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Critical Reviewsin Biomedical Engineering, 38(4):305345 (2010)

0278-940X/10/$35.00 2010 by Begell House, Inc.

I. INTRODUCTION

Te editorial introducing this issue oCritical Reviews

in Biomedical Engineeringprovides a general rame-work or the ast growing eld o electromyography.Tis review outlines a) some technical topics and someissues marginally or not addressed in recent overviewso the state o the art; and b) some problems, not

yet addressed or mostly unsolved and currently underinvestigation, that represent important challengesor the near uture.

Te electromyogram (EMG) is a compoundsignal comprising contributions o a ew dozens tomany hundreds o motor units (MU), which are acti-

vated asynchronously, depending on the contractionorce. Teir electrical contributions to the EMG, the

Advances in Surface EMG: Recent Progress inDetection and Processing Tecniues

Roberto Merletti,1* Matteo Aventaggiato,1 Alberto Botter,1 Ales Holobar,2Hamid Marateb,1 & Taian M.M. Vieira1

1Laboratory or Engineering o the Neuromuscular System (LISiN), Department o Electronics, Politecnicodi Torino, Italy; 2Faculty o Electrical Engineering and Computer Science, University o Maribor, Slovenia

*Address all correspondence to Pro. Roberto Merletti, LISiN, Politecnico di orino, Via Cavalli 22/H, 10138 orino (O), Italia; el: +39 011 433 0476; Fax: +39

011 433 0404; [email protected].

ABSTRACT:Tis article is the rst section o a review work structured in three parts and concerning a) advancesin surace EMG detection and processing techniques, b) recent progress in surace EMG clinical research applica-tions and, c) myoelectric control in neurorehabilitation. Tis article deals with the state o the art regarding a) theelectrodeskin interace (equivalent circuits, skin treatment, conductive gels), b) signal detection modalities, spatiallters and ront-end ampliers, c) power line intererence removal, separation o propagating and non-propagatingpotentials and removal o outliers rom surace EMG signal maps, d) segmentation o surace EMG signal maps,e) decomposition o surace EMG into the constituent action potential trains, and ) relationship between suraceEMG and orce. Te material is presented with an eort to ll gaps let by previous reviews and identiy areas openor uture research.

KEY WORDS: surace electromyography, electrode-skin interace, power line intererence, surace EMG decom-position, surace EMG imaging, EMG-orce relationship

ABBREVIATIONS

2-D, 2-dimensional;ARV, averaged rectied value; BSS, blind source separation; CMRR, common mode rejectionratio; CNS, central nervous system; CV, conduction velocity; DD, double dierential; DRL, driven right leg circuit;ECG, electrocardiogram; EEG, electroencephalogram; EMG, electromyogram or electromyography; HDsEMG,

high density surace electromyogram; ICA, independent component analysis; IED, inter electrode distance; LMS ,least mean square; LMSE, least mean square error; MART, multichannel adaptive resonant theory; ML, maximumlikelihood; MSE, mean square error; MU, motor unit; MUAP, motor unit action potential; MVC, maximal voluntarycontraction; NASICON, Na super ionic conductor; PCA, principal component analysis; PDE, partial dierentialequation; PLL, phase locked loop; RMS, root mean square; RMSD, root mean square dierence; S/H, sample andhold circuit; SD, single dierential; sEMG, surace electromyogram or surace electromyography;SENIAM, SuraceEMG or Non Invasive Assessment o Muscles (EU Project); SNR, signal-to-noise ratio; VG,virtual ground; VR,

virtual reerence

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motor unit action potentials (MUAP), superimposelinearly both in space and time creating a signal thatcan be detected with indwelling electrodes (needles,thin wires) or surace electrodes (sEMG). Te needle

detects signals generated within a volume o a ewcubic millimeters near the tip o the needle, resultingin high selectivity. On the contrary, surace electrodesdetect signals generated in a volume ranging roma ew to hundreds o cubic centimeters in the mostsupercial layers, depending on the electrode arrange-ment. Te signal on the surace o the skin is a two-dimensional (2-D) distribution o potential, whichmay be sampled with relatively ew electrodes. Eachsurace electrode samples (in space) this potentialdistribution, generated by the underlying sources,in one location (pixel) o the skin surace so that arather coarse image can be obtained.

In recent years, the detection o sEMG hasundergone considerable advances due to the devel-opment o linear and 2-D electrode arrays. Telatter provide time evolving spatial samples o theinstantaneous image o the surace potential distri-bution. A second type o image describes the spatialdistribution o sEMG variables (such as amplitudeor spectral variables estimated over a specied timeinterval) on the skin surace.

Successive rames o sEMG amplitudes, either

instantaneous or averaged, generate movies o neu-romuscular activity. In turn, this implies proper sam-pling o the images in time and space, an issue otenneglected or underestimated in practical applications(see Section II.B).

Recent developments in sEMG have taken placein many areas. Most o these developments concernthe design o electrode arrays, miniaturization othe ront-end electronics, solution o a number otechnical, processing and interpretation problemsconcerning the signals acquired with 2-D arrayso closely spaced electrodes (High Density surace

EMG, HDsEMG). Tese problems are much moreserious than those encountered with the classicalsingle electrode pair.

Te time evolution o the spatial distribu-tion o sEMG potentials is providing much moreinormation about the activation o the underlyingmuscle(s) than the classical pair o electrodes, used

in kinesiology, and opens new horizons to the non-invasive investigation o the central and peripheralneuromuscular system. Tis new branch o suraceelectromyography is evolving into an image-based

technique.Merletti and Farina recently published a review

on intramuscular EMG signal detection and process-ing,1 while S. Karlsson and co-workers published oneon sEMG processing techniques.2 A third recentreview on sEMG was published in Clinical Biome-chanics. 3 A ourth review4 dealt with sEMG in gaitanalysis, an issue not addressed in this work.

Tis review addresses issues concerning tradi-tional as well as HDsEMG and is structured insections describing technical challenges such as

the electrode-skin interace, multichannel detec-tion systems, removal o intererences and artiacts,separation o propagating and non-propagating com-ponents, identication o localized muscle activity,decomposition o the HDsEMG into the constitu-ent action potential trains, simultaneous detectiono intramuscular and HDsEMG, and EMG-orcerelationship. Tese topics may appear as a sample orelatively disconnected problems; the common ele-ment is the act that they are being addressed in anumber o laboratories. Most o them are unsolved

and represent open challenges that oer room orresearch to experienced investigators as well as tomotivated students.

II. ADVANCES IN SURFACE EMGDETECTION TEChNIqUES

II.A. Te Eectrode-Sin Interface

Equivalent circuit. Te electrode-skin interace is therst block o any system detecting bioelectric signals.

Such interace is complex because o the dierentcharge carriers involved in the media (electrons inthe metal material and ions in the gel and skin) andis roughly modeled as indicated in the box o Fig1a. It consists o an R-C network (Ze), a batterydescribing the hal-cell potential (Vb) and a noisegenerator (Vn).

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Advances in Surface EMG: Recent Progress in Detection and Processing Techniques 307

Te impedance Ze o Fig. 1a orms a voltagedivider with the input impedance o the amplier.In the case o dierential detection there are twosuch dividers, one or each input o the dierentialamplier. In general Zi>>Ze and the attenuation othe signal is negligible in either the monopolar or

dierential detection mode. However, i the twoelectrode impedances Ze1 and Ze2 are not identical acommon mode voltage VCM will generate a dierential

voltage Vd at the input o the dierential amplier.For Zi>>Ze this voltage value is approximately givenby Eq. (1).

FIGURE 1. a) Ze is the electrode-skin impedance; Zi is the amplier input impedance. They orm a voltage dividerwhose eect is important in transorming part o the common mode voltage into dierential voltage, as explained

in Fig. 2a. The equivalent circuit includes a DC generator (Vb) and a noise voltage generator (Vn). b) Magnitudeo the electrode-skin-electrode impedance at three requencies, with and without slight skin abrasion ater wash-ing with soap, 1 min and 30 min ater electrode application. One pair o Ag-AgCl electrodes (2 mm x 5 mm, 20mm IED) was placed on the back o the thumb by using perorated oam (lled with conductive gel). Sinusoidalvoltages (0.1 Vp) at 30 Hz, 500 Hz, and 10 kHz were applied to measure the impedance on 5 subjects (2 measure-ments or each subject). c) Magnitude o the impedance o the electrode-gel-skin system measured by applying a2.5 Ap sinusoidal current at 20 Hz between an Ag-AgCl electrode (2 mm x 8 mm) placed on biceps brachii (byusing perorated oam lled with conductive gel) and a reerence bracelet electrode (15 cm2 surace) placed aroundthe wrist o the same arm. The relative dierence is evaluated between the magnitude o impedance with (Z A)and without (ZNA) skin abrasion (no previous washing). d) Root mean square value (RMS) o the noise between twoelectrodes applied on the skin above our muscles (two measurements per muscle, requency range: 10500 Hz).All data collected at the Lab. or Eng. o the Neuromuscular System, Politecnico di Torino. Fig. 1d is reproducedrom Merletti and Parker, 2004.16

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(1)Due to the common mode input voltage, the

voltage at the output o the ront-end amplieris:

(2)

where ACM and Ad are the common mode and thedierential gains o the amplier. Te second termis oten predominant over the rst indicating thata high common mode rejection ratio (CMRR =Ad/

ACM) is a desirable but not sufcient condition toreduce common mode intererences.

