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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015 1959

A Stimulus Artifact Removal Technique for SEMG

Signal Processing During Functional Electrical

StimulationShuang Qiu, Jing Feng, Rui Xu, Jiapeng Xu, Kun Wang, Feng He, Hongzhi Qi, Member, IEEE, Xin Zhao,

Peng Zhou, Member, IEEE, Lixin Zhang, Member, IEEE, and Dong Ming, Senior Member, IEEE

AbstractGoal: The purpose of this study was to design amethod for extracting the volitional EMG from recorded surfaceelectromyography (EMG), contaminated by functional electricalstimulation (FES) artifact.Methods:Considering that the FES ar-tifact emerges periodically with rather large amplitude in nonsta-tionary EMG, we designed an adaptive-matched filter (AMF) viagenetic algorithm (GA) optimization. Both the simulated and realdata from seven subjects were processed, using the GA-AMF filterand comb filter, respectively. To test the filtering effect on theEMG,

contaminated with FES artifact of different current intensities, thecontaminated EMG was simulated by combining the simulationartifact and clean EMG with various FES artifacts to clean EMGratios.Results:The results show that, in simulation test, comparedto theEMG filtered by comb filter, thesimulated EMG(p < 0.05),filtered by using GA-AMF, had significantly higher correlation co-efficient, higher signal to noise ratio, and lower normalized rootmean square error, whereas the real EMG (p < 0.05), filtered byusing GA-AMF had higher power reduction than that filtered byusing comb filter. The results indicate that GA-AMF can effec-tively remove FES artifact from the EMG of the stimulated muscleand its adjacent muscle, and the GA-AMF filter performed betterthan did the comb filter.Conclusion:All these results demonstratethat the GA-AMF filter is capable of extracting volitional EMGfrom the stimulated muscle and adjacent muscles. Significance:GA-AMF could provide technical support for improving EMGfeedback control of FES rehabilitation system.

Index TermsArtifact removal, functional electrical stimula-tion, stimulus artifact, surface EMG.

I. INTRODUCTION

THE spinal cord, the main pathway for interactions between

the brain and peripheral nervous system, lacks the capabil-

ity for self-regeneration, and its motor functions cannot even be

restored by currently available rehabilitation technologies [1],

[2]. Functional electrical stimulation (FES) to induce muscle

Manuscript received September 3, 2014; revised November 22, 2014 andFebruary 3, 2015; accepted February 21, 2015. Date of publication February27, 2015; date of current version July 15, 2015. This work was supported bythe National Natural Science Foundation of China under Grants 81222021,61172008, 81171423, 81127003, National Key Technology R&D Program ofthe Ministry of Science and Technology of China under Grant 2012BAI34B02and the Program for New Century Excellent Talents in University of the Min-istry of Education of China under Grant NCET-10-0618. Asterisk indicatescorresponding author.

D. Ming is with the Neural Engineering and Rehabilitation Lab, Depart-ment of Biomedical Engineering, College of Precision Instruments and Op-toelectronics Engineering, Tianjin University, Tianjin 300072, China (e-mail:[email protected]).

S. Qiu, R. Xu, J. Xu, K. Wang, F. He, H. Qi, X. Zhao, P. Zhou, and L. Zhangare with Tianjin University.

J. Feng is with the Shriners Hospitals for Children.Digital Object Identifier 10.1109/TBME.2015.2407834

contraction and corresponding joint movement has been popu-

larly used to restore paralyzed motor functions caused by spinal

cord injury [3], [4]. It also has wide applications in cardiac and

diaphragmatic pacing [5], [6], pain management [7], truncal

stability [8], improvement of bone and muscle health [9], as

well as in restoring function or preventing loss of function [10].

With the stimulation patterns specifically adjusted depending

on the subjects muscle activities, the electrical stimulus pulsesneeded to induce and control neural activations can be con-

trolled for functional restoration [10] through surface or subcu-

taneous electrodes. At the same time, surface electromyography

(EMG), a noninvasive method to measure muscle activities, is

extensively used in investigating muscle function and fatigue

[11][14]. However, recorded EMG signals are easily contam-

inated by FES artifacts due to EMG amplifiers simultaneous

recording of stimulus current during electrically elicited con-

tractions [15] and by artifacts induced by electrical pulse with

high amplitude. For a better understanding of FES-induced mus-

cle activities measured with EMG, removing/eliminating the

artifacts caused by electrical stimulation is of vital importance.

An FES system usually generates constant low-frequency

stimulation pulses that will be mixed with the recorded EMG of

harmonic frequencies collected from the stimulated muscle. Re-

moving such FES artifacts by using a frequency decomposition

method is difficult because of the overlapping of EMG and FES

artifact in the frequency domain. Using a blank window method

for artifact removal, several studies [16], [17] have attempted

to use the EMG signal as the feedback to predict and control

stimulation current. However, such blank window method could

not remove long-lasting artifact by extending the blanking time

only. FES artifact may last for milliseconds or even tens of

milliseconds because of the acquisition device, tissue proper-

ties, electrode properties, and other reasons [15]. Therefore, theblank window method failed to extract the EMG from lower

limb muscles stimulated by surface FES. Some research groups

also tried to apply fixed comb filter and two-stage peak detection

algorithm, under the assumption that the FES artifact and the

M-wave generated by the stimulation are stationary [18][20].

However, those methods could not remove FES artifact under

high stimulation intensity, which could easily induce deforma-

tion of FES artifact due to capacitive characteristics of muscle.

Thus, the present study aims at removing the artifact evoked

by FES from the recorded EMG for a clean EMG signal, so

that we can understand more about muscle activities and the ef-

fect of stimulation, as also about the controlling of stimulation.

0018-9294 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

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1960 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 8, AUGUST 2015

We proposed a novel technique using an adaptive-matched filter

algorithm based on genetic algorithm (GA-AMF). Its perfor-

mance, on both simulated data and real data, was compared

with that of comb filter (one of the most effective methods for

FES artifact removal [21], [22]).

II. EMG SIGNALS-EXPERIMENTAL RECORDINGS

ANDMETHODS

A. Subjects

Seven healthy subjects (three males and four females, age:

23.5 1.5 years; body weight: 64.8 19.2 kg; height: 1.722 0.102 m) participated in this study. None of the subjectsreported history of musculoskeletal disorder. To exclude the

impact of residual fatigue, the subjects were advised not to take

up any strenuous exercise 24 h prior to the commencement of the

experiment. The study was approved by the ethical committee

of Tianjin University.

B. Experimental Setup and Protocols

Parastep I NMES system by Sigmedics Inc. (USA) was used

to stimulate the quadriceps muscle for inducing effective muscle

contraction, with electrodes placed over the motor points of the

quadriceps muscle. The stimulation pulse width (300 s) andfrequency (25 Hz) were preset and kept stable during the whole

experiment procedure [10]. The stimulation amplitudes ranged

from 0 to 150 mA (470 ), with an increment step of 10 mA.An EMG system (Noraxon TELEMYO 2400T transmitter

and Noraxon MyoResearch Master Edition software, Noraxon

USA, Scottsdale AZ, USA) was used to record EMG signals

during knee joint movement. The skin was first wiped with al-

cohol, and then pregelled disposable surface electrodes wereplaced (F55, Tianjin Zhuyou, Tianjin, China). Each electrode

had a dual silver/silver chloride recording surface with aggres-

sive hydro-gel adhesive and can be repositioned on the same

patient. With a telemetric transmitter attached to the subject,

two pairs of surface electrodes (interelectrode distance of 2 cm)

were placed, one pair over the muscle belly of rectus femoris

(RF) and the other over gastrocnemius muscle (GM). The con-

ductive area of each electrode was 1 cm in diameter. The EMG

signals were sampled at 1.5 kHz with a 12-b resolution and then

transmitted to a computer through Noraxon Wireless PC LAN

card. The signals were digitized online and stored in a computer

equipped with Noraxon Myoresearch XP software.At the commencement of the experiment, the subjects were

seated with the shank free to swing. Each subject took vol-

untary movement session and FES induced movement session

separately.

In the voluntary movement session, the clean EMG data were

recorded during knee extension from a relaxed position (with

knee angle at approximately 90) to full extension (with knee

angle at 180). The subjects were asked to perform six cycles of

voluntary movement. Each cycle included knee extension and

flexion for a duration of 1 s and a relax interval of approximately

2 s. Thus, the total duration of each cycle is 3 s. The EMG

data collected in the middle cycles (the third and fourth cycles)

were further adopted in the simulation, because they were more

Fig. 1. Flow chart illustrating the experimentaland filtering process. Hexagonstands for experimental session; rectangle stands for process; ellipse stands forsignal input or output. OR is the logical operator or.

similar to the natural contraction than the cycles at the beginning

or the end of the session.

In the FES induced movement session, stimulation current

was delivered to the knee extensors (quadriceps muscle group)

of the right leg. Stimulation intensity was first increased in steps

of 10 mA from 0 level (0 mA) to the level that induced full kneeextension and then decreased in steps of 10 mA back to level 0.

Electrical stimulation at each level of intensity lasted 4 s. Upon

the stimulation current reaching the level of 60 mA, recording of

the EMG signals started. The recorded EMG was contaminated

by the FES artifact and would be used later for simulating FES

artifact. For healthy adults, 60 mA, based on Parastep I system,

was a modest level of stimulation which could generate slight

movement but was not high enough to induce contraction with

a large joint displacement.

C. Generating Contaminated EMG With Various FES

Artifacts to Clean EMG Ratios for Simulation

The experimental and filtering process is illustrated as a flow

chart (see Fig. 1). The EMG data from voluntary movement

with a cycle of 3 s were used as clean EMGs(t).

When recording EMG from a stimulated muscle or its ad-

jacent muscles, it is impossible to separate muscle contraction

from FES artifact prior to filtering. The global average method

is the most basic average method that averages the signal over

all segments [18]. The simulated FES artifact n(t) was calcu-

lated through averaging the same subjects EMGg(t)recorded

during FES using the global average method, as described be-

low. Every FES waveform consisted of a pair of a positive pulse

and a negative pulse with equal charge (pulse amplitude 60 mA)

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QIUet al.: STIMULUS ARTIFACT REMOVAL TECHNIQUE FOR SEMG SIGNAL PROCESSING DURING FES 1961

Fig. 2. Illustration of typical waveform of FES stimulator with a stimulationfrequency of 25 Hz.

for security (see Fig. 2). Since the FES frequency is 25 Hz, the

duration of each FES cycle is 0.08 s. Therefore, the EMG signal

recorded during FES was segmented with 0.08-s epochs basedon the cycle of FES system. FES artifact [n(t)] was extracted

from real contaminated EMG [g(t)]recorded during FES

n(t) = 1

N

Ni=1

gi (t) (1)

where n(t) is the simulated FES artifact, and gi (t) is the ithsegment of the EMG recorded during FES; N represents the

total number of segments. The 3-s EMG recorded during each

movement cycle included 37 epochs of 0.08 s and a remainder

of 0.04 s which was discarded. Therefore, N is 37 in the above

equation. FES artifact [n(t)] would be mixed with clean vol-

untary EMG to simulate contaminated EMG in the simulation

test.

The simulated contaminated EMG x(t) was the sum of the

clean voluntary EMGs(t)and weighted FES artifactn(t).

x(t) =s(t) +n(t) (2)

where is the ratio between FES artifact and clean voluntaryEMG, i.e., 0.3, 0.5, 1, 1.3, 1.5, and 2 (see Section II-F). It

is to be noted that the recorded EMG in the FES system was

also contaminated by some nonadditive noise which was of low

amplitude [18] and could hence be ignored for the present study.

D. GA-AMF Filter for FES Artifact Removal

The adaptive-matched filter (AMF) via genetic algorithm

(GA) optimization was specifically designed to remove FES

artifact.

1) FES Artifact Estimation Using Dynamic Average: As an

initial step of the GA-AMF algorithm, a dynamic averaging

method was used to estimate the FES artifact template from

the contaminated EMG signal contained in the GA-AMF. Seg-

ments with similar shapes of FES artifact were averaged as

a good estimator for the artifact [18]. Such a dynamic aver-

aging method [18] has the advantage of overcoming the dy-

namic changes of artifact characteristics. Additionally, several

continuous segments with temporal similarity can be averaged.

For the ith segment, the FES template was estimated as the

average signal of2kw + 1segments in its vicinity

vi (t) =

i+ kwj = ik w

Xj (t)

2kw + 1 (3)

wherevi (t)is theith segment of estimated FES template v(t),andXj (t)is thejth segment of the contaminated EMG signal,which acts as the input signal of GA-AMF; kw is a positiveinteger, which is selected as 3 for the present study. When the

number of segments before or after the ith segment was smaller

thankw , only the available segments were used for averaging[18]. The template was calculated individually considering the

differences between the subjects muscle properties.

2) AMF: With the template of FES artifact [v(t)] identified

in the above step, GA-AMF filter was designed to detect and

remove FES artifact mixed with the input signal. With the input

of the contaminated EMG and the output of the extracted FES

artifact in the AMF filter, the difference between the input and

output signals gives the filtered EMG. The AMF filter can beestablished using a finite impulse response digital filter with

an impulse responseh(t) =n(t0 t), wheret0 is determinedspecifically to maximize the ratio (r0 ) between the FES artifact

and the filtered EMG [23] according to the following equation:

r0 = |yn (t0 )|

E{ys (t)2}(4)

where t0 is the time when r0 reaches the maximum, E is themathematical expectation of ys (t)

2 . ys (t) =s(t)h(t) is the

convolution of the signal s(t)with the filter response h(t). Sim-

ilarly, yn (t) =n(t)h(t). Here, r0 with high values indicates

that more FES artifact can be detected and a clean EMG can beobtained.

