Accurate Real-Time Feedback of Surface EMG
During fMRI
G. Ganesh, D. W. Franklin, R. Gassert, H. Imamizu and M. Kawato

J Neurophysiol 97:912-920, 2007. First published Sep 27, 2006; doi:10.1152/jn.00679.2006
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J Neurophysiol 97: 912–920, 2007.
First published September 27, 2006; doi:10.1152/jn.00679.2006.

Innovative Methodology

Accurate Real-Time Feedback of Surface EMG During fMRI
G. Ganesh,1,2 D. W. Franklin,1,2 R. Gassert,3 H. Imamizu,2 and M. Kawato2
1

National Institute of Information and Communication Technology, Kyoto; 2Advanced Telecommunication Research InstituteComputational Neuroscience Laboratories, Kyoto, Japan; and 3Laboratory of Robotic Systems, Ecole Polytechnique Fédérale deLausanne, Lausanne, Switzerland
Submitted 6 June 2006; accepted in final form 24 September 2006

The advent of functional brain imaging modalities such asfunctional MRI (fMRI) and PET has allowed us to determine
the specific brain areas involved in the production and control
of motor tasks (Diedrichsen et al. 2005; Schaal et al. 2004;
Shadmehr and Holcomb 1997). However, to fully understandmotor control, it is critical to combine fMRI with techniquessuch as EMG, which give an estimate of the level of muscle
activation. By examining the changes in muscle activity during
adaptation to novel tasks, a deeper understanding of the mechanisms behind learning is obtained (Franklin et al. 2003b; Osu
et al. 2003). The use of EMG to estimate the muscle activation
is important because the brain does not simply control the force
at the hand but also regulates the impedance of the limb during
interactions with the environment (Burdet et al. 2001; Franklin
et al. 2003a; Hogan 1984). Brain activation therefore is not
only related to external variables such as kinematics or handforce, but is also related to other factors such as muscle force
or muscle activation (Scott 2003; Todorov 2000). Furthermore,
the acquisition of real-time EMG during functional imaging
would allow for the accurate control of experiments using
EMG feedback (Gomi and Osu 1998; Osu and Gomi 1999) inthe scanner. Better monitoring and control of individual muscle
activation would greatly increase the variety of motor experiments possible during fMRI and the reliability of the resultsfrom these experiments. For example, this could enable experiments separating control of force from control of muscle
co-contraction.
However, recording EMG data during fMRI is a technological challenge. Radio frequency (RF) pulses and magnetic field
gradients, used during the imaging for excitation and spatial
localization of the protons, induce large imaging artifacts in
any EMG leads. Additionally, motor control studies may involve limb movements that result in movement artifacts as the
EMG wires move in the magnetic field. It is necessary to
suppress all of these artifacts to obtain measurable EMG fromrecordings during scanning. We examined the various methods
proposed in literature to remove fMRI artifacts from EMG, aswell as electroencephalography (EEG) signals. Most of theseare postprocessing methods that are unsuitable for real-time
artifact removal because they are either computationally expensive or require large averaging periods. Our objective is todevelop a signal filtering system that is robust, computationallyinexpensive, simple to implement, and enables good estimationof real-time EMG during fMRI.
Because of the large artifacts induced during scanning, one
common strategy has been to completely avoid EMG recording
during scanning. Several studies repeat the experiment outside
the scanner to collect EMG, assuming that the EMG would be
similar as when inside the scanner (Ehrsson et al. 2001;
MacIntosh et al. 2004; Milner et al. 2005). Other studies use
intermittent recording of EEG (Baudewig et al. 2001; Goldman
et al. 2000) and EMG (Liu et al. 2000, 2002; Tanaka et al.
2004) during short intervals in between scans when the noise islow. This technique is able to provide a good estimate of EMG
in experiments where the muscle activity is maintained at aconstant level for a long time. However, if the muscle activity
is intermittent, it becomes necessary to align the stimuli with
the intervals in the scanning. This may be possible for fMRI
experiments with a “blocked design,” but for “event-related
designs,” requiring sparse sampling, alignment may reduce thepopulation covered by the data points.
Among off-line processing methods, use of average artifact
waveform subtraction is probably the most popular (Allen et al.
2000; Moosmann et al. 2003; Van Duinen et al. 2005). Thismethod, however, requires de-synchronization of the stimuli
and brain scans and a long averaging period, making on-line
implementation difficult. High-frequency EEG acquisition dur-

Address for reprint requests and other correspondence: G. Ganesh, Computational Neuroscience Lab., ATR International, 2-2-2 Hikaridai, Seika-cho,
Soraku-gun, Kyoto 619-0288, Japan (E-mail: gganesh@atr.jp).

