Multi Channel Surface EMG

Detection and Conditioning

M. Gazzoni and U. Barone

Laboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronics and Telecommunications,

Politecnico di Torino, Torino, Italy

Keywords: Multi-channel Surface EMG, Spatial Filtering, Spatial Sampling.

Abstract: The electromyogram is a compound signal comprising the electrical activity of the motor units activated

asynchronously during voluntary muscle contractions. The temporal and spatial evolution of EMG can be

sampled by surface electrodes. The basic principles and concepts about sEMG signal conditioning, spatial

filtering, and spatial sampling are introduced and discussed.

1 INTRODUCTION

The electromyogram (EMG) is a compound signal

comprising the electrical activity of the motor units

(MU) activated asynchronously during voluntary

muscle contractions. The summation of action

potentials of active MUs generates, on the skin

surface, an electrical field; the surface EMG

(sEMG). Temporal and spatial evolution of this field

might be sampled by surface electrodes

appropriately positioned above active muscle

regions. The properties of the detection system as

well as the characteristics of the circuits for the

conditioning of sEMG influence its quality and

informative content.

2 sEMG CONDITIONING

The electrode skin interface properties change

continuously due to its sensitivity to environmental,

chemical and mechanical factors which affect

electrical properties. A collection of common

disturbing events and unpredictable interfering

signals are: a) motion artifacts; b) mechanical

vibrations of cables with consequent variation of

capacitance, electric charges, and voltage drop; c)

power line interference coupling; d) ground loops; e)

fluctuations of electrode polarization; f) charge

distribution variability on skin layers;

Mechanical solutions and fabrication methods

have been studied to design surface electrodes with

low polarizable level and low noise floor within

EMG frequency bandwidth.

Several articles and technical notes about bio-

potential circuit implementations (AFE, Analog

Front End) were published. Different approaches

and solutions were presented to properly detect bio-

potentials characterized by low amplitude (order of

microvolt), extremely low frequency band (under

1kHz), high DC offset (up to ±0.5VDC) and low

SNR ([5dB − 35dB]). General design criteria were

published for bioelectric data acquisition systems

(R.R. Harrison, 2007), (Bernhard Fuchs, 2002).

System-on-Chip based approaches were also

proposed (N.V. Helleputte, 2008) for advanced

biomedical applications such as miniaturized and

implantable sensing amplifiers (Wang 2006, R.R.

Harrison 2003, T. Denison 2007, R. Rieger 2006)

and wearable electronic sensors (L. Yan 2009).

Table 1 reports the main properties of typical surface

EMG signal.

Table 1: EMG Signal characteristics for a typical detection

system based on Ag/AgCl electrode with wet conductive

gel.

Property Value

Min Max

EMG bandwidth (BWEMG) 20Hz 500Hz

EMG Peak Amplitude 100μV 5mV

Total RMS Noise Voltage

(EMG)

10μVRMS

119

Gazzoni M. and Barone U..

Multi Channel Surface EMG - Detection and Conditioning.

DOI: 10.5220/0004663701190125

In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics (DeNeuro-2013), pages 119-125

ISBN: 978-989-8565-80-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

ADC

NBI T

12 





NBIT

FSR

LSB

kLSBS

FSR: Full Scale Range

S/H

AFE

Impedance

Matching

Active

circuit

(optional)

EMG channel

Reference

Electrode

EMG channel

skin

Conductive gel

electrode

EMG



COMMON MODE FEEDBA CK

HOLD

DC offset

Re moval

circuit

INA

chB

AFE

Anti-aliasing Fi lter

chA

chB

>500Hz

EMG

AFE

EMG

= G

AFE

·(V

chA

–V

chB

)

AFE

>> Z

EMG

within [20Hz-500 Hz]

Figure 1: Summary of one channel EMG amplifier chain until the digitizing process. Three main blocks describe the full

signal conditioning flow. The dotted blocks are optional and point out to specific improvement of the amplifier. The

impedance matching circuit minimizes the mismatch between the equivalent impedances of the electrode-gel-skin contacts

in order to reduce the false differential voltage detection (voltage divider effect). An example of Analog front End amplifier

(AFE) implementation is reported by functional blocks. Specifically, a monolithic Instrumentation amplifier (INA)

performs the differential signal extraction and amplification (VEMG=Gain·[VchA-VchB]). The common mode trend Vcm

of the input signals is rejected the common mode component Vcm principally caused by power line interference coupling

and motion artefacts. Specifically, low noise amplifier chain (AFE) and high resolution digitizing process (ADC) are strictly

required to design a high resolution EMG detection system according to EMG signal characteristics and Electrode-Gel-Skin

properties.