Since the common mode voltage, mostly dueto the power line parasitic coupling Cp described

in Fig. 2a, may be o the order o a ew volts whilethe signal to be amplied may be 56 orders omagnitude smaller, the reduction o the undesired

voltage Vd (generated by VCM ) is very important. Aew strategies may be adopted to minimize Vd:

Decrease V1. CM by saely connecting the subject tothe power line ground with the Virtual Ground

FIGURE 2. a) Explanation o the dierential voltage generated by the VCM and by the imbalance o the Ze values

(see Fig.1b), Zp is the parasitic impedance between patient reerence and power line ground, b) Virtual ground

circuit which grounds the patient only or low currents; the current to ground is limited by the high R values. c)

Driven Right Leg circuit: the common mode voltage is ed back with reverse phase and reduced by a actor theo-

retically equal to the gain o the amplier. Z can be a small capacitor (10100 pF, or stability) or a resistor or may

be absent. d) Reduction o power line intererence by bootstrap o the isolated power supply. Z in+ and Zin

- are the

internal input impedances o the ampliers. The entire circuit foats at the common mode voltage and no common

mode current theoretically fows into Z in+ and Zin

-. An optical isolator must be used to separate the foating rom

the non-foating electronics. V+ and V- are the power supply voltages or the operational ampliers and are gener-

ated by an isolated DC-DC converter. See also Fig. 6 or additional congurations.

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(VG) circuit (which limits the leakage currentrom power line to ground to sae values) orby cancellation techniques such as the DrivenRight Leg Circuit (DRL), as described in Fig.

2b and c.Reduce the dierence (Z2. e1 - Ze2) by addingcompensating impedances in series with theelectrodes or by careully treating the skin toreduce the contact impedance.Select ront-end ampliers with very high Z3. i.Detect monopolar signals using a dierential4.technique reerred to as virtual reerenceelectrode.

Tese techniques are discussed in Sections II.Band III.

Skin treatments. he most important actoraecting the impedance o the electrode-gel-skininterace is the outermost skin layer: the epidermis.Many actors aect the impedance and noise o theelectrode-gel-skin interace58: skin treatment, therequency at which measurements are made, andelectrode surace, are the most important ones.Suchactors are not only relevant or the detection obioelectric signals but also or the detection o skin

voltage distributions resulting rom current injectionin electrode impedance tomography. Te literatureconcerning the impedance o the electrode-gel-skin

interace is very heterogeneous and contradictory.Many authors mentioned the value o such imped-ance without indicating electrode size and therequency at which measurements were made, sothat comparison between observations o dierentauthors is oten impossible. Proper skin prepara-tion is necessary to reduce the electrode-gel-skinimpedance, as well as the imbalance between theimpedances o two electrodes, by removing bodyoils and aky skin layers. Such preparation (washing

with soap, slight or strong abrasion, rubbing withconductive gel, ether or alcohol) is subjective and

may lead to dierent impedance values, dependingon the modality o application (Fig. 1b and 1c). Tenoise generated at the interace also depends on skintreatment (Fig. 1d).

In 2000, the European Project on Surace EMGor Non Invasive Assessment o Muscles (SENIAM)proposed a skin preparation protocol that consisted

o shaving, massaging with sandpaper or abrasivepaste and rinsing the skin with water to remove theabrasion aky residuals.9

Dierent skin treatments give dierent results.

Tis is demonstrated in Fig. 1b and Fig. 1c, wherethe impedance obtained testing dierent skin treat-ments with the same electrode size is shown.

Te tests demonstrate the inuence o skintreatments on the electrode-gel-skin impedance.Results suggest that the treatment recommended byHermens et al.9 (rubbing with abrasive gel and thencleaning with tap water) is the best among thosebeing tested. Contrary to common practice, rubbing

with alcohol or solvents leaves the skin dry and withhigh impedance. However, the degree o impedancereduction due to skin treatment is not consistent indierent individuals and or dierent experimenters.An electrode-skin impedance measuring device wouldbe a useul eature to incorporate into a HDsEMGsystem to automatically detect poor contacts.

Skin shaving and abrasion or spiked electrodes(see below) can cause micro-lesions. Tis has impli-cations concerning re-usable electrodes.

Electrode materials and gels. Silver or silver chlo-ride (Ag or AgCl) electrodes are widely used becauseo their reported low noise, stable and relativelyrequency-independent impedance.10

A Silver-Silver Chloride (Ag-AgCl) electrode isproduced by placing a silver electrode as the anode ina solution o NaCl. Te thickness o the AgCl layergenerated with this procedure depends on both thecurrent density and the charge passed. Te currentdensity is o the order o 1 mA/cm2 and the chargepassed is about 1 C/cm2.10,11

NASICON (Na super ionic conductor) ceramichas also been proposed12 as electrode material.Its general ormula is Na1+x Zr2 Six P3-x O12. For x= 2, the ionic resistivity is about 103cm at roomtemperature. For example, the resistance o a cylinder

with the diameter o 1cm and thickness o 2mmis lower than 1k.12,13 NASICON-type ceramicelectrode was tested without the application o anyelectrolyte between the electrode and the skin.12,13

Te impedance decreased as a unction o time oapplication, mainly in the resistive component. Tis

was explained by the perspiration process that occurs

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immediately with time ater the application o theNASICON-based electrode on the skin. It was alsodemonstrated that NaCl solution or skin abrasioncauses the resistance to decrease markedly.12

Dry disposable electrodes made o silver coatedsilicon (size 2x2 mm or 4x4 mm), with etched spikesthat pierce the stratum corneum to circumvent itshigh impedance and reach the stratum granulosum,have been proposed.14 Spikes are 0.15 mm apart and0.10.2 mm in length. A spiked pair o electrodes onthe skin present an impedance 1015 times lower thanthat o a similar pair o electrodes with at suraces.

Te greater contact surace o the spiked electrodereduces the contact impedance and should reducenoise as well as artiacts due to relative electrode-skinmovement. Results o studies on noise and artiactso these electrodes are not yet available.

Te electromechanical stability o the electrode-skin contact is an important issue in EMG detectionsince momentary loss o contact, or large changes ocontact impedance, induce artiacts in the detectedsignal. Tese events may be caused by accelerationand by the inertia o the wires pulling on the elec-trodes. Conductive gel should limit such artiactsby providing a exible link between the skin andthe electrode. Mechanical disturbance tests aimedto assess resistance to pulling, peeling, and sweating

have been described by Roy et al.15

Tese authorsound that the use o hydrophilic gels was associatedwith greater motion artiacts when the electrodewas mechanically perturbed. Additional researchis needed to optimize the adhesive used to x theelectrodes and the gel used to improve the contactquality and stability.

Most articles in this eld are ocused on the signalanalysis and interpretation and neglect to describethe electrode system and the skin treatment adopted.

Te SENIAM report9 analyzed 144 peer-reviewedpublications: in 57% the electrode material was not

mentioned, in 61% the shape and size o the electrodeswere not mentioned and in 62% the skin preparationwas not described. In 18 articles (13%) the electrode-skin impedance was measured and ound in the rangeo 16 k, but the measurement technique was notdescribed and the requency o the applied voltage/current was not always provided. Tese values are

compatible with large electrode suraces (order o1 cm2) but not with small ones (a ew mm2), usedin electrode arrays, as shown in Fig. 1b and 1c. Teissues o electrode-skin interace and the design o

optimal gels deserve urther investigation.

II.B. Detection and Conditioning of EMGFrom Eectrode Array Systems

Detecting signals on the skin with a number opoint-like electrodes means to sample the potentialdistribution in space. o meet Nyquist theorem,and avoid aliasing in space, the inter-electrodedistance (IED) must be smaller than a threshold

value. Considering propagating signals only, such

a value can be estimated rom the relationship s =t/v where s is the spatial requency (cycles/m), t isthe temporal requency (cycles/s or Hz) and v is thesignal propagation velocity. Considering urther thatthe highest temporal requency o sEMG is about400 Hz and the propagation velocity is near 4m/s thehighest spatial requency o interest is 100 cycles/mand the spatial sampling requency should be higherthan 200 samples/m, which means IED less than5 mm. Tis value is rarely met and in most applicationso electrode arrays IED is 8 mm or 10 mm, which

implies some aliasing in space. Te consequenceso this aliasing and the value o the highest spatialrequency o the nonpropagating components havenot been investigated. Individual electrode pairs usu-ally have an IED o 10 mm to 20 mm.

Signal detection techniques. Figure 3 depicts theevolution rom a single monopolar or bipolar (SD)EMG channel, detecting the signal in one skinlocation, to linear arrays and to HDsEMG whichdetect the image o the potential distribution overthe skin surace covered by the array. Te SD elec-trode montage, with electrodes aligned along the

ber direction, provides a high rejection o com-mon mode signals and allows easy identicationo innervation zones. Te double dierential signal(DD) is the dierence o SD signals rom adjacentchannels and is particularly suited or estimation omuscle ber conduction velocity. Both the SD andDD detection modes introduce spatial lters that

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are extensively described in many manuscripts andbooks.16 Tese spatial lters are desirable to attenuatenonpropagating components o either physiologicalor external origin, such as those due to end-o-bereects, remote sources and power line intererence,

which are present in the monopolar signals. Teyimply loss o inormation that may be relevant orcertain applications.

Te monopolar signals are usually read with

respect to a reerence provided by an electrode placedar rom the muscle o interest, oten on a bonyprominence where the EMG is presumably zero.

Tis method has drawbacks since the intererencevoltage present as common mode on the electrodeso the array may be dierent rom that present onthe reerence electrode. Tis voltage dierence is not

rejected by the dierential amplier and is consid-ered as signal. o overcome this problem, othermultichannel bioelectric signals (ECG, EEG) areread with respect to their own spatial average,1719

which is used as a virtual reerence. Tis techniqueis attractive but implies a orm o spatial ltering o

which the user must be aware. For this reason theVirtual Reerence technique is discussed below.

Detection o monopolar signals: Virtual Reer-ence technique. A method to reduce the power lineintererence rom monopolar EMG signals is thedetection o each channel with respect to the aver-age o all the detected signals, presumably aectedby the same intererence. Tis method is reerred toas Virtual Reerence (VR) and is based on the actthat the line integral o the EMG potential over itsentire support in the ber direction, as well as theintegral o the EMG on a surace that covers theentire spatial support o the potential generated bythe active sources, is zero.20 Tereore, such integral,estimated as the average o the electrode potentials,provides a value whose uctuations represent onlythe external common mode voltage. Tis averagecan be used as a reerence with respect to which themonopolar signals o the electrode array are measured.However, this approximation is valid only or anelectrode array covering the entire spatial support o

the signals generated by the active sources.2123

ForEEG applications this requirement is approximatelyullled by high density detection systems whichully cover the head surace. For practical EMGrecordings, this condition can rarely be achieved,thereby, the application o the VR method maylead to ltering eects and modiy the shape o themonopolar EMG signals.

Monopolar signals detected over a muscle by amulti-channel system aligned to the muscle bersinclude both propagating and nonpropagating com-ponents. Nonpropagating components are mainly

due to external intererences, to cross-talk and tothe potential generation (at the neuromuscular

junctions) and extinction (at the muscle-tendonjunctions, oten reerred as end-o-ber eect). Seealso Section III.D.

I the muscle and tendons are not entirely cov-ered by the detection system, the nonpropagating

FIGURE 3. a) Traditional monopolar detection with respectto a remote reerence taken as zero (reerence) potential.

b) Bipolar (or single dierential, SD) detection (or mon-tage) along the ber direction. c) Linear (one dimensional,1-D) array o electrodes along the ber direction. Spatiallters (such as double or N-dierential) can be obtainedby properly weighting and adding the signals rom nearbyelectrodes. d) 2-dimensional (2-D) array o electrodesproviding an image o potential distribution. Spatiallters (such as double or N-dierential in the columnor row direction, Laplacian, inverse binomial, etc.) maybe applied to the signals. Image processing proceduresmay be applied to the image to interpolate or virtuallyrotate the image to align it with the ber direction or todetect edges or areas o high or low activity or gradient(see Fig. 13).

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FIGURE 4. a) Schematic representation o the Virtual Reerence (VR) detection technique applied to channel 0(rst channel o a detection system constituted by Nelectrodes separated by a distance daligned with the ber

direction). The impulse response (hVR) and the transer unction (HVR) or the generic channel k are reported. Fora given time instant V(z) is the potential distribution in space and VVR

k is the signal detected rom channel k usingthe VR technique. V(z,t) is a potential distribution propagating mono-directionally in the z direction with a conduc-tion velocity v. b) Magnitude o the transer unction HVR o channel 10 (N=16, d=5mm) or dierent conductionvelocities (v) o the traveling potential V(z). c) Magnitude o the transer unction HVR o channel 8 (d= 5mm, v=4m/s) or two detection systems (N=8, N=32). d) Magnitude o the transer unction HVR or all the channels o a16-electrode detection system (N= 16, d= 5mm, v= 4m/s). |HVR|o channels 1 and 8 are highlighted, |HVR| o theremaining channels (rom 2 to 7 and rom 9 to 16) are reported in gray.

components do not have instantaneous zero spatialaverage. Since the VR method in non-ideal condi-tions implies a spatial lter, the properties o the

propagating and nonpropagating components areaected by the application o this technique.o clariy this eect, consider a detection system

constituted by N electrodes separated by a distance d,aligned with the ber direction and placed on one sideo the innervation zone (Fig. 4a). Te position o therst electrode corresponds to the zero o the z-axis.

Te EMG signal (V(z,t)) is a potential distribution, onthe surace o the skin above the muscle,propagatingin the z direction with a conduction velocityv. For any

given time instant to the voltage VVRdetected by electrodek, in position zk, using the VR method is:

(3)

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From Eq. (3) it appears that the application oVR technique can be described by a lter with spatialimpulse response:

(4)

Te transer unction in the spatial (Eq. 5) andtime requency (Eq. 6) domains is:

(5)

(6)

wheres isthe spatial angular requency, is the timeangular requency, and v is the conduction velocity oV(z,t) and s = / v. Note that all the poles on theimaginary axis are cancelled by zeros. From Eq. (6)it can be observed that the properties o the transer

unction depend on the ollowing parameters:a) conduction velocity (v) o the EMG signal

V(z): HVR= 0, or v . I the nonpropagatingcomponents (v ) o EMG signal are identicalon all channels they are removed by the VR method

while the propagating components are ltered (intime) according to their conduction velocity;

b) number o channels (N) o the detection sys-tem: HVR= or N . Te ltering eect othe VR method on the propagating components isreduced when the number o the detection channelsincreases. However, the nonpropagating components

are attenuated by the eect described in (a);c) requency () o V(z, t) and distance (d)

between two consecutive electrodes: |HVR| presentsa dip or = v / d;

d) the channel (k) to which the method is applied:the ltering eect is dierent or dierent channelso the detection system. Tis eect is reduced with

increasing the number o channels (N) as indicatedabove in b) .

Figure 4 (b,c,d) shows how |HVR()| is aectedby the conduction velocity (v) o the EMG signal,

the number o channels (N)o the detection system,and the channel (k) to which the VR method isapplied.

I the VR method is applied to a matrix oelectrodes, the eect o the transversal decay (withrespect to the propagation direction) o the suracepotential could lead to additional distortions o thepropagating and nonpropagating components o thesignals. I we consider a bi-dimensional detectionsystem with M columns aligned with the propaga-tion direction (z-axis) and N rows (aligned with the

x-axis), the potential distribution that propagates inthe z direction can be written as V(x,z,t) = a(x)* V(z,t)where a(x) is the unction that describes the decayo the potential in the transversal direction (x). Tesubtraction o the average signal rom each channelo the detection system leads to amplitude distor-tions. Tese distortions are due to the act that theamplitude o the average signal is not representativeo the signal amplitude detected by each channel othe matrix (ie, the average signal is multiplied bythe average o a(x), whereas the generic signal othe column i is multiplied by a(x

i

), where xi

is thex-coordinate o the ith column).

Te degree o the amplitude distortion dependson the transversal decay unction: the higher is thedecay rate, the higher is the degree o signal distortion.

Tereby, the surace potential generated by supercialmotor units (with a ast transversal decay) will bemore aected by the use o the VR method withrespect to the surace potential produced by deepersources (with slow transversal decay).

For ast transversal decays (supercial motorunits), the use o VR method could lead to wrong

estimations o the monopolar signal amplitude overthe columns o the matrix. Since the magnitude othe amplitude distortion depends on the columnposition, a consequent error could occur when aspatial lter involving dierent columns (such asLaplacian, IB2 or transversal SD) is applied to themonopolar signals reerred to the average or when

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the conduction velocity vector is not aligned withthe z-axis.

Another relevant limitation o the VR method isthat it is based on the assumption that the externalintererence (eg, power line intererence) aects allthe channels o the detection system with the sameamplitude and phase. I this hypothesis is not valid,the VR method will not be efcient in the reductiono external intererence rom all the channels o the

detection system. For example, i only one channelis aected, because o a poor contact, all the others

will be aected as well because the contributionwill be distributed to all channels by the averagedreerence.

Figure 5a reports an example o monopolar EMGsignals detected rom the biceps brachii muscle with

a linear array o 16 electrodes (10mm apart) alignedwith the ber direction during an isometric contrac-tion at 30% o the maximal voluntary contraction(MVC). A synthetic power line intererence signal(50 Hz plus harmonics) was added to the monopolarEMG signals. Te amplitude o the intererence signal

was linearly increased rom channel 1 to channel 16in order to obtain a non-uniorm distribution o the

intererence amplitude over the detection system.Te VR method was applied to the monopolarchannels o Fig. 5a thus obtaining the set o 16signals (VR monopolar signals) reported in Fig. 5b.Figure 5c and 5d show the power spectral density oone channel (number 7) rom the panels above. TeVR method was eective in reducing the power line

FIGURE 5. a) Example o 16 EMG channels detected with a linear array aligned with the bers on the bicepsbrachii muscle. Detection with respect to a remote reerence electrode (no VR). Articial power line intererence(50 Hz and our additional harmonics) was added with intensity increasing rom ch 1 to ch 16. b) spectrum o ch 7.c) signals rom a) measured with respect to the average o the signals in a) (VR). d) spectrum o ch. 7 o c). Note

the reduction o physiological common mode signals indicated by the smaller spectral area. Arbitrary units (a.u.)are the same in b) and d).