The costs of publication of this article were defrayed in part by the paymentof page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

INTRODUCTION

912

0022-3077/07 $8.00 Copyright © 2007 The American Physiological Society

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Ganesh G, Franklin, DW, Gassert R, Imamizu H, Kawato M.
Accurate real-time feedback of surface EMG during fMRI. J Neurophysiol 97: 912–920, 2007. First published September 27, 2006;
doi:10.1152/jn.00679.2006. Real-time acquisition of EMG duringfunctional MRI (fMRI) provides a novel method of controlling motor
experiments in the scanner using feedback of EMG. Because of theredundancy in the human muscle system, this is not possible fromrecordings of joint torque and kinematics alone, because these provide
no information about individual muscle activation. This is particularly
critical during brain imaging because brain activations are not only
related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is
corrupted by large artifacts induced by the varying magnetic fields andradio frequency (RF) pulses in the scanner. Methods proposed inliterature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We
describe an acquisition system and algorithm that enables real-time
acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated.
Although this decreases the power content of the EMG, this method
provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, likeactual EMG, very well correlated with joint torque and can thus be
used for real-time visual feedback during functional studies.

Innovative Methodology
REAL-TIME FEEDBACK OF EMG DURING fMRI

913

signal is cut off during acquisition. Movement artifacts are induced
because of the movement of the conductive loop formed by theelectrodes and the neighboring wires in regions where the magnetic
field is not uniform and caused by the change in the apparent loop areawith the motion of the wires. The conductive loop is essentially fixed
to the subject limb by the electrodes. Therefore as long as the wires
are firmly fixed, the movement artifacts are in general concentrated
around frequencies contained in the movement tasks, which rarely
exceed 5 Hz. (In our present task, the artifacts were found to be below
3 Hz.) Presuming the movement artifacts are not large enough tosaturate the input channels, they are removed effectively by high passfiltering of the data above 20 Hz.
During fMRI using gradient-echo EPI, the RF pulse and thegradient fields are applied repetitively to acquire image slices of thebrain, with one complete brain volume (scan) acquired every repetition time (TR). Because of the nature of the scanning protocol, which
repeats for every image slice, the imaging artifacts also repeat with afrequency equal to the slice acquisition frequency. The image sequence repeats every 98.6 ms (10.2 Hz) corresponding to the slice
acquisition time (for a TR ⫽ 5 s). The sequence is very repeatable
except for short periods in between every scan. A major chunk of thepower in the noise is thus contained in the harmonics of the slice
acquisition frequency (Fig. 1). The noise can thus be effectively
reduced by removing these particular frequencies from the data
(Hoffmann et al. 2000). We propose a linear comb filter for thispurpose (see Filter implementation), which is easy to implement,
computationally inexpensive, and can be thus implemented for realtime noise cancellation. Temporally concentrated interscan artifacts
left over after the comb filtering operation are removed using a simple
zero order hold on the output.
The signal is band-pass filtered between 20 and 250 Hz before
passing into the comb filter. High-pass filtering above 20 Hz helps toremove movement artifacts and any temporal drifts present in thesignal. Low-pass filtering below 250 Hz preserves most of the useful
EMG data while reducing digitization errors, which increase withfrequency and lead to clipping of signal peaks.

METHODSIn our setup, EMG was recorded from four muscles acting at thewrist [flexor carpi radialis (FCR), flexor carpi ulnaris (FCU), extensor
carpi radialis longus (ECRL), and extensor carpi ulnaris (ECU)]. Afterelectrode placements for each muscle were determined using functional movements, the area was cleansed with alcohol and abrasive gel(Nuprep, D.O. Weaver and Co.). EMG electrodes designed for use in
the MR environment (NE-706A, Nihon Kohden) were filled with EEGelectrode paste (Biotach, GE Marquette Medical Systems) and firmly
fixed to the subjects skin with tape. Two electrodes were positioned on
the belly of each muscle separated by ⬃1 cm. An elastic cloth sleeve
was placed over the electrodes and wires, fixing them against the

The artifacts induced in the EMG lines can be classified as RF pulse
artifacts, gradient field artifacts, and movement artifacts. The artifacts
caused by the RF field (42.58 MHz/T for our scanner) are of muchhigher frequency than EMG and can be removed by low-pass filtering.
In our case, we choose to use a separate RF filter to ensure that the RF
noise does not saturate the input channels of the EMG amplifier.
Gradient artifacts are induced by the gradient magnetic fields usedduring imaging for spatial encoding. The frequency range of thegradient field artifacts covers the entire EMG spectrum and thus
cannot be removed by a low-pass filter and form the major part of theimaging artifacts in the EMG lines. The amplitude of the induced
artifacts is given by Faraday’s Law as
V max ⫽

冉冊dB
⫻Adt maxRatio of total power in
20 -250 Hz range

Theory

EMG

0.08

0.04

where Vmax is the peak amplitude of the artifact, B is the magnetic
field, and A represents the loop area. The maximum field gradient forour scanner is (dB/dt)max ⫽ 27 Ts⫺1 at a slew rate of 72 Tm⫺1s⫺1.
Taking a conservative estimate for the loop area as 10 cm2, we get amaximum gradient artifact of 54 mV (pk-pk) in the channels. In
comparison, the amplitude of raw EMG is a few millivolts. Even
though the noise magnitude is considerably higher than the raw EMG
signal, the significant fact is that it is well below the input range of 500
mV of our EMG amplifier, which ensures that no part of the noise0
10.2 Hz

50

100

150

200

250

Frequency [Hz]FIG. 1. Plot of power against frequency shows that noise is concentrated infrequencies in the multiple of 10.2 Hz, which corresponds to the slice
acquisition frequency of our scanner during a scan of TR ⫽ 5 s. As weband-pass the incoming signal between 20 and 250 Hz before comb filtering,
power has been expressed as a ratio of total power in this frequency range.

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ing fMRI is possible by customizing the echo-planar imaging(EPI) sequence to create short artifact-free periods (Anami etal. 2003). However, the customized and complex acquisition
system reported still requires average artifact removal after
acquisition before a clean signal is obtained. Other computationally intensive off-line methods include subtraction of an
artifact template in the frequency domain (Sijbers et al. 1999)
and temporal principle component analysis (Michiro et al.
2004).
A real-time artifact removal system reported for EEG (Garreffa et al. 2003) uses initial recordings of the MR artifact todetermine the waveform of the EPI artifact for subtraction from
subsequent EEG data. However, the higher frequency content
of EMG, which requires precise signal synchronization, andchanges in the artifact over time, could produce particular
problems in adapting this technique for EMG recording.
It has been shown that the major part of the artifacts during
fMRI using gradient-echo EPI are concentrated in particularfrequencies and can be effectively reduced by removal of thesefrequencies from the signal (Hoffmann et al. 2000). However,the proposed method relies on postprocessing and has beenused specifically to detect epileptic spikes in EEG that allow
low-pass filtering below 40 Hz. We extend this method forreal-time artifact removal in EMG that has a much larger
frequency band (20 –250 Hz) and hence larger artifact content.
We propose an acquisition setup and a simple comb filter
algorithm that enable real-time artifact removal from EMG
during fMRI scanning. Comparisons with the EMG obtained
from our algorithm with the noise-free EMG obtained in theabsence of image artifacts show that the cleaned EMG is a very
good estimate of the actual EMG. The proposed algorithm canwork for both block and event-related fMRI without requiring
any stimuli synchronization with the scanner or changes in thescanning protocol.

Innovative Methodology
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GANESH, FRANKLIN, GASSERT, IMAMIZU, AND KAWATO

Filter implementation
Let f(k) represent the kth real-time observation of the noisy signal
after band-pass filtering between 20 and 250 Hz. f(k) is the summationof the EMG signal e(k) and EPI artifact, n(k)f共k兲 ⫽ e共k兲 ⫹ n共k兲(1)

The comb filter is implemented by a pth order difference equation andis represented in time space aso共k兲 ⫽

共1 ⫺ Bp兲
f共k兲
2

(2)

where o(k) is the output of the algorithm and B represents the
backward shift operator such that Bpf(k) ⫽ f(k ⫺ p). If ts is the
sampling time interval, f(k ⫺ p) represents value of f, observed pts
seconds before f(k). The value of p would be determined by the slice
acquisition time ta and sampling time ts as p ⫽ ta/ts. Substituting fromEq. 1
o共k兲 ⫽

共1 ⫺ Bp兲
共1 ⫺ Bp兲关e共k兲兴 ⫹关n共k兲兴
2
2(3)

Because the artifacts are concentrated in the multiples of the slice
acquisition frequency (wa ⫽ 2␲/ta) and the artifact waveform repeats
with a time period equal to the slice acquisition time ta, we have n(k)⫽ n(k ⫺ p). Therefore the second term in Eq. 3 tends to zero leading
to artifact removal from the EMG signal. The effect of the filter on
EMG signal e(k) is similar and EMG frequencies that are multiples of
wa are subjected to a gain of zero. The gain on the intermediate
frequencies varies between zero and one (Fig. 2A).
The phase lag introduced by the filter is also shown in Fig. 2B.
The phase lag, however, is not of major concern to us because the

FIG. 2. Bode plot of comb filter. A: the simple comb filter implemented has
a rectified sinusoidal gain profile. Frequencies in multiples of wa are subjected
to a gain of 0. Maximum gain of 1 is achieved on frequencies of (2n – 1)wa/2.
B: phase lag induced by filter.

cleaned EMG is rectified and integrated over a large moving
window that is more than twice the maximum time lag introducedby the filter.

Cleaning and visual feedback
Even though the scanner noise is largely repetitive, the cycle isbroken for a short period between scans. The artifacts during thisinterscan period are no longer concentrated in particular frequencies.
Thus these interscan artifacts are not removed by the comb filter.
However, these artifacts are temporally concentrated for a period of⬃100 ms after each scan. By monitoring the sