles during voluntary isometric contrac-tions. J Neuropsysiol 1975;38:933–46.

[27] Freund HJ, Wita CW. Computer analysis of interval statistics ofsingle motor units in normals and patients with supraspinal motorlesions. Arch Psychiatr Nervenkr 1971;214:56–71.

[28] Fuglevand AJ, Winter DA, Patla AE. Models of recruitment andrate coding organization in motor-unit pools. J Neurophysiol1993;70:2470–88.

[29] Fuglevand AJ, Winter DA, Patla AE, Stashuk D. Detection ofmotor unit action potentials with surface electrodes: influence ofelectrode size and spacing. Biol Cybern 1992;67:143–53.

[30] Gielen FL, Wallinga-De Jonge W, Boon KL. Electrical conduc-tivity of skeletal muscle tissue: experimental results from differ-ent muscles in vivo. Med Biol Eng Comput 1984;22:569–77.

[31] Gootzen THJM, Stegeman DF, Van Oosterom A. Finite limbdimensions and finite muscle length in a model for the generationof electromyographic signals. Electroencephalogr Clin Neurophy-siol 1991;81:152–62.

[32] Gootzen THJM, Vingerhoets HJM, Stegeman DF. A study ofmotor unit structure by means of scanning EMG. Muscle Nerve1992;15:349–57.

[33] Griep PA, Boon KL, Stegeman DF. A study of the motor unitaction potential by means of computer simulation. Biol Cybern1978;30:221–30.

[34] Griep PA, Gielen FL, Boom HB, Boon KL, Hoogstraten LL, PoolCW, Wallinga-De Jonge W. Calculation and registration of thesame motor unit action potential. Electroencephalogr Clin Neuro-physiol 1982;53:388–404.

[35] Gydikov A, Gerilovski L, Radicheva N, Troyanova N. Influenceof the muscle fibre end geometry on the extracellular potentials.Biol Cybern 1986;54:1–8.

[36] Halliday DM, Conway BA, Farmer SF, Rosenberg JR. Load-independent contributions from motor-unit synchronisation tohumanphysiological tremor. J Neurophysiol 1999;82:664–75.

[37] Helal JN, Bouissou P. The spatial integration effect of surfaceelectrode detecting myoelectric signal. IEEE Trans BiomedEngng 1992;39:1161–7.

[38] Henneman E. Recruitment of motoneurons: the size principle. In:Motor unit types, recruitment, and plasticity in health and disease.Progr Clin Neurophysiol 1981;9:26–60.

[39] Hermens HJ. Surface EMG. Ph.D. thesis, Twente University ofTechnology, 1991.

[40] Hermens HJ, Van Bruggen TAM, Baten CTM, Rutten WLC,Boom HBK. The median potential frequency of the surface EMG

power spectrum in relation to motor unit firing and action proper-ties. J Electromyogr Kinesiol 1992;2:15–25.

[41] Hof AL. EMG and muscle force: an introduction. Hum Mov Sc1984;3:119–53.

[42] Hogrel JY, Duchene J, Marini JF. Variability of some sEMGparameter estimates with electrode location. J ElectromyogrKinesiol 1998;8:305–15.

[43] Jackson JD. Classical electrodynamics. New York: John Wileyand Sons, 1998.

[44] Jansen PH, Joosten EM, Vingerhoets HM. Muscle cramp: maintheories as to aetiology. Eur Arch Psychiat Neurol Sci1990;239:337–42.

[45] Kamen G, De Luca CJ. Firing rate interactions among humanorbicularis oris motor units. Int J Neurosci 1992;64:167–75.

[46] Kirkwood PA, Sears TA. The synaptic connections to intercostalmotoneurones as revealed by the average common excitationpotential. J Physiol 1978;275:103–34.

[47] Kleissen RFM, Buurke JH, Harlaar J, Zilvold G. Electromyogra-phy in the biomechanical analysis of human movement and itclinical application. Gait Posture 1998;8:143–58.

[48] Koh TJ, Grabiner MD. Evaluation of methods to minimize crosstalk in surface electromyography. J Biomech1993;1(Suppl):151–7.

[49] Kranz H, Baumgartner G. Human alpha motorneurone discharge,a statistical analysis. Brain Res 1974;67:324–9.

[50] Kumar S, Mital A. Electromyography in ergonomics. London:Taylor and Francis, 1996.

[51] Kyrolainen H, Komi PV, Belli A. Mechanical efficiency in ath-letes during running. Scand J Med Sci Sports 1995;5:200–8.

[52] Lago PJ, Jones NB. Effect of motor unit furing time statistics onEMG spectra. Med Biol Eng Comp 1977;15:648–55.

[53] Lateva ZC, McGill KC. The physiological origin of the slowafterwave in muscle action potentials. Electroencephalogr ClinNeurophysiol 1998;109:462–9.

[54] Lateva ZC, McGill KC, Burgar CG. Anatomical and electrophy-siological determinants of the human thenar compound muscleaction potential. Muscle Nerve 1996;19:1457–68.

[55] Le J, Gevins A. Method to reduce blur distortion from EEG’susing a realistic head model. IEEE Trans Biomed Engng1993;40:517–28.

[56] Lindstrom L. Contributions to the interpretation of myoelectricpower spectra. Ph.D. thesis, Chalmers University, Goteborg,1974.

[57] Linssen WHJP, Stegeman DF, Joosten EMG, Van ’t Hof MA,Binkhorst RA, Notermans SLH. Variability and interrelationshipsof surface EMG parameters during local muscle fatigue. MuscleNerve 1993;16:849–56.

[58] Luttmann A, Jager M, Sokeland J, Laurig W. Electromyograph-ical study on surgeons in urology; II: determination of muscularfatigue. Ergonomics 1996;39:298–313.

[59] Masuda T, Sadoyama T. Skeletal muscles from which the propa-gation of motor unit action potentials is detectable with a surfaceelectrode array. Electroencephalogr Clin Neurophysiol1987;67:421–7.

[60] McComas AJ. Motor units: how many, how large, what kind? JElectromyogr Kinesiol 1998;8:391–402.

[61] Merletti R, Lo Conte L, Avignone E, Guglielminotti P. Modellingof surface myoelectric signals; part I: model implementation.IEEE Trans Biomed Engng 1999;46:810–20.

[62] Milner-Brown HS, Stein RB, Yemm R. The orderly recruitmentof human motor units during voluntary isometric contractions. JPhysiol 1973;230:359–70.

[63] Mori S. Discharge patterns of soleus motor units with associatedchanges in force exerted by foot during quiet stance in man. JNeurophysiol 1973;36:458–71.

[64] Nandedkar SD, Sanders DB, Stalberg EV, Andreassen S. Simul-


ation techniques in electromyography. IEEE Trans BiomedEngng 1985;31:775–85.

[65] Person RS, Kudina LP. Discharge frequency and discharge pat-tern in human motor units during voluntary contractions of mus-cle. Electroenceph Clin Neurophysiol 1972;32:47–83.

[66] Person RS, Libkind MS. Simulation of electromyograms showinginterference patterns. Electroenceph Clin Neurophysiol1970;28:625–32.

[67] Plonsey R. The active fibre in a volume conductor. IEEE TransBiomed Engng 1974;21:371–81.

[68] Post A, Pijpers JR, Bosch P, Boschker MSJ, editors. Models inhuman movement sciences, IFKB Amsterdam/Nijmegen.Enschede: PrintPartners Ipskamp, 1998.

[69] Reucher H, Silny J, Rau G. Spatial filtering of noninvasive multi-electrode EMG; part II: filter performance in theory and model-ling. IEEE Trans Biomed Engng 1987;34:106–13.

[70] Roeleveld K, Blok JH, Stegeman DF, Van Oosterom A. Volumeconduction models for surface EMG; confrontation with measure-ments. J Electromyogr Kinesiol 1997;7:221–32.

[71] Roeleveld K, Stegeman DF, Vingerhoets HM, Van Oosterom A.Single motor unit potential contribution to surface electromyogra-phy. Acta Physiol Scand 1997;160:175–83.

[72] Roeleveld K, Stegeman DF, Vingerhoets HM, Van Oosterom A.The motor unit potential distribution over the skin surface andits use in estimating motor unit location. Acta Physiol Scand1997;161:465–72.

[73] Rosenfalck P. Intra- and extracellular potential fields of activenerve and muscle fibres. Acta Physiol Scand1969;321(Suppl.):1–168.

[74] Roth BJ, Gielen FLH, Wikswo JP. Spatial and temporal fre-quency-dependent conductivities in volume conduction calcu-lations of skeletal muscle. Math Biosci 1988;88:159–89.

[75] Schneider J, Silny J, Rau G. Influence of tissue inhomogeneitieson noninvasive muscle fibre conduction velocity measurementsinvestigated by physical and numerical modelling. IEEE TransBiomed Engng 1991;38:851–60.

[76] SENIAM BIOMED II, European recommendations for surfaceelectromyography, CD-ROM, ISBN 90-75452-14-4, 1999.

[77] Shiavi R, Negin M. Stochastic properties of motoneuron activityand the effect of muscular length. Biol Cybern 1975;19:231–7.

[78] Stalberg E, Antoni L. Electrophysiological cross section of themotor unit. J Neurol Neurosurg Psychiat 1980;43:469–74.

[79] Stashuk DW. Simulation of electromyographic signals. J Electro-myogr Kinesiol 1993;3:157–73.

[80] Stegeman DF, Dumitru D, King JC, Roeleveld K. Near- and far-fields: source characteristics and the conducting medium in neur-ophysiology. J Clin Neurophysiol 1997;14:429–42.

[81] Stegeman DF, Linssen WHJP. Muscle fibre membrane electro-physiology and surface EMG: a simulation study. J ElectromyogrKinesiol 1992;2:130–40.

[82] Van Bolhuis BM, Medendorp WP, Gielen CCAM. Motor-unitfiring behaviour in human arm flexor muscles during sinusoidalisometric contractions and movements. Brain Res1997;117:120–30.

[83] Van Boxtel A, Schomaker LR. Motor unit firing rate during staticcontraction indicated by the surface EMG power spectrum. IEEETrans Biomed Engng 1983;30:601–9.

[84] Van Oosterom A. History and evolution of methods for solvingthe inverse problem. J Clin Neurophysiol 1991;8:371–80.

[85] Van Oosterom A. Principles of inverse electrophysiological mod-elling. In: Hermens HJ, Stegeman DF, Blok J, Freriks B, editors.The state of the art on modelling methods for surface electromy-ography, deliverable 6 of the SENIAM project. Enschede: Roes-singh Research and Development, 1998:37–44.

[86] Van Veen BK, Rijkhoff NJ, Rutten WL, Wallinga W, Boom HB.Potential distribution and single-fibre action potentials in a radi-

ally bounded muscle model. Med Biol Eng Comput1992;30:303–10.

[87] Wallinga W, Meijer SL, Aberink MJ, Vliek M, Wienk ED. Thebio-electrical source behind the (compound) motor unit actionpotential. In: Hermens HJ, Stegeman DF, Blok J, Freriks B, edi-tors. The state of the art on modelling methods for surface electro-myography, deliverable 6 of the SENIAM project. Enschede:Roessingh Research and Development, 1998:29–34.

[88] Wallinga W, Meijer SL, Alberink MJ, Vliek M, Wienk ED, YpeyDL. Modelling action potentials and membrane currents of mam-malian skeletal muscle fibres in coherence with potassium con-centration changes in the T-tubular system. Eur Biophys J1999;28:317–29.

[89] Webster JG, editor. Medical instrumentation: application anddesign. New York: John Wiley and Sons, 1997.

[90] Weytjens JLF, Van Steenberghe D. The effects of motor unitsynchronisation on the power spectrum of the electromyogram.Biol Cybern 1984;51:71–7.

[91] Yao W, Fuglevand RJ, Enoka RM. Motor-unit synchronisationincreases EMG amplitude and decreases force steadiness of simu-lated contractions. J Neurophysiol 2000;83:441–52.

[92] Zwarts MJ, Van Dijk JP. Methods to determine muscle fibre con-duction velocity. In: Hermens HJ, Stegeman DF, Blok J, FreriksB, editors. The state of the art on modelling methods for surfaceelectromyography, deliverable 6 of the SENIAM project.Enschede: Roessingh Research and Development, 1998:85–9.

[93] Zwarts MJ, Van Weerden TW. Transient paresis in myotonic syn-dromes, a surface EMG study. Brain 1989;112:665–80.

Dick Stegeman was born in Enschede, TheNetherlands on March 9, 1951. He received aM.Sc. in electrical engineering for the Univer-sity of Twente, Enschede. In 1977 he joined thelaboratory of Medical Physics and Biophysics,University of Nijmegen. He worked on modelstudies of electrical nerve activity. In 1981 hereceived his Ph.D. degree from the Universityof Nijmegen. Since 1984 he has been Head ofthe physics group at the Department of ClinicalNeurophysiology, Institute of Neurology. Since1997 he has held a part-time appointment in the

context of the research program “movement systems” of the Friedrich-Schiller University in Jena, Germany. His research interests include thephysics of neurophysiological processes, electrophysiological modellingand the theory of volume conduction, and also the quantitative analysisand source characterisation from the topography of EMG and brainactivity data.


Joleen Blok was born in Veldhoven, TheNetherlands, on January 22, 1972. She receivedan M.Sc. in experimental physics from the Uni-versity of Nijmegen, The Netherlands, in 1995.As a Ph.D. student at the Department of ClinicalNeurophysiology of the University Hospital StRadboud in Nijmegen, she is currently workingon clinical applications of multi-electrode sur-face electromyography. She combines herresearch with a course leading to registration asa professional clinical physicist. Her researchinterests include both fundamental and applied

surface electromyography and mathematical modeling of electrophysiol-ogical phenomena.

Hermie Hermens received his masters degreein biomedical engineering at the University ofTwente in 1981. In 1983 he became Head of theresearch group of the Roessingh RehabilitationCentre. He received his Ph.D. in 1991. His the-sis concerned model studies and clinical appli-cation of surface electromyography in rehabili-tation. In 1991 he was appointed as Director ofthe research institute born from the Roessinghresearch group: Roessingh Research and Devel-opment. Currently he supervises four Ph.D. stu-dents working in the fields of surface electromy-

ography and functional electrical stimulation. He participates in severalEuropean projects and is co-ordinator of two large European projects:CREST (computer aided rehabilitation using electrical stimulation andtelematics) and SENIAM (surface EMG for a non-invasive assessmentof muscles).

Karin Roeleveld was born in Hoorn, TheNetherlands, on July 28, 1969. She received herM.Sc. degree in human movement sciences fromthe Vrije Universiteit in Amsterdam, TheNetherlands in 1992. Thereafter, she joined theDepartment of Clinical Neurophysiology of theUniversity Hospital in Nijmegen, The Nether-lands to work as a Ph.D. student on the funda-mentals of surface electromyography usingmulti-electrode surface electromyography. Sheobtained her Ph.D. in 1997. Thereafter sheworked for 18 months as a post doc at the Motor

Research Group of the Friedrich-Schiller University, Jena, Germany. Cur-rently she has an appointment as Associate Professor at the Departmentof Sport Sciences, NYNU in Trondheim, Norway. Her research interestsare related to the neuromuscular system and focus on co-ordination ofhuman movement and signal analysis in fundamental and applied sur-face electromyography.

surface emg models properties and applications - 2000

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