The most important building block for EMG

recording is the signal conditioning chain (see

Figure

1). Integrated circuits selection, configuration and

dimensioning should be performed in order to

maximize the signal quality until the digitizing

process.

The primary aspects to handle during EMG

amplifier design are:

 Efficient techniques for removal of DC due to

electrode polarization effect.

 Very flat Differential Voltage gain within EMG

bandwidth.

 High accuracy voltage gain setting (<1%) and

good linearity within full voltage dynamic.

 Low gain mismatch among channels (<0.5%).

 AFE transfer function with very low group delay

within EMG frequency band.

 Very high Common Mode Rejection within EMG

bandwidth (CMRR > 90dB).

 High Power Supply rejection (PSRR > 80dB).

 Very high Input impedance (|Z

AFE

|> 100MΩ)

within EMG frequency band.

 Negligible referred-to-input Total Noise floor

level with respect to Electrode-skin interface

noise 

EMG

(e.g. 1μV

RMS

within EMG bandwidth

[20Hz-500Hz]).

 Very low harmonic distortion of the EMG Power

spectrum.

 High accuracy (<0.05%), very low noise

(<3μVPP) voltage reference for A/D conversion.

 Programmable sampling frequency (>1ksps/ch)

and simultaneous digitizing process of EMG

signals.

 High performances, optical isolating interfaces to

guarantee patient safety (IEC-60601).

Secondary aspects should also be evaluated, to

optimize the circuit performances. The main

characteristics to be focused on are: a) Low power

and low voltage operating conditions (e.g. Battery

based power supply system); b) Innovative small-

sized integrated circuit suitable for wearable medical

device development; c) Use of advanced materials

and new technologies to improve the behaviour of

the electrode-gel-skin interface.

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3 ELECTRODE

CONFIGURATION

AND LOCATION

The importance of a standardization of electrode

configuration and location for the reproducibility

and correct interpretation of sEMG measurements

has been widely recognized. The European

concerted action SENIAM showed that a large

variety of sensors, sensor placement procedures, and

equipment are used in the European laboratories to

detect sEMG signals (Hermens et al. 1999) and this

is still one of the major issues in clinical surface

EMG.

3.1 Monopolar Detection

The ideal configuration for the detection of the

potential distribution on the skin is to move on the

skin a point electrode measuring the voltage with

respect to a remote reference where the potential is

zero (monopolar detection).

The monopolar detection provides the whole

information which can be recorded from the

detection volume but it is mainly used in research

applications because of its lack of spatial selectivity

(recording of the contribution of sources that are

near or far from the electrodes) and its sensitivity to

common mode signals. Spatial filtering techniques

have been proposed to detect surface EMG signals

tendon

5 mm

end plate

45 mm

1234 5

muscle

detection

points

Monopolar detection

NDD detection

Single differential detection

Double differential detection

123 45 123 45

123 45

20 ms

5 mm

Figure 2: Example of the spatial selectivity of different

spatial filters with respect to propagating and non

propagating components. It is possible to observe: a) the

higher spatial selectivity of NDD in transversal direction

with respect to mono-dimensional spatial filters. 2) the

enhancement of end-of-fiber effects in the case of NDD

filter with respect to the reduction obtained by SD and DD

filters.

enhancing the spatial selectivity of surface

recordings by limiting the detection volume

(Broman et al.1985; Disselhorst-Klug et al. 1997;

Farina et al. 2002a).

3.2 Spatial Filtering

Spatial filters in surface EMG detection are based on

the linear combination of signals detected by a

number of electrodes placed over the skin with a

defined geometry with the purpose of attenuating

specific spatial frequencies with respect to others.

One of the most common goals is the attenuation of

non-propagating components of either physiological

(such as the end-of-fiber effects and remote sources)

or external origin (power line interference) which

are present in the monopolar signals.

The effect of the spatial filter on the detected signal

depends on the weights assigned to each electrode,

on the geometry of the electrode configuration, on

the electrode shape and size.

3.2.1 One-dimensional Spatial Filters

The simplest and most widely used spatial filter is

the bipolar or single differential (SD), which records

the difference between the potentials detected by

two electrodes placed at a fixed distance (inter-

electrode distance, IED).

Despite the simplicity of the bipolar

configuration, the effect of its transfer function as a

spatial filter requires an accurate analysis for a

correct EMG detection and interpretation.

The differential detection system output depends

on the spatial frequency of the input and sEMG

spectral parameters obtained with different inter-

electrode distances and electrode sizes cannot be

compared (Fuglevand et al. 1992).

SD detection provides the rejection of common

mode signals; a further enhancement of spatial

selectivity can be achieved by using a more selective

spatial filter.

One of the most important classes of spatial

filters in sEMG recording is represented by the

Laplace filters. The simplest Laplacian filter is the

double differential (DD) filter that is constituted by

three equally spaced electrodes, the central electrode

weighted with a factor –2 and the others with +1.

More complex detection configurations to provide

selectivity high enough to separate single motor unit

action potentials (MUAPs) from the interference

EMG signal are represented by two-dimensional

spatial filters.

MultiChannelSurfaceEMG-DetectionandConditioning

121

3.2.2 Two-dimensional Spatial Filter

The propagation of a MUAP along the muscle fibres

results, on the skin surface, in a propagating two-

dimensional distribution spatially low-pass filtered

by the volume conductor. Since the optimal spatial

filter is the one closest to the inverse of the volume

conductor filter, two-dimensional spatial high-pass

filters have been proposed (Disselhorst-Klug et al.

1997; Reucher et al. 1987a, 1987b).

Reucher et al. proposed the normal double

differentiating filter (NDD-filter) realized by five

cross-wise arranged electrodes whereby the central

electrode is weighted with the factor –4 and the

surrounding electrodes with the factor +1. NDD-

filter improves the spatial selectivity in all directions

(Figure 4), and allows the separation of the activities

of single MUs even at maximum voluntary

contraction (Reucher 1987a, 1987b). Moreover the

2-D systems are less sensitive to fiber orientation

and the electrode placement is less critical with these

types of filters.

3.2.3 Spatial Filtering and Inter-electrode

Distance

The inter-electrode distance (IED) is regarded as one

of the most relevant properties of the sEMG

detection systems. Although it affects sEMG signal

characteristics, a high variability and a wide range of

values for IEDs (4-48 mm) can be found in literature

(Hermens 1999).

In literature it has often been suggested that a

decrease of inter-electrode distance (IED) would

increase the spatial selectivity of the detection

system. In literature no evidence can be found for

this. Roeleveld (Roeleveld et al. 1997a) performed an

experimental study investigating the effect of the IED

variation (from 6 to 84 mm) in the bipolar detection

on the contribution of motor unit potentials to the

surface EMG. The contribution of superficial and

deep motor units to the recorded SEMG signal was

found to be unrelated to IED as long as IED < 40

mm while for IED exceeding 40 mm the

contribution of deeper motor-units to SEMG is

greater than the contribution of superficial ones.

3.2.4 Detection System Orientation and

Location on the Muscles

The orientation defines the direction of a mono-

dimensional detection system with respect to the

direction of the muscle fibers; the location defines

the position of the detection system on the muscle.

The SD and DD detection systems are usually

placed in the direction of the muscle fibers but they

could also be placed in the transversal direction.

When electrodes are arranged parallel to the muscle

fibres, the filters are referred as longitudinal (LSD

and LDD) while when arranged transversally to the

muscle fibers, the filter is known as transversal

(TSD and TDD). LSD and LDD result in better

longitudinal and poorer transversal selectivity with

respect to TSD and TDD.

In literature, the most common locations of the

detection system on a muscle are the following: a) in

the center of the muscle, b) on the muscle belly; c)

somewhere between the innervation zone and one

tendon; d) on the motor point.

Although the transfer function of the spatial filter

is independent of the electrode location on the

muscle, the motor end-plates (where MUAPs

generate) and the muscle-tendon junctions (where

MUAPs extinguish) are two positions that must be

carefully considered.

The signal generated by a single fiber and

detected with a bipolar system placed along the fiber

direction, symmetrically with respect to the end-

plate of the fiber, provides a zero voltage. Since the

innervations of MUs in a muscle are concentrated in

one or two locations, this electrode location

corresponds to signals that are small, noisy, and

sensitive to small displacements between electrodes

and muscle (Masuda et al. 1985) and must be

avoided in practical applications. Similar

observations apply to muscle-tendon junction. For

this reason, the location of a pair of electrodes is

critical and should be optimized by placing the

electrodes between the innervation zone(s) and a

muscle-tendon junction. Figure 3 depicts this

situation and shows the importance of a correct

electrode placement.

EMG global variables, such as amplitude,

spectral characteristic frequencies, and estimates of

conduction velocity, are heavily altered when

electrode pairs are placed on or near the innervaton

zone (Jensen et al., 1993, Lateva et al., 1993, Roy et

al.,1986).

These considerations imply the identification of

the innervation zone(s) before the electrode pairs are

applied: this task can be achieved by means of an

electrode array.

3.2.5 Spatial Filtering and Cross-talk

The signal detected on a muscle and generated by

another active muscle is referred as cross-talk. Many

applications of sEMG require the simultaneous

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detection of sEMG from many muscles to evaluate,

for example, the muscular co-ordination pattern

(Koh and Grabiner 1993; Winter et al. 1994). In

these cases, it is mandatory to reduce the cross-talk

from near muscles.

Spatial high-pass filters enhance the signals

propagating along the fibers of MUs located close to

the recording electrodes and suppress the

contributions of more distantly located sources. At

some distance from the sources the contributions due

to end-of-fiber effects become predominant with

respect to the propagating components because the

latter decay in space more slowly than the first. It

could be concluded that high-pass spatial filtering

would reduce cross-talk and different kinds of

spatial filters have been applied for cross-talk

reduction. However, the experimental results have

shown that cross-talk is not reduced by spatial high-

pass filtering (van Vugt and van Dijk 2001).

This discrepancy between theory and practice is

justified by the fact that the model used for the

description of spatial filter theory considered a

potential distribution generated on the skin by a

propagating MUAP neglecting the MUAP

generation and extinction phenomena. It is known

that the generation and extinction phenomena

produce a non-propagating potential distribution on

the skin surface, and spatial filters have different

transfer functions with respect to propagating and

non-propagating signals. Simulations of the filter

responses to non-propagating potentials have shown

that most of the spatial filters do not reduce them

and in some cases they even enhance them

(Dimitrova et al. 2002; Farina et al. 2002b). Figure 2

shows an example of enhancement of end of fiber

potentials by NDD filter with respect to the

reduction obtained by SD and DD filters.

Although different techniques have been tested

to reduce crosstalk, this issue is not yet satisfactorily

solved.

Matrix column: 1 2 3 4 5

distal

proximal

SD channels

norm.val: 0.8 mV

distal

proximal

SD channels

norm.val: 0.8 mV

100 ms

30 degrees

110 degrees

RMS map

IZs

Figure 3: Example of topographical information obtained from multi-channel detection systems. sEMG signals have been

recorded during an elbow flexion using a grid of electrodes (13 rows and 5 columns with one missed electrode, 8 mm ied)

during a progressive elbow flexion. The SD SEMG signals (on the left) and the interpolated RMS distribution estimated on

one epoch 250 ms long (on the right) are reported for two elbow angles (30 degrees on the top and 120 degrees on the

bottom (0 degrees correspond to maximum extension)). The positions of the innervation zones and of the tendon are

highlighted. From the images on the right it is possible to identify the two areas (left and right) of activity corresponding to

the two heads of the biceps brachii and the different positions and shift of the innervation zones and tendon for the two

biceps brachii heads.

MultiChannelSurfaceEMG-DetectionandConditioning

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3.3 Spatial Sampling

The surface EMG signal evolves in time and space,

and it can be described as a three dimensional signal

with one temporal and two spatial (the skin plane)

dimensions. Sampling the EMG potential

distribution by placing a number of detection

systems in different locations over the skin allows

studying how the surface EMG signal evolves in

time and space.

If a spatial filter (one-dimensional (SD or DD),

two-dimensional (NDD or other type)) is applied to

each detection point, the potential distribution is

spatially filtered and also spatially sampled.

3.3.1 Spatial Sampling in One

Dimension: the Linear Arrays

The first systems performing a spatial sampling of

sEMG were proposed by DeLuca, Merletti, and

Masuda. They proposed linear arrays of electrode

placed along the fiber direction to estimate the

velocity of propagation of action potentials, to

identify some anatomical characteristics such as the

innervation zone location and the muscle fiber

length and to investigate in detail the processes of

generation, propagation, and extinction of the

MUAPs along the muscle fibers.

Roeleveld et al. (1997b) proposed the use of two

electrode arrays located both longitudinally and in

the transversal direction with respect to the muscle

fibers to estimate MU depth.

Linear electrode arrays have also been applied to

obtain guidelines for the standardization of the

sEMG recording when a global analysis of the signal

is performed.

3.3.2 Spatial Sampling in Two Dimensions

The spatial distribution of voltage on the skin above

the muscle can be detected with a grid of electrodes

that provides two dimensional (2D) sampling in

space. If the grid covers a large part of the muscle it

provides spatial information that is largely

independent of the temporal information. The time

evolution of the voltage distribution on the skin can

be tracked by sampling in time.

Multi-channel sEMG is an interesting non-

invasive methodology to: 1) obtain muscle

anatomical information (such as the location of

innervation zones, tendon endings, and the direction

of the muscle fibers), 2) to obtain a topographical

representation of muscle activity, and 3) to

decompose the surface EMG signal into the

constituent single MU action potential trains if

electrode grids with small electrode sizes and inter-

electrode spacing (High Density EMG, HD-EMG)

are used (Zwarts et al. 2003).

3.3.3 Anatomical Information and

Topographical Representation

of Muscle Activity

The knowledge of fiber direction and innervation

zone location is relevant, for instance, for defining

the optimal locations for estimating EMG variables

in isometric and dynamic contractions. The

topographical representation of muscle activity

allows studying the regional variations in the degree

of muscle activation with time. This is particularly

important in dynamic contractions. Figure 3 shows

the distribution of single differential (SD) sEMG

RMS at two different elbow angles of isometric

contraction of the biceps brachii and demonstrates

that a single sampling point is not representative of

the spatially heterogeneous muscle activity and the

activity it detects depends in a strong way on the

geometrical factors.

The two dimensional spatial sampling obtained

using HD sEMG detection systems results in a three

dimensional signal, which can be used to reconstruct

the 3-D potential distribution if the Nyquist limits

are met in all the three dimensions.

3.3.4 Aliasing in Space

To meet the Nyquist theorem in space, the inter-

electrode distance (IED) must be smaller than a

threshold value. If we simplify the problem and we

consider only propagating signals, the IED threshold

value can be identified starting from the relationship

= f

/v between the spatial frequency (f

cycles/m),

the temporal frequency (f

, cycles/s or Hz) and the

signal propagation velocity (v, m/s). If we consider

400Hz as the highest temporal frequency of sEMG

and a propagation velocity of about 4m/s, the highest

spatial frequency is 100 cycles/m. For the Nyquist

theorem, the spatial sampling frequency should be

higher than 200 samples/m, which means IED less

than 5 mm. Some commonly used values of IED (10

mm or 20mm) imply aliasing in space but its

consequences on the sEMG signals have not been

investigated.

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