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intererence, however, residual spectral lines are stillevident at 50 Hz and its harmonics (Fig. 5d).

Tis is due to the act that the common mode(computed as the spatial average o all the signals

o the detection system) is not representative o theintererence that aects individual channels (since theamplitude o the intererence is not the same or eachchannel o the detection system) and its subtractionreduces the intererence dierently in dierent loca-tions. Tis eect could lead to substantial alterationso the good EMG signals when an artiact (eg, acontact problem) aects one or ew electrodes o thedetection system. A considerable reduction o thepower o EMG signal can be observed (see Fig. 5cand 5d). Tis reduction is due to the ltering eecton the physiological nonpropagating components othe monopolar EMG signal.

In conclusion, the VR method can be useul todetect monopolar EMG signals with multichanneltechniques when the power line intererence appearsas a true (identical) common mode component overthe entire detection system. Nevertheless, the lter-ing eect on both propagating and nonpropagatingcomponents and the high sensitivity to artiacts(due to poor contacts) represent relevant limita-tions that make the VR method not always preer-able with respect to the standard use o a reerence

electrode.Choosing the proper electrode location. Kinesio-logical applications o sEMG imply the simultane-ous study o sEMG detected with many electrodepairs, each applied on a specic muscle. Findingthe optimal location o these electrode pairs is animportant issue.

Assuming ideal geometry and homogeneous(not necessarily isotropic) tissue, dierential detec-tion rom a pair o electrodes placed along theber direction, symmetrically with respect to theinnervation zone (end-plate region) o a motor unit,

theoretically provides a near zero voltage.16 Manyusiorm muscles have bers parallel to the skin andinnervations o their motor units concentrated inone or two locations. In these cases the location o apair o electrodes is critical and should be optimizedby placing the electrodes between the innervationzone(s) and a muscle-tendon junction. Tis implies

the identication o the innervation zone(s), a taskwhich can be achieved by means o an electrode arrayand a multichannel EMG system2428 used beore theelectrode pairs are applied. In the uture an electrode

array will likely replace individual electrode pairs.Research is under way or automatic identicationo the innervation zone(s) and thereore automaticselection o the best electrode pair rom a linear orbi-dimensional array.29,30

Front-end amplifer systems. Figure 6a shows theideal acquisition system consisting o a high inputimpedance sample and hold (S/H) circuit, whichreads signals directly rom the electrodes, ollowed bya multiplexer and an amplier that matches the signalamplitude to the input range o the A/D converter.A 24-bit A/D converter would accommodate DCand power line intererence components that wouldbe later removed by sotware. Tis solution is not

yet easible today because the parasitic capacitancesbetween the digital controls and the analog chan-nels o the S/H and multiplexer circuits introduceartiacts in low level signals (charge injection phe-nomena). Front-end amplication stages are thereorerequired. An array o ampliers that can operate inthe monopolar or dierential mode is presented inFig. 6b. Te array o input voltage ollowers providesboth impedance buering and shield drive or the

electrode connections, but adds some noise.An array o non-inverting high input imped-ance ampliers is depicted in Fig. 6c. Tis systemmeasures the monopolar input signals with respectto their average and requires highly matched ratioso R2/R1 to guarantee identical gains in all channelsto allow the correct computation o dierential EMGby successive stages or by sotware. Each channel isamplied by (1+ R2/R1) and the subtracted averageis amplied by R2/R1. Tis implies choosing R2 >>R1. Te limitations o this conguration, similar tothe VR, have been discussed above.

Figure 6d shows an instrumentation amplierwith cancellation o the DC voltage (implementedby means o the eedback amplier A5) due to thehal-cell potential Vb (Fig. 1a). Alternatively, a unityDC gain o the rst stage can be obtained by addinga capacitor in series with the gain-controlling resistorR. In this way the AC components o the signals are

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amplied by the rst low-noise stage while the DCcomponent is not and possible saturation o the stageis avoided. A high-pass lter ater the dierentialstage will eliminate the residual DC component.

Te input impedances o these circuits are dueto the input resistance (usually > 1 G) and to theparallel input parasitic capacitance (usually Ci ~ 5 pF)

o the dierential amplier. In the sEMG requencyrange the capacitive component is the predominantactor. For example, or Ci = 5 pF and = 100 Hzthe input impedance magnitude is only 300 M.Considering that the electrode-skin impedancemay range rom 50 k to a ew M (Fig 1b,c), thephenomenon described in Fig 2a and in Eq. (1)

may introduce substantial components at the powerline requency and its harmonics despite the highcommon mode rejection ratio (CMRR) o the di-erential ampliers. Te circuit depicted in Fig. 2dprovides a bootstrap technique that strongly reducesthe eect o Ze1 Ze2, Zin+ and Zin-. Te circuits oFig. 2d and Fig. 6d may be combined to increase

perormance and power line rejection.Other approaches have been proposed to reduce

the eect o imbalance between Ze1 and Ze2. One isto introduce a xed resistor in series with one elec-trode and a voltage controlled resistor in series withthe other and automatically adjust it to minimizethe power line voltage component at the amplier

FIGURE 6. a) Ideal system with low level sample and hold and multiplexer and a single A/D converter. Devices oper-ating at such a low voltage range are not commercially available. Amplication is required beore A/D conversion.b) Acquisition system with guarded cables and either monopolar or dierential detection. The multiple electronicswitch S selects the monopolar or dierential option. c) Multichannel amplier using the average reerence system

(see text or details), d) Instrumentation amplier (ampliers A1, A2, A3) with guarded input, Driven Right Leg (DRL)circuit (amplier A4) and cancellation o DC output component by integrative eedback (amplier A5). The circuit ind) can be used to implement each dierential amplier depicted in b). In this case, the channel independent DRLcircuit depicted in Fig. 2c should be used.

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signal power (in addition to intererence power) inthe notch band, requirement or components withprecise value and low time and temperature drit.Alternative, quasi-on-line or o-line sotware optionsare described in Section III.B.

Figure 7 shows a ew examples o multichanneldetection systems. Many other examples exist, devel-oped in many laboratories, implemented on cloth,exible silicon rubber and other material. Te newtechniques o printed electronics are very promisingin this eld and will likely soon lead to the produc-tion o ink-jet printed electrode arrays.

II.C. Spatia Fiters and Cross-ta

Consider a sinusoidal potential distribution movingin one direction with constant velocity over a sur-ace. Such velocity (m/s), the wavelength (m), therequency in space s (cycles/m or m-1), the period

(s) and the requency in time t (cycles/s or s-1 orHz) are related by = / =t/s where =1/ s and

= 1/ t.A spatial lter provides a (linear or non-linear)

combination o the signals detected by a number opoint-like electrodes with the purpose o enhanc-ing or attenuating specic spatial requencies withrespect to others. For example, a pair o point-likeelectrodes spaced d millimeters apart, along the patho propagation and with weights +1 and -1, wouldreject propagating sine waves with spatial wavelengtho d/k meters and enhance spatial wavelengths o 2d/

(2k+1) meters where k is any positive integer.16A metal electrode with physical dimensions (eg,

a disk, a square or a bar, o surace area S) orcesequipotentiality on the skin within S at a valueapproximately equal to the potential average that

would be present under the same surace withoutthe electrode.34 Such an electrode is equivalent to

FIGURE 8. Spatial lters and their eects. a1), a2) and a3) Signals obtained simultaneously with the longitudinalsingle and double dierential techniques (SD and DD) and with the Normal DD (Laplacian) conguration. b1) Struc-ture o a 3x3 bidimensional spatial lter; R and C are the Row and Column indices. b2) and b3) Examples o 3x3spatial lters. b4) Concentric electrode lter. b5) Example o electrode arrangement according to an hexagonalstructure. Note that in cases b2) and b3) electrodes are aligned along our spatial directions, 45o apart, whereasin conguration b5) six directions can be identied 30o apart rom each other. In both cases the inter-electrodedistance is not the same in all directions. Both grids can be extended in space. Interpolation can provide additionalpixels and a ner grid.

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a spatial low pass lter whose impulse response ish(x,y) =1/S or x and y within S and h(x,y)=0 or xand y outside S. Dierential detection (in the direc-tion longitudinal or transversal with respect to the

ber direction) shown in Fig. 8 a1 approximates aspatial derivative and thereore reduces commonmode voltages o physiological (end o ber eect)or external origin (power line intererence). Morecomplex linear combinations o electrode signals,such as in Fig. 8 a2, a3, b2, b3, b4,35 provide high-pass spatial lters, thereore privileging the sharpsignals generated by near sources with respect tothe diused signals generated by remote sources osimilar intensity (Fig. 8 a1-a3). For a more detailedanalysis o the impulse responses and o the transerunctions o the spatial lters depicted in Fig 8a1a3,see chapter 7 o Merletti et al.16

It could be concluded that high-pass spatial lter-ing would reduce cross-talk because the decrease ospatially high-passed EMG intensity, with increasing(lateral or vertical) distance between electrodes andsources, is steeper with respect to monopolar detec-tion, as indicated in a number o publications.3639

Tis conclusion is not always correct since dierentspatial lters have dierent transer unctions withrespect to propagating and nonpropagating signalsand their response depends on the anatomy (thick-

ness and conductivity o subcutaneous and cutaneouslayers, ber length, etc.).38 At some distance rom thesources the contributions due to end-o-ber eectsbecome predominant with respect to the propagat-ing components because the latter decay in spacemore slowly than the rst. Cross-talk is mostly dueto nonpropagating components whose amplitudeshave a spatial gradient and are thereore dierentunder dierent electrodes. Its presence aects theestimates o muscle ber CV which thereore dependon the spatial lter adopted.40 Although dierentapproaches have been tried, such as spatial lters,40,41

removal o nonpropagating components,36 and blindsource separation,42 the issue o cross-talk is not yetsatisactorily solved.

Nonlinear spatial ilters perorm nonlinearoperations on signals obtained rom neighbor-ing electrodes in one or two dimensions. Oneamily o such ilters considers the dierence

between the energy o one channel and that othe neighbors in one, two or our directions.43

With reerence to Fig. 8b1 the lter output at thelocation (R,C), at each time instant t, is:

(7)

respectively, in the longitudinal or transversal direc-tion, or in the two orthogonal directions. In the worko Zhou43 the signal-to-noise ratio (SNR) and thekurtosis o the probability density unctions o signals

y were both higher than those obtainable with linearlters since peaks were greatly enhanced. Nonlinearltering could be used as a orm o pre-processingbeore extraction o motor unit discharges by win-dow comparators. Non-linear lters o higher ordershave not been investigated. For example, raising thesignal to power 3 would maintain its polarity andurther increase the separation among peaks havingdierent amplitudes thereore making it easier toseparate them using amplitude thresholds. However,

the eect o non linear ltering on superimposedMUAPs has not been investigated and should betreated with caution because o the creation o newsignal components in the requency domain.

III. ADVANCES IN SURFACE EMGPROCESSING AND DECOMPOSITIONAlGORIThMS

III.A. Removing Poer line InterferenceFrom EMG Signas

Some basic hardware methods to reduce power lineintererence have been described above (DrivenRight Leg, Virtual Ground, bootstrapping, balancingelectrode impedances, virtual reerence). Very otenthese techniques are insufcient because the interer-ence is very strong or dierent on dierent channels

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(see Fig. 2a and Fig. 5). Many sotware techniques,applicable on-line or o-line, are available rom theliterature and can be applied to a single signal or toan array o signals.

Notch flter and spectral interpolation. A numeri-cal narrow-band notch lter, centered on the powerline requency, may be applied to the EMG signal toremove the power line intererence. Tis techniqueproduces a dip into the EMG spectrum and intro-duces a phase rotation in neighboring harmonics thatmodies the shape o the signal components. Digitalanti-causal lters, applied o-line, can avoid the phaseshit. Spectral interpolation implies computationo the FF o the original signal, removal o thespectral lines at and near the power line requencyand replacement o such lines (in the amplitude andphase plots) with new lines obtained by polynomial

interpolation based on the lines beore and aterthe removed requency band. Inverse FF recon-structs the original signal without the intererencecomponents. Notch lters and spectral interpolationmethods may be applied at the 50 Hz or 60 Hz linerequency and at their harmonics. See Mewett et al.44or additional details. Both methods work well i theinterering requency is constant. Since the power linerequency and its harmonics undergo uctuations inthe range o 23%, adaptive methods perorm betterbecause they can track such uctuations.

Adaptive intererence cancellers. Tese cancellersreconstruct the intererence component and subtractit rom the original signal. o do this they need areerence signal. Since obtaining a reerence signalrom the power line may not be easy, such reerencemay be obtained rom the signal itsel by extracting its

FIGURE 9. Basic diagram o the adaptive intererence canceller. a) The sinusoidal intererence signal I(n) is esti-mated as y(n) = w1(n) r1(n) + w2(n) r2(n) and subtracted rom the input signal. The sine and cosine components r1(n)and r2(n) are obtained with recursive equations where c is updated by the phase inormation (n) obtained by thePhase Locked Loop (PLL). The weights w1(n) and w2(n) o each harmonic are optimized by the LMS algorithm tominimize the intererence contribution to the output e(n). b) Multiplication o the PLL output by the order o thedesired harmonic provides the same unction or the harmonics o the intererence. The system can be expandedor multichannel application by sharing the generation o the sine and cosine unctions and duplicating the LMSalgorithm or the estimation o a vector o coecients.

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values o each channel dene a point in the plots oFig. 11. Outliers are identied on a statistical base.Figure 11a shows one o the plots where the two

variables have been normalized, as indicated inGrondlund et al.,52 to have zero mean and onestandard deviation rom the mean. Figure 11bshows the same plot ater decorrelation by means

o robust Principal Component Analysis, that isthe variables have been rotated in the directiono maximum variance.

Considering each dimension independently,it is impossible to identiy the two outliers (lledcircles) rom the two Boxplots because the rela-tionship between the variables is not captured by

FIGURE 10. Interpolated ampli-tude map (RMS computed or

60-ms epoch) above the BicepsBrachii muscle during isometricelbow lexion at 10% MVCbeore (let) and ater (right)processing. The x-axis and y-axisare, respectively, array columnsand rows. The original rame hasbad-contact problems (R3-C4and R5-C3) that are identiedas described in the text.

FIGURE 11. a) The 2D distribution o the RMS data o 4 ms and 128 ms epochs (rst and second descriptor variable)o HDsEMG signal or the 49 channels used in Fig. 10. RMS values are scaled to zero mean and unity variance. Thetwo boxplots indicating the rst, second and third quartiles or each variable are shown in the gure and can notidentiy the two outliers (lled circle). b) The Robust Elliptic Plot (Relplot), a Bivariate extension o Boxplot ater de-correlating the two dimensions by means o Principal Component Analysis (PCA). The inner ellipse, called hinge,shows the bulk o the data while the outer ellipse, called ence, identies the possible outliers (lled circles). 51

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two univariate analysis. By un-correlating the twodescriptor variables and using the Relplot (RobustElliptic Plot), a Bivariate extension o Boxplot,51,52it is possible to identiy the two outliers (Fig. 11b).

With this approach,52 the characteristics o the 49monopolar EMG signals o Fig. 10a were describedusing the standard deviations o each signal estimatedduring long and short running windows. Tey wereused to orm 256 two-dimensional distributions othe descriptor variables. Ten, a Relplot (dened asour separate quarter ellipses used to capture theasymmetry o the RMS values o the short andlong epochs) was constructed and used to identiythe outliers in two-dimensional space. Te numbero times each signal was classied as outlier oreach epoch provides a signal quality index or everychannel and epoch.

Expert users can check the quality o the signalsand identiy the suitable EMG channels rom whichto extract the desired inormation. However, thisprocedure is quite time-consuming especially i thequality o the channels changes over time. A Mam-dani Fuzzy system was used to combine knowledgeo experts with robust statistics.53 A uzzy system

was used because it can be built on the experienceo experts while providing the capability o mergingdierent methods described by rules in conditions

that are inherently imprecisely dened. For moreinormation about the Fuzzy systems, readers canreer to Wang.54

In a recent work, Marateb55 investigated threeoutlier detection methods including distance, densityand distribution-based approaches and combinedthem with the Fuzzy rules obtained rom experts.53Fuzzy membership unctions used in the rules weretuned using Particle Swarm Optimization method.56

Te output o this Fuzzy system is the probabilitythat each HDsEMG channel is an outlier.

Results obtained using the Quelplot are shown

in Fig.10 beore and ater identiying the outliers andcorrecting the image by interpolation using PartialDierential Equation (PDE)-based image inpaintingmethod which preserves the continuation o levellines.57 Te modication o images in a way that isnon-detectable or an observer who does not knowthe original image is called retouching or inpainting.

However, the accuracy o this interpolation is highlydependent on the number o bad-channels presentin the interpolation area. When there are ew badchannels in the interpolation area (

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where is an invertible (square) matrix with dimen-sions N x N. Te last row imposes the condition ozero spatial mean. In such a case the monopolarsignals can be reconstructed rom the SD signals

as where . Since this operationgenerates monopolar signals that have instantaneouszero spatial means, the intererence that would likelybe present in the case o direct monopolar detectionis absent. I the electrode array covers only part othe spatial support o the signal, the last row o vec-tor and matrix cannot be included. Te matrixinversion required to estimate the monopolar signalsis replaced by the Moore-Penrose pseudo-inversion.It can be shown that the pseudo-inversion imposesthe condition o zero spatial average over the surace

or line covered by the electrodes.36

An experimentalcondition in which the entire signal support in theber direction is available is provided by the circulararray used or EMG detection rom the external analsphincter (EAS), described in the ollowing article inthis issue. In this case, the error between the observedand reconstructed monopolar signals can be used toindicate i the EAS bers are oriented according tothe electrode array or not.58

III.D. Separation of Propagating and

Nonpropagating Potentias

Te EMG signals detected by an array o electrodesaligned with the ber direction can be approxi-mated as a linear combination o propagating andnonpropagating components. Te nonpropagatingsignals may be due to generation (at the neuro-muscular junctions) and extinction eects (at themuscle-tendon junctions) o the motor unit actionpotentials, to the stimulation artiact in electricallyelicited contractions, and to common mode voltagessuch as power line intererence. Te propagating

components are the motor unit action potentialsthat are the summation o the potentials propagatingrom the neuromuscular junctions to muscle-tendon

junctions at a velocity o about 4 m/s (physiologicalrange o 36 m/s). Te main reasons or separatingthese two components are a) muscle ber conduction

velocity (CV) is an important physiological variable

whose estimate should be based on propagatingcomponents only (see Section III.E.), and b) thenonpropagating components provide inormationabout cross-talk and end-o-ber eect generated at

the muscle-tendon junctions. Te ollowing approachhas been proposed by Rubio-Vela59 and its resultsare described in Fig. 12.

Let us consider a set o three equally spacedsEMG detection systems detecting a componentV1(t) propagating with constant velocity, and anonpropagating component V0(t) (the method maybe applied to either monopolar or SD signals andcan be extended to more than three channels) asdescribed in Eq. (14).

(9)

where Ei(t) are the recorded signals, and the coe-cients aij (i=1,2; j=0,1) are the unknown elementso matrix A.

Let us initially dene an estimate or the delay

. Te Fourier transorm o the system o equationsdened by Eq. (9) can be written in matrix orm asshown below:

(10)

where is the angular requency and ^ indicatesFourier transormed signals. Te above equations

will be satised when the determinant o the 3x3

matrix is identically zero, which means

(11)

Equation (11) can be rewritten in the ollowingorm (useul or the implementation o an iterativesearch o the solution)

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(12)

Te weights wij are not known a priori. Eq. (12)can be written in matrix orm as ollows

(13)

and indicates Fourier transorm operator (identi-cal to previously used or convenience). Te optimal

solution o Eq. (13) in the least mean squares (LMS)sense is calculated by taking the Moore-Penrosepseudo-inverse o as ollows

(14)

where the symbols * and # indicate mean squareoptimal and Moore-Penrose pseudo-inverse.

FIGURE 12. Separation o propagating rom nonpropagating components o sEMG signals detected using a lin-ear electrode array with inter-electrode distance o 5 mm and electrode diameter o 1 mm. a) Three monopolarsEMG channels showing a motor unit action potential during a voluntary contraction o a biceps brachii muscle, b)

Propagating component and, c) Nonpropagating component, due to the generation and extinction phenomena.d) Three monopolar sEMG channels showing a compound action potential (M-wave) during an electrically elicitedcontraction o a biceps brachii muscle. e) Propagating component and, ) Nonpropagating component. The latteris mostly due to the stimulation artiact. Redrawn rom Mesin et al.36

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Once the ve weights wij are obtained, theour unknown coefcients o the matrix can beobtained by inverting the denitions o the weightswij in terms o the elements aij o the matrix ,

written in the ollowing matrix orm

(15)

From Eq. (15), the unknown elements aij o thematrix are computed optimally in the LMS senseby pseudo-inverting the matrix .

Once the coefcient matrix is obtained, thepropagating and nonpropagating components areestimated by pseudo-inverting the matrix andapplying it to the data (ie, pseudo inverting Eq. 9).

Te delay can be initially estimated using doubledierential signals or multichannel estimation asrequired or the assessment o muscle ber conduc-tion velocity (Section III.E.) and then rened byminimization o the error between the experimentaland the reconstructed signals.

Tis algorithm is sensitive to noise and signalshape perturbations in dierent channels (that is

Vo(t) and V1(t) are not the same on all channels).An improved version has been proposed by Mesin36who considered a regularization term and reportedexamples o applications and results. Te technique isalso suitable or removal o intererences that appearas nonpropagating signals, regardless o their wave-orm, such as stimulation artiacts, as indicated inFig. 12d,e,. A comparison o the various techniquesor removing intererences and/or separating compo-nents that are not propagating rom those that are(possibly resolving dierent propagation velocities)is missing and long due.

III.E. Estimation of Musce FiberConduction Veocity

Muscle iber conduction velocity (CV) is animportant physiological variable because it reects

muscle atigue and ber size. Although it can beestimated rom a single SD signal obtained rom apair o electrodes, using the dips introduced by theSD spatial lter, the estimates obtained rom linear

electrode arrays have much smaller error.6065 I themuscle bers are parallel to the skin and at least twodetection sites are available in the ber directionabove a region o unidirectional propagation, CVis estimated as d/ where d is the distance betweenthe detection sites and is the delay between thetwo similar signals. Similarity is quantied by thecross-correlation coefcient between the two signals(values >0.8 are considered acceptable). A review othe classical methods or CV estimation is providedin Farina and Merletti.60

Te delay that maximizes the cross-correlationunction between two signals is the same that mini-mizes the mean square error between the same signals.

Tis property is exploited in multichannel systemsto nd the delay whose integer multiples (1, 2, N) minimize the global mean square error (MSE),

which is obtained by adding the MSEs o all possiblechannel pairs. Under the conditions o monodirec-tional propagation under the array and absence ostationary waves (nonpropagating components, seeSection III.D.), increasing the number o channelsreduces the estimation error.61

HDsEMG allows the application o new tools orestimates o CV and innervation zone location basedon imaging processing techniques.62,63 An extensiveliterature exists on muscle ber CV estimation andthe interested reader is reerred to it. 2,60,61,6466

III.F. Segmentation of Surface EMG Maps

Sequences o HDsEMG images are oten used torepresent regional variations in the degree o muscleactivation with time.6769 Visualization o these

sequences provides the investigator with immediateinormation o localized muscle activation. Indeed,as individual portions o the same muscle might beactivated selectively, HDsEMG imaging provides apromising tool or ascertaining when and where in themuscle its motor units are activated. Te automaticidentication o localized muscle activity, or example,

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could be useul or the estimation o the total muscleorce, the onset o muscle activity and the degree oactivation o dierent synergists spanning the same

joint (see Section III.I.). Te watershed technique,originally developed or the automatic segmentationo digital images,70 proved to be accurate or theidentication o localized muscle activity in mapso HDsEMG.69

Tis section summarizes how HDsEMG imagesshould be processed beore applying the watershedalgorithm, which was developed by Vincent andSoile70 or the automatic segmentation o digitalimages. Initially, consider the conceptual bases on

which the segmentation algorithm is conceived. IHDsEMG images are represented as topographicalrelies, the pixels with high and low intensity (ie, highand low sEMG amplitude) would appear as elevated

and depressed suraces, respectively, as indicated inFig. 13a. By piercing such a surace in its regionalminima and immersing the whole surace in water,the depressed suraces would start to ood and ormcatchment basins. Ater progressive immersion, thecatchment basins would be surrounded by the narrowand elevated suraces, the watershed lines. Tereore,the watershed algorithm could be applied to theimages o gradient o HDsEMG images, wherepixels o high intensity (ie, watershed lines o thegradient) conne groups o pixels with low intensity(ie, catchment basins o the gradient). For a detaileddescription o iteration steps o the watershed algo-rithm see Vincent and Soile.70

Considering that pixels in the EMG imagesample the amplitude o EMG signals in space (ie,ARV or RMS values), the edges o subsets with low

FIGURE 13. a) Topographical representation o an image created with the ARV amplitudes o experimental EMGsignals (epoch o 250 ms); b) the gradient o the EMG relie shown in (a), c) the opened-closed gradient, obtainedby ltering the gradient in (b) with equations 20 and 21, d) watershed segmentation o the fattened gradient shownin (c). Note that clusters in (d) enclose groups o pixels with similar intensities in (a).

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and high EMG activity can be identied by comput-ing the Euclidean normgemg o the gradients o theimage rames Iemg (Fig. 13b):

(16)

where indicates the transpose operator,px and pydenote the coordinate o each pixel in the EMGimage and i corresponds to the time epoch over

which Iemg was computed.Nr and Nc stand or thenumber o elements along the rows and columns othe Sobel operator s:

(17)

Te Sobel mask is preerred over the conventionaldierential operator because it computes the imagegradient by weighting the intensity o neighbor pixels.In any case, the gradient operation emphasizes thenoise content in the image and thus leads to theappearance o several regional minima, as indicated inFig. 13b. For this reason,gemgmust be attened beorethe application o the watershed algorithm. Other-

wise, the occurrence o spurious regional minimawould result in the over-segmentation ogemg.

Image opening and image closing, or example,are nonlinear operators oten used or the lteringo digital images. Tese operators are attractive andpreerred to the use o conventional linear ltersbecause they atten (eg, remove narrow peaks andgaps) rather than smooth the images.71 Qualitatively,opening and closing attenuate and intensiy the

intensity o pixels with intensity values exceedingor not reaching some threshold, respectively. Teopening and closing operations applied to gemg aredened as:

(18)

(19)

where and are the Minkowski addition andMinkowski dierence operators, respectively. Testructuring element v is usually composed o a mask

with zeros and ones when processing binary images.71In such a case, the opening and closing operationsresume to the combination o image erosion ol-lowed by image dilation and vice versa, respectively.

Te dilation and erosion operations determine theintensity o any given pixel in the output image byapplying a predened rule to the corresponding pixeland to its local neighbors in the input image. Gray-scale image dilation sets the intensity o each outputpixel to the highest intensity among all pixels in theneighborhood, whereas gray-scale image erosion setsthe intensity o the output pixel to the minimum

intensity o all the pixels in the neighborhood:

(20)

(21)

where Dvis a square grid with nine elements, so thatthe structuring element v corresponds to a mask withzeros disposed into a 3x3 grid.px andpy denote theposition o the pixel in the input and output image,

respectively, and zx and zy are the relative positionso the pixel in the structuring element v. In our case,the size o the input image gemg is dened by thenumber and disposition o surace electrodes in thegrid, while image resolution can be urther increasedby various interpolation techniques.

Te opened-closed gradient oIemg provides aattened surace or applying the watershed algorithm(Fig. 13c and d). Indeed, the segmentation o such asmoothed gradient results in accurate identicationo clusters o high and low EMG activity.69

Figure 13 illustrates how clusters o EMG

activity are automatically obtained with the water-shed algorithm applied to the smoothed gradiento one HDsEMG map. In this case, three clusterso localized activity were identied, with cluster 3being the area with highest neuromuscular activity.

When a matrix o surace electrodes is used or themonitoring o muscle activation, any regional varia-

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tion in activity might be perceived. On this view, thewatershed segmentation showed remarkable potenti-ality or distinguishing the localized activity betweentwo portions o the same muscle and between twoadjacent muscles.69

Other techniques or the processing oHDsEMG images could be applied to urtherexploit the use o the watershed algorithm or theautomatic identication o local variations in muscleactivation. Te use o global histogram equalization,72

or example, increases the contrast between pixelswith low and high intensities and thus increases thesegmentation accuracy o HDsEMG images detectedor muscle contractions at low eort levels.69

Some properties o the watershed algorithmrender its use attractive and preerred with respectto conventional clustering techniques or the auto-

matic segmentation o EMG maps because 1) theidentication o clusters o activity does not rely onthe minimization o any distance measure and is notsupervised; 2) initial guess about the actual number oclusters in the image is not required; 3) the accuracyis marginally sensitive to noise level, thickness o atlayer and acquisition conguration; 4) isolated pixels

with high intensity, likely resulting rom contactproblems between the skin and the electrode, wouldbe removed by the gradient attening.7072

III.G. Decomposition of MuticanneSurface EMG Into te Constituent Trains ofMotor Unit Action Potentias

Te decomposition o multichannel sEMG consistso identiying either MUAPs or discharge patterns

FIGURE 14. Principle o decomposition o the multichannel surace EMG into the constituent motor unit actionpotential (MUAP) trains. The algorithm identies the times o occurrence o repeating events (MUAPs) that are theinstants o discharge o the individual motor neurons innervating the muscle bers closer to the skin. MUAPs containinormation on muscle anatomy (length o bers, location o innervation zones, conduction velocity o action poten-tials along the muscle bers, etc.). MU discharge patterns refect control strategies o central nervous system.

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o MUs (Figs. 14 and 15). MUAPs contain inorma-tion on muscle anatomy (length o bers, locationo innervation zones, conduction velocity o actionpotentials along the muscle bers, etc.), whereasthe discharge patterns reect the control strategiesadopted by the central nervous system (CNS). Tis

inormation is crucial or advanced research con-cerning neuromuscular pathologies, work-relateddisorders, myoelectrical maniestations o atigue,ber typing, etc. It is also crucial or preventiono numerous neuromuscular diseases (eg, myalgia,repetitive strain injuries, etc.) and or assessmentso eectiveness in motor rehabilitation.

In contrast to intramuscular EMG, where thehigh-selectivity o the detection system enablesrecordings rom nearby muscle bers only, suraceelectrodes exhibit much lower selectivity and acquirethe contributions o MUs within the distance o up

to a ew centimeters (Fig. 17). In addition, skin andadipose tissue that separate the active bers rom thedetection sites act like a low-pass lter, reducing therequency bandwidth o sEMG below 500 Hz. Boththe aorementioned actors hinder sEMG decom-position as they attenuate morphological dierencesbetween MUAPs at the surace o the skin.

Surace EMG decomposition received remark-able attention over the past ew years and has recentlybecome easible under the constraint o controlledisometric conditions and recording o sEMG romseveral locations over the muscle.7375,8792 Dierentalgorithms proposed can generally be categorized into

two large amilies: blind source separation (BSS) andtemplate matching. Tis section reviews both amilies,but ocuses particularly on BSS approaches.

Template matching approaches. Although theclassic template matching algorithms, developedor decomposition o intramuscular EMG, provedunreliable when applied to sEMG, their multichannelextensions provided solid results on relatively sparsesEMG signals, with limited number o MUAPsuperimpositions.8791 However, these algorithms aretypically incapable o dealing with MUAP super-impositions and ail to identiy complete discharge

patterns o detected MUs.For example, an early approach to multichannel

sEMG decomposition was proposed by Gazzoni etal.88 Tis approach consisted o the ollowing steps:a) segmentation o signals rom a 1-D or 2-D arrayusing a multi-scale matched lter (that is a continu-ous wavelet transorm); b) identication o MUAPs

FIGURE 15. Decomposition

o the HDsEMG obtainedrom a healthy abductor pol-licis brevis muscle during itsisometric contraction, rom0 to 10% MVC and back to0% in 11 s. Each dot repre-sents a discharge o a motorunit with the instantaneousdischarge requency (inverseo the interspike interval)indicated on the y-axis. Theorder o motor unit recruit-ment and decruitment canbe seen, associated with the

produced muscle orce.

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presenting propagation, in either direction, within aphysiological range; c) classication by means o theMultichannel Adaptive Resonant Teory (MAR)

which groups the propagating MUAPs into clusters

(templates) assigned to individual MUs. Since super-impositions are assigned to separate clusters (usuallyo one element each) and not solved, the dischargerates cannot be estimated. However the extractedMUAP templates provide inormation on the loca-tion, anatomy and CV o individual MUs.

A new generation o template matching algo-rithms is being developed.92 Tese algorithms deriverom estimation theory93 and build on strong proba-bilistic priors, such as the regularity o interspikeintervals, to enhance template recognition on areduced set o surace electrodes.

Blind source separation approaches. Te blindsource separation (BSS) approach assumes a linearmixing model o the sources either with no memory(instantaneous mixing o sources) or with nitememory (convolutive mixing o the sources). In orderto reproduce experimental conditions, a zero-meannoise is usually added to either o the models. ypi-cally, the noise is assumed to be spatially uncorrelatedand independent o the source signals.

In the instantaneous model, the M mixturesx(t)=[x1(t), x2(t),, xM(t)]T, detected in a time instant

t are obtained as a linear combinations o the Nsource signals s(t) =[s1(t), s2(t),, sN(t)]Tat the sametime instant:

(22)whereA() denes the mixing matrix in dependenceo geometrical relation between the investigatedmuscle bers and the up-taking electrodes. In iso-metric muscle contraction and with surace electrodesxed to the skin, (and thus A) can be consideredconstant, whereas in dynamic conditions changes asa unction o the joint angle and other variables.

he instantaneous model assumes that theobserved mixtures are instantaneous weighted sumso the source signals. Tis oversimplication ignoresmany anatomical characteristics, such as MUAPpropagation along the muscle bers, presence oinnervation or muscle-tendon zone, heterogeneityo volume conductor that acts like a low-pass lter,and muscle ber orientation, etc.

Convolutive mixing model. Zazula et al.76 and Liet al.77 modeled the mixingprocess by a causal mul-tidimensional convolution that utilizes the memoryo the source samples received in the past using

(23)wheredenotes the time lag. Tis modeling approachdoes not assume that the shapes o MUAPs o thesame MU are similar in dierent EMG channels.Even more, by assuming, as in Fig.14, the sourcess(t) as series o Delta unctions representing neuralrings rather than MUAPs, that is as sequences ozerosand oneswith each oneindicating the dischargeo a given MU and zero indicating no discharge, themixing matrix Adirectly comprises all the detectedMUAP shapes:

(24)

with aij(,) denoting the-th sample of the MUAP

of the j-th MU as detected by the i-th electrode.

This model is able to account for the anatomical

characteristics of a muscle (being fusiform, annular

or pennate), ltering effect of adipose tissue, and

any property of the up-taking electrodes or of the

spatial lter used.Despite its limitations, the instantaneous model

has been requently addressed,7880 mainly due to itssimplicity and availability o algorithms or blindseparation o instantaneous mixtures. However, thereported results o instantaneous multichannel sEMGdecomposition were o limited success (see Teisand Garcia78 or comparison o dierent instanta-neous BSS algorithms on the same set o sEMGrecordings). Te convolutive model with impulsivesources, described above, was addressed by Holobarand Zazula, who introduced the Convolution Kernel

Compensation (CKC) algorithm.73,74 Tis algo-rithm is ully automatic, implicitly resolves MUAPsuperimpositions, and relies minimally on anatomicproperties o the investigated muscle. It extractscomplete discharge sequences o MUAPs rom theintererential signal (Fig. 14), whereas the MUAPshapes are estimated by spike triggered averaging o

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the EMG signal. By typically requiring a ew minuteso computation time on a standard personal computerto decompose one minute long signals, it is suitableor o-line processing and mainly applicable to basicand applied research in neurophysiology. Over thepast ew years, the algorithm has been extensivelytested in a variety o experimental conditions duringisometric contractions o healthy muscles with di-erent anatomical properties, ranging rom usiorm,

trapezoidal, pennate to annular muscles (Fig. 16). Inthese tests, it identied complete discharge patternso up to 25 concurrently active motor units, morethan any other existing sEMG decomposition tech-nique.81 It is, however, noteworthy that the numbero motor units identied with the decomposition omultichannel sEMG varies notably across subjects,

muscles, and experimental conditions. For example,the range o individual motor units detected rom theabductor digiti minimi, tibialis anterior, and bicepsbrachii muscles during static contractions at 20%MVC orce ranged rom one to nineteen.81,82

Te validation o this and other HDsEMGdecomposition techniques, that is the quantitativeassessment o their accuracy, addresses the importantissue o a gold standard in EMG decomposition.

Most investigators are currently using the needleEMG as such a standard (see Section III.H.), but ageneral consensus has not been reached yet.

Te main drawback o the BSS approach is thenumber o observations M, which must be largerthan the number o sources N. Recently, separationo sparse time series has gained considerable atten-

FIGURE 16. a) Discharge patterns o motor units identied rom HDsEMG o biceps brachii during an isometric con-traction at 10% MVC and MUAPs o MU 4 as detected by the array o 513 electrodes with the corner electrodesmissing; b) discharge patterns o motor units identied rom HDsEMG o gastrocnemius medialis during isometriccontraction at 40% MVC and MUAP shapes o MU 6 as detected by the array o 88 electrodes; c) discharge pat-terns o motor units identied rom HDsEMG o external anal sphincter during its 100% MVC contraction and MUAPshapes o MU 1 as detected by the intra-anal probe with 163 electrodes. All the HDsEMG channels were acquired

in single-dierential montage. Each dot represents a discharge o a motor unit with the instantaneous dischargerequency (inverse o the interspike interval) indicated on the y-axis.

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tion.8386 By assuming the source signals s(t) have asparse representation on a given basis, the proposedmethods utilize maximum likelihood (ML) estima-tors in order to iteratively learn both the mixingmatrix and the source signals out o the observeddata even in underdetermined mixing models (ie,

with more sources than observations). Nonetheless,the number o required sEMG observations remainedrelatively high, implying the use o HDsEMG andmulti-channel ampliers to record rom up to a ewtens o sEMG signals rom a single muscle.

Te issue o the required number o sEMGchannels is still perplexing and requires additionalexplanation. It is well known that low selectivityo surace electrodes and the low-pass ltering osubcutaneous tissue attenuate morphological dier-ences between MUAPs at the surace o the skin,

thus inducing the need or multichannel recordings(Fig. 17), but the optimal number o electrodes andtheir conguration is still under investigation. Asor intramuscular EMG, the analysis o individualmotor units rom the sEMG requires the identi-cation and classication o the action potentialssignicantly contributing to the signal. Tis taskis possible only i the motor units are uniquelyrepresented by their action potentials, regardless othe algorithm used. With decreased delity in timesupport (when compared to intramuscular EMG),sEMG detection must increase the spatial support toguarantee that the MUAPs o dierent motor unitsdier rom each other. For example, in the studyby Farina et al.,75 based on simulated signals, onebipolar recording allowed the discrimination o lessthan 5% o the motor units active in the muscle tis-

FIGURE 17. Acquisition o surace and intramuscular EMG: the high selectivity o intramuscular electrodes enablesthe acquisition o high-delity signals with contributions rom a limited number o motor units. Surace electrodesare located at a much larger distance rom muscle bers and exhibits much lower selectivity than intramuscularelectrodes. This attenuates morphological dierences between MUAPs o dierent MUs. Thus, increased spatialsupport (ie the number o electrodes) o acquired sEMG is required to reliably discriminate dierent MUs.

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Merletti et al.334

sue, whereas more than 80% o detected motor unitshad unique surace representation when a system o55 electrodes with 3-mm inter-electrode distance

was used. Recently, extreme high-pass ltering o

sEMG signals (with cut-o requency set to 300Hz or above) was proposed to increase the MUAPdiscriminating properties o template matchingalgorithms.92 However, these algorithms are currentlystill under experimental validation.

Limitations and constraints o multichannel sEMGdecomposition.Decomposition o EMG (intramuscu-lar or surace, based on BSS or template matching) iscurrently still limited to moderate contraction levels(up to ~50% o maximum voluntary contraction). Athigher contraction levels, both the number o activeMUs in the detection volume and their dischargerates increase signicantly. Tis typically increases thephysiological noise rom small and distant motor unitsand considerably increases the complexity o EMGintererence pattern. Dierent signal preconditioningtechniques have been proposed to counteract the lowselectivity o sEMG up-taking electrodes. As one omost commonly used countermeasures, spatial lter-ing has been adopted to reduce the number o MUssignicantly contributing to the sEMG signals.9496

wo-dimensional lters, such as the Laplacian (Fig.8a3), have been shown to substantially improve

selectivity with respect to one-dimensional lters97

and individual motor unit action potentials can beextracted rom surace recordings by highly selectivespatial lters, even at maximal contraction orces.98

Te second main limitation o multichannelsEMG decomposition originates in the numbero identied MUs. Even with as many as 2025identied MUs per muscle, the proportion o motorunits identied rom sEMG is relatively small whencompared to the number o active motor units. Alsonoteworthy, sEMG typically identies large andsupercial motor units, with the average depth up to

~ 1 cm in the muscle tissue.81 Tereore, generaliza-tion o the sEMG decomposition results to the wholemuscle is only possible i motor units with dierentproperties (size, type, etc.) are considered to haveuniorm distribution within the muscle cross-section,so that supercial region o such a cross-section isrepresentative o the rest. Te problem o represen-

tativeness o identied MUs can partially be solvedby simultaneously acquiring and decomposing bothsurace and intramuscular EMG,99 as discussed inSection III.H. However, despite recent progress, no

technique or complete in vivo identication o allMUs active in the muscle tissue is available, yet.

Finally, multichannel sEMG decomposition iscurrently still limited to isometric muscle contrac-tions, ie, contractions in which the geometrical rela-tion between the investigated muscle and up-takingelectrodes is xed in time. Slow changes o MUAPshapes, such as during atiguing contractions, canreadily be tracked and compensated by most othe existing decomposition techniques, however,decomposition o sEMG rom patients with large

variations o MUAP shapes due to certain neuromus-cular diseases can be problematic.Te decompositiono EMG during natural muscle movements (ie, indynamic conditions) is a much more difcult prob-lem. During dynamic muscle contractions, distancesbetween the detection system and the active MUschange continuously as a unction o time (or, moreprecisely, as a unction o joint angle). Tis causescontinuous, but substantial changes in the shape odetected MUAPs hindering the EMG decomposi-tion. In ast contractions, the mixing matrix A(,)rom model (Eq. 24) may substantially change within

a ew tens o milliseconds, that is, within the lengtho the average interspike interval. Tese dynamics cancurrently neither be modeled nor tracked by EMGtechniques. No attempt to decompose the dynamicEMG signals, even those recorded during very mod-erate dynamic contractions, has been published.

III.h. Simutaneous Acuisition ofIntramuscuar and Muticanne sEMG

Te number o MUs identied rom sEMG is cur-

rently limited to approximately 30, with a typicalrange rom 10 to 20 MUs per contraction. Tisnumber is relatively small when compared to the totalnumber o motor units active in the skeletal muscle.

When a larger number o MUs is required, multi-channel sEMG must be recorded and decomposedsimultaneously with intramuscular recordings.82 As

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already mentioned, with respect to the intramuscularEMG, the multichannel sEMG is limited to super-cial MUs and samples rom a much larger spatialsupport (Fig. 17). Tereore, it is very unlikely that

both surace and intramuscular acquisition techniquesdetect exactly the same subset o active MUs.

Decomposition o simultaneously acquiredintramuscular and multichannel sEMG has beenused or validation o sEMG decomposition.82,92 Terationale or this is two-old. First, many experimen-tally validated decomposition techniques exist orintramuscular EMG and they have been consideredreliable and accurate or several decades.100 Second,the agreement o two independent decompositiontechniques applied to two dierent sets o simul-

taneously acquired EMG recordings is commonlyconsidered a direct indication o accuracy since itis very unlikely that the same errors are made byboth methods.101

Te joint recording o intramuscular and sEMGsignals is also useul to simultaneously investigateperipheral and central control properties o the neu-romuscular system as it likely increases the numbero identied MUs.82 ypically, the discharge timesidentied rom the decomposition o intramuscularEMG signals are used to trigger the time-lockedaveraging o the sEMG signal and thus to extract

and estimate the surace representation o the singleunit action potentials that cannot be reliably identiedrom the sEMG. From the surace action potentials,motor unit properties, such as muscle ber conduction

velocity, location o innervation zone and muscle berlength and orientation can be estimated,39 thoughthe proposed estimators have mainly been limitedto usiorm muscles. Spike-triggered averaging isalso applied to the joint torque or estimating thetorque contribution o individual units,102 althoughthe approach has several limitations.103

III.I. EMG-Force Modes and Modes ofload Saring

Te possibility o estimating muscle orce rom thesEMG signal is very attractive since it might assessthe contributions o single muscles to the total orce

exerted by a muscle group. However, assessing therelationship between the orc

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