Before the matched filter detects the FES artifact, the orig-

inal signal would first pass the whitening filter to remove the

correlated components in the signal[s(t)]with a linear adaptive

autoregressive (AR) model.

s(t) =M

i=0

wi (t)s(ti) +t , 0 i M (5)

whereMis the order of the filter, s(ti)is delay ofs(t)byi, tis the modeling error that gradually approximates white noise

if the model is correct. The set of AR parameters is represented

asW(t) = [w1 (t), w2 (t), . . . , wM(t)]. By selecting the properorder M, the whitening filter can be adjusted so that it does

not predict the stimulation artifact n(t). Therefore, when the

contaminated EMG is received by the whitening filter, its output

is as given below

y(t) = x(t)M

i=0

wi (i)x(ti)

= n(t)M

i=0

wi (i)n(ti) +t =nwhite(t) +t (6)

wherenwhite(t)is the distorted signal after passing through the

whitening filter. When the observation window orderMis small

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enough, the high frequency components are no longer included

in the AR model, and the detection ofnwhite(t) is equivalentto the detection ofn(t) [23]. The integral value ofMrangingfrom 2 to 5 was determined by the GA that will be explained

in the next section. The adaptive least mean square algorithm

[24] was used to computeW(t), with gradient search methodwhich updates the filter coefficients for each input signal V(t) =K[v(t), v(t+ 1), . . . , v(t+M 1)][24].Kis the multiple.

W(t+ 1) =W(t) + 2e(t)V(t) (7)

where is the filters searching step size that can determine thefiltering speed, ande(t)represents the instantaneous error of the

filter based on the following equation:

e(t) =x(t)W(t)HV(t) (8)

whereHis conjugate transpose operator. W(0) ={0}.Before running the AMF algorithm, the three parameters (M,

,K) of AMF, which are critical to filtering performance, needto be initialized based on experience, and GA was applied to

estimate these filter parameters.3) Optimization of AMF by Using GA: GA, a global method

of searching by simulating the natural biological evolution [25],

wasused to optimize the parameters (M, , K) of AMF followingthe procedures given below:

1) Encoding: Since the three parameters (M,, K) are realnumbers, encoding the problems input data is real value

encoding. An individual represents a set of parameters for

AMF.

2) Fitness Value and Objective Function: Objective function

is used to calculate the fitness value (J) of each individual

in the population. An individual with higher fitness value

indicates a better solution for AMF than the one withlower value. The objective function is defined as follows:

J=

i= 0

(q1|eJ(i)|+q2 e2J(i)) +q3 P0 (9)

where eJ(i)is the removed FES artifact, calculated as thedifference between the contaminatedEMG andthe filtered

EMG after removal of FES artifact;P0 is the energy ratiobetween the removed FES artifact and the EMG, filtered

by using GA-AMF;q1 ,q2 andq3 are the correspondingweights. This objective function is characterized by the

removed FES artifact signal, its energy, and the energy

ratio between removed part and remaining part. Highervalues ofJindicate more effective removal of FES artifact

and much cleaner filtered EMG. Here,q1 + q2 = 1,q1 =0.999,q2 = 0.001,q3 = 0.01.Jvalue was used to selectthe optimal individual by comparing individuals in the

population.

3) Genetic Operators: Populations are produced adopting

the following procedures [25], [26]. First, individuals (M,

, K) with high fitness values were selected directly forthe next population. The crossover operation was imple-

mented by combining genetic informationfrom two parent

individuals to produce a new individual. Then, to avoid

being trapped into locally optimal solution prematurely,

this updated individual was modified using mutation op-

eration implemented by adding a random value to the

parameters (M,,K) and limiting their values to the pre-selected ranges.

The output of AMF is the detected FES artifact. The output of

GA-AMF is defined as the difference between the contaminated

EMG (the filter input) and the detected FES artifact. In other

words, the output of GA-AMF is the clean EMG.

The initialization of GA-AMF was based on the following

parameters: population size= 30, crossover probability= 0.7,mutation probability= 0.1, maximum generation = 100.q1 =0.999,q2 = 0.00,q3 = 0.01. The value ofMranged from 2 to5; was set at values between 0 and 1; for RF, 0 < K< 3,and for GM, 1< K< 10, and the parameters (M,,K) will berandomly initialized in these ranges.

E. Simulation and Test

Experiments were conducted on simulated and real EMG sig-

nals of the seven subjects to show the effectiveness of GA-AMF.

For a comparison with the proposed method, the conventionalcomb filter was involved as well. The comb filter is a commonly

used and well-performing filter; it accentuates or attenuates the

input signal at regularly spaced frequency intervals. In our ex-

periment, the fixed comb filter was used to remove interference

at 12.5 Hz and its corresponding harmonics.

First, performances of the GA-AMF filter and comb filter

were evaluated using simulated contaminated EMG with FES

artifact to clean EMG ratio of 1.

Second, to test the filtering effect on EMG contaminated

by FES of various intensities, contaminated EMG were first

simulated by mixing FES artifact and clean EMG in various

ratios (six ratios: 0.3, 0.5, 1, 1.3, 1.5, and 2), and then comb

filter and GA-AMF filter were used separately to filter the FES

artifact from the simulated contaminated EMG.

Third, AMF needs to be set with optimized parameters to help

boost processing speed, and the optimized parameters (M, ,K)were obtained by filtering the contaminated EMG (in the ratio

level of 1) using GA-AMF filter. The optimized parameters were

then setas thefixed parameters of AMF to filter thecontaminated

EMG (in the other ratio levels) to investigate the difference

between GA-AMF and fixed parameters AMF.

Fourth, the real contaminated EMG signals were separately

filtered by a comb filter and a GA-AMF filter.

F. Filter Performance Evaluation

Performance of the GA-AMF filtering on simulated contami-

nated EMG from the seven subjects wasevaluated andcompared

with that of comb filter. The following parameters, which could

evaluate the filtering effect and distortion, including correlation

coefficients [27], signal-noise-ratio (SNR) [28], normalized root

mean squared error (NRMSE), and frequency spectrum [29],

were calculated for each filtering method.

First, correlation coefficients between the clean EMG and

the filtered EMG provided a quantitative measure of filtering

performance in the time domain [27]. To make an objective

comparison of the two filters, correlation coefficients based on

Pearson correlation with significance level of 0.05 were used

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Fig. 3. Example of filtering the simulated contaminated EMG (with FESartifacts to clean EMG ratio of 1) of one subjects right RF using comb filter andGA-AMF filter, respectively. (a) Clean EMG; (b) Contaminated EMG, whichwas the sum of clean EMG and FES artifact; (c) EMG filtered using combfilter; (d) EMG filtered using GA-AMF filter. Their corresponding spectraldistributions were displayed on the right column (e)(h).

as an evaluation index. Higher correlation coefficients indicate

greater filtering performance.

Second, the SNR, defined as the ratio of the mean energy

of the clean EMG signal s(t) to the mean energy of the noise

[28], was used to evaluate the overall improvement of signal

quality. Higher SNR values signify better filtering performance.

SNR was calculated for both the input signal x(t)and the output

signaly(t) of the filter, following the artifact-removing procedure

summarized below

SNRx = 10log10 s(t)2

nx (t)

2

= 10log10

s(t)2

(x(t)s(t))2 (10)

SNRy = 10log10

s(i)2

ny (i)2

= 10log10

s(t)2

(y(t)s(t))2. (11)

Third, theroot mean squareerror (RMSE) wasused to analyze

the average difference between clean EMG and filtered EMG.

To compare filtering performance among signals with differ-

ent amplitude ranges, RMSE was normalized by the standarddeviation of clean EMG, resulting in NRMSE.

NRMSEx = RMSExStd(s(t))

=

(x(t)s(t))2 / Number of samples

Std(s(t)) (12)

NRMSEy = RMSEyStd(s(t))

=

(y(t)s(t))2 / Number of samples

Std(s(t)) . (13)

Fig. 4. Example of filtering the simulated contaminated EMG (with FESartifact to clean EMG ratio of 1) of one subjects right GM using comb filterand GA-AMF filter, respectively. (a) Clean EMG from GM; (b) ContaminatedEMG composed of clean EMG clean EMG and FES artifact; (c) EMG filteredwith comb filter; (d) EMG filtered with GA-AMF filter. Their correspondingspectral distributions were displayed on the right column (e)(h).

Fourth, to compare the clean and filtered EMG distributions

in frequency domain, spectra of the clean EMG and of the EMG

filtered using GA-AMF filter or comb filter were obtained by

using a sequential Fourier transform.

Lastly, since the real clean EMG cannot be measured sepa-

rately in FES experiments, it is impossible to calculate SNR or

assess filter performance with real data. Therefore, power re-

duction (PR) was used as an alternative to SNR. It is defined as

the ratio between power of the input signal [contaminated EMG

g(t)]and power of the output signal [filtered EMG,yg (t)][30].

PR = 10 log10

Pinput

Poutput

= 10 log10

g(t)2

yg (t)2. (14)

It is to be noted that although PR is not a direct measurement

of filter performance when processing real data, it does, at least,

indicate the extent of FES artifact removal.

G. Statistical Analysis

Additional statistical analyses were applied to compare the

performances of removing FES artifact from the contaminated

EMG signal.

Two-tailed pairedt-test was used to compare the differencesbetween GA-AMF filter and comb filter, and between GA-AMF

filter andfixed parameters AMF filter. Bonferroni correction was

applied to counteract the increased likelihood of type I error of

multiple comparisons. The significance level for each family of

the tests was set as 0.05. Thepvalue of each t-test was adjusted

by multiplying the number of tests in the family. The reported

pvalue was the adjusted value.

The two-way repeated measures analysis of variance

(ANOVA) with filter method (comb and GA-AMF) and ratio

of FES artifact to clean EMG (six ratios: 0.3, 0.5, 1, 1.3, 1.5, 2)

as factors was applied to assess their dependence the effect of

FES artifact removal. Significance level was set as 0.05.

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TABLE ICORRELATION COEFFICIENTBETWEENCLEANEMG AND FILTEREDEMG AFTERREMOVAL OFFES ARTIFACTUSING ACOMBFILTER ORGA-AMF FILTER

Mu sc le Filte r S ub je ct1 S ub jec t2 Su bjec t3 Su bjec t4 Su bjec t5 Su bje ct6 S ub je ct7 mea nstd

RF COMB 0.85 0.84 0.83 0.84 0.85 0.88 0.87 0.850.02

GA-AMF 0.87 0.88 0.88 0.86 0.88 0.91 0.88 0.880.01

GM COMB 0.78 0.80 0.75 0.77 0.78 0.77 0.80 0.780.02

GA-AMF 0.81 0.82 0.77 0.79 0.79 0.80 0.82 0.800.02

Input to the filters is the simulated contaminated EMG with FES artifact to clean EMG ratio of 1. Significant difference between the

two filters is indicated by one asterisk (p < 0 .05 ) or two asterisks (p < 0 .01 ).

TABLE II

SNR y (UNIT: DB) OF EMG FILTERUSINGCOMBFILTER ORGA-AMF FILTER


RF COMB 5.56 5.26 5.16 5.27 5.46 6.49 4.45 5.380.56

GA-AMF 6.04 6.47 6.44 5.85 6.36 7.48 6.52 6.450.48

GM COMB 3.17 2.12 3.53 3.58 0.59 2.37 0.77 2.301.15

GA-AMF 3.48 2.28 3.89 3.91 0.65 2.69 0.82 2.531.27

Input to the filters is the simulated contaminated EMG with FES artifact to clean EMG ratio of 1. Significant difference between thetwo filters is indicated by one asterisk (p < 0 .05 ) or two asterisks (p < 0 .01 ).

TABLE IIINRMSEy OFEMG FILTEREDUSINGCOMBFILTER ORGA-AMF FILTER


RF COMB 0.53 0.55 0.55 0.54 0.53 0.47 0.48 0.520.03

GA-AMF 0.50 0.47 0.48 0.51 0.48 0.42 0.47 0.480.03

GM COMB 0.75 1.00 0.67 0.68 2.03 1.01 1.82 1.140.52

GA-AMF 0.73 0.99 0.64 0.66 2.00 0.97 1.81 1.110.52

Input to the filters is the simulated contaminated EMG with FES artifact to clean EMG ratio of 1. Significant difference between the

two filters is indicated by one asterisk (p < 0 .05 ) or two asterisks (p < 0 .01 ).

TABLE IVOPTIMALFILTERPARAMETERS OFGA-AMF FILTER FORFILTERING EMG

FROMRF AND GM OF SEVENSUBJECTSDURINGSIMULATION TESTS

Subject RF GM

M K M K

1 0.48 3 2.38 0.40 2 8.21

2 0.35 4 2.01 0.37 3 4.11

3 0.49 2 0.79 0.42 2 3.00

4 0.23 2 1.30 0.28 2 10.35

5 0.58 3 1.58 0.35 2 9.82

6 0.78 3 1.32 0.56 3 5.88

7 0.28 2 1.25 0.77 4 3.55

III. RESULTS

A. Effect of FES Artifact Removal

Fig. 3 illustrates an example of filtering the simulated con-

taminated EMG of the right RF using comb filter and GA-AMF

filter, respectively. Signals were obtained in both the time and

frequency domains. Fig. 3(a) shows the clean EMG obtained

from the right RF during voluntary movement. Fig. 3(b) shows

the simulated contaminated EMG which was the sum of the

clean EMG and the FES artifact. The simulated contaminated

EMG was in the SNRx level of16.32 dB. Fig. 3(c) showsthe EMG filtered using a comb filter, and Fig. 3(d) the EMG

filtered using GA-AMF filter with minimal observable residual

FES artifact. As can be seen in the right column of Fig. 3, the

filtered EMGs[see Fig. 3(c) and (d)] and the clean EMG [see

Fig. 3(a)] have similar spectra [see Fig. 3(e), (g) and (h)] in

the frequency domain. Similarly, Fig. 4 shows an example of

filtering the simulated contaminated EMG from the right GM of

the same subject. Although the spectrum graph contains some

distortions, it can be said that both the filters could effectively

eliminate FES artifact (see Figs. 3 and 4).

The performances of the comb filter and of the GA-AMF filterwere evaluated and compared using the following parameters: 1)

Correlation coefficient between the clean EMG and the filtered

EMG; 2) SNRy of the filtered EMG; 3) NRMSEy of the filteredEMG. The signal to be filtered was the contaminated EMG with

FES artifact to clean EMG ratio of 1. The results of comparison

for both RF and GM are summarized in Tables I to III. For

both RF and GM, GA-AMF filter shows a higher correlation

coefficient (p

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Fig. 5. Analysis of the contaminated EMGs from RF with various FES arti-fact to clean EMG ratios. (a) Correlation coefficients between clean EMG andcontaminated EMG; (b)S NRx of the contaminated EMG; (c) NRMSEx ofthe contaminated EMG.

Fig. 6. Performance of removing FES artifact from the contaminated EMG

signals of RF with various artifact to EMG ratios using comb filter or GA-AMFfilter. (a) Correlation coefficients between clean EMG and filtered EMG; (b)

SNR y of the filtered EMG; (c) NRMSEy of the filtered EMG.

Fig. 7. Comparison of performance of the filters using GA-AMF or fixed-parameters AMF when the FES artifact to clean EMG ratio varies. Significant

difference is indicated by an asterisk (p

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TABLE VPR (UNIT: DB) AFTERFILTERING REALEMG OF SEVENHEALTHYSUBJECTS

Muscle Filte r S ubje ct1 S ub je ct2 S ub je ct3 S ub jec t4 Sub jec t5 Su bjec t6 Su bjec t7 mea nstd

RF COMB 17.7 15.5 19.0 13.3 20.6 16.1 18.5 17.22.3

GA-AMF 19.2 16.0 21.0 15.1 21.1 22.2 19.8 19.22.5

GM COMB 12.5 13.2 15.0 15.2 15.8 16.8 15.5 14.91.4

GA-AMF 13.7 15.0 15.6 15.3 15.8 19.1 16.8 15.91.6

Significant difference between the two filters is indicated by one asterisk (p < 0 .05 ) or two asterisks (p < 0 .01 ).

GA-AMF filter) and the stimulus artifact to clean EMG ratio

(six levels: 0.3, 0.5, 1, 1.3, 1.5, 2) was applied with dependent

variables of correlation coefficient, SNRy , and NRMSEy , re-spectively. The results revealed no interaction between the type

of filter and the FES artifact to clean EMG ratio. But, the type

of filter showed a significant effect on all the three dependent

variables that measure filter performance [correlation coeffi-

cient: F(1, 6) = 41.006,p

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The filtering effect of GA-AMF on contaminated EMG was

tested on six ratios of FES artifact-to-EMG. The filtering per-

formance of both GA-AMF and comb filters decreased as the

artifact-to-EMG ratio increased (see Fig. 6). Hence, it follows

that the filtering performance depends on the degree of contam-

ination of EMG by FES artifact. In other words, GA-AMF filter

performed better with less severely contaminated EMG. The

performance of GA-AMF filter did not differ significantly from

that of AMF filter with fixed parameters when the artifact-to-

EMG ratio was below 2 (see Fig. 7). This result supports the use

of AMF filter with fixed parameters to increase the processing

speed in removing FES artifact in real-time FES system. How-

ever, the fixed parameters are applicable to the fixed location of

the measuring EMG electrodes. If the electrodes or stimulation

electrodes are moved to a different location, the parameters will

have to be changed.

Our results show that GA-AMF was effective in removing

FES artifact for the stimulated muscle, as well as its adjacent

muscle (see Figs. 8 and 9). The contamination of EMG of GM

(an adjacent muscle of RF) was less severe than that of theRF (the only muscle being stimulated for inducing knee joint

movement). EMG of the stimulated muscle, as also of its ad-

jacent muscles, should be filtered to extract voluntary EMG

signals.

Indeed, the present study has several limitations: 1) GA-AMF

wasdesigned with the assumption that the stimulation frequency

is constant,and it remains to be ascertained if theFES systemcan

be applied with varying frequencies; 2) we tested this method

only on healthy subjects, but it has to be tested on paralyzed

muscle, too.

V. CONCLUSION

The GA-AMF filter, with better performance than that of

comb filter, can effectively eliminate FES artifact, making the

filtered signal similar to volitional EMG. To conclude, it can be

said that the present study provided an optimal way to extract

weak volitional EMG, collected from a stimulated muscle and

its adjacent muscles. This would be of great help in investigating

muscle fatigue, controlling stimulation intensity, and removing

electrical stimulation artifact from electroencephalogram.

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Shuang Qiu received the B.S. degree in biomedicalengineering from Tianjin University, Tianjin, China,

in 2010, where she is currently working toward thePh.D. degree in biomedical engineering.

She is currently a Visiting Student at the De-partment of Physical Medicine and Rehabilitation,Harvard Medical School, Boston, MA, USA. Herresearch interests include rehabilitation engineering,especially in functional electrical stimulation, the de-

sign of functional electrical stimulation system, sig-nal processing and analysis of electromyography and

electroencephalogram evoked by functional electrical stimulation.

Rui Xu received the B.S. degree in biomedical en-gineering from Tianjin University, Tianjin, China,in 2010, where she is currently working toward thePh.D. degree.

Her research interests includebiomechanics, mus-culoskeletal modeling, and EMG processing.

Jiapeng Xureceived the B.S. degrees in biomedicalengineering from Tianjin University, Tianjin, China,in 2014, and where he is currently working towardthe Masters degree.

His research interests include braincomputer in-terface, functional electrical stimulation, and stokerehabilitation.

Kun Wang received the Bachelors degree fromHebei University of Technology, Tianjin, China, in2013. She is currently working toward the Mastersdegree in biomedical engineering at Tianjin Univer-sity, Tianjin.

Her major research interests include physiologi-cal information detection and processing evoked bymotor imagery.

Feng He received the B.S. and M.S. degrees inbiomedical engineering from Tianjin University,Tianjin, China, in 1994 and 1998, respectively.

He had served as the Chief Technical Officer atTianjin Stone Company for 5 years. Since 2003, hehas been a Lecturer of College of Precision Instru-ment and Optoelectronics Engineering, Tianjin Uni-versity. His research interests include neural engi-neering, biomedical signal detection and processing,

and medical instrument design.

Hongzhi Qi received the B.S., M.S., and Ph.D. de-greesin biomedical engineering from Tianjin Univer-sity, Tianjin, China, in 1998, 2002, and 2008, respec-tively.

He is currently an Associate Professor with theDepartment of Biomedical Engineering, College ofPrecision Instrument and Optoelectronics Engineer-ing, Tianjin University. From 2009 to 2011, he wasa Postdoctoral Research Fellow at the Institute ofBiomedical Engineering, Chinese Academy of Med-

ical Science and Peking Union Medical College, Bei-jing, China. His research interests include b iosignal processing, machine learn-ing, and their application to rehabilitation technology, with emphasis on humanmachine interface and neurofeedback treatment.

Xin Zhao received the B.S., M.S., and Ph.D. degreesin biomedical engineering from Tianjin University,

Tianjin, China, in 2002, 2005, and 2007, respectively.He is currently a Lecturer in the Department of

Biomedical Engineering, Tianjin University. His re-search interests include medical and biomedical im-age processing, image data analysis, and biomedicalimaging.

Peng Zhou received the B.S., M.S., and Ph.D. de-grees in biomedical engineering from Tianjin Uni-versity, Tianjin, China, in 2002, 2005, and 2007, re-spectively.

He is currently an Associate Professor at TianjinUniversity. His research interests include neural en-gineering and biomedical signal processing.

Lixin Zhang received the B.S. and M.S. degreesin biomedical engineering from Tianjin University,

Tianjin, China, in 1986 and 1993, respectively.He is currently a Researcher in the Departmentof Biomedical Engineering, Tianjin University. Hisresearch interests include neural engineering andbiomedical signal processing.

Jing Feng received the B.S. and M.S. degreesin biomedical engineering from Tianjin University,Tianjin, China, in 1991 and 1993, respectively. Shereceived the Ph.D. degree in biomedical engineeringfrom The Hong Kong Polytechnic University, HungHom, Hong Kong, in 1997.

She is currently the Manager of the Motion Anal-

ysis Lab at Shriners Hospitals for Children in Port-land, OR, USA. Her research interests include gaitand clinical motion analysis, biomedical signal pro-cessing, motor development, and motor control.

Dong Ming received the B.S. and Ph.D. degreesin biomedical engineering from Tianjin University(TJU), Tianjin, China, in 1999 and 2004, respec-tively.

From 2002 to 2003, he was a Research Associatewith the Department of Orthopaedics and Trauma-tology, Li Ka Shing Faculty of Medicine, Universityof Hong Kong, Hong Kong, and from 2005 to 2006,he was a Visiting Scholar with the Division of Me-chanical Engineering and Mechatronics, University

of Dundee, U.K. He joined TJU Faculty with theCollege of Precision Instruments and Optoelectronics Engineering in 2006 andhas been a full Professor of biomedical engineering since 2011. He is currentlythe Chair Professor at the Department of Biomedical Engineering, TJU, and theHead of the Neural Engineering and Rehabilitation Laboratory, TJU. His majorresearch interests include neural engineering, rehabilitation engineering, sportsscience, biomedical instrumentation, and signal/image processing, especially infunctional electricalstimulation, gait analysis, and braincomputer interface. Hehas also managed more than ten national and international research projects, or-ganized and hosted several international conferences as the Session Chair or theTrack Chair more than the last ten years and was the General Chair of the 2012IEEE International Conference on Virtual Environments, HumanComputer In-terfaces and Measurement Systems. Furthermore, he has been an International

Advisory Board Member of theThe Foot, and the Editorial Committee MemberofActa Laser Biology Sinica, and International Journal of Biomedical Engi-neeringin China.

Dr. Ming is the Chair of IEEE-EMBS Tianjin Chapter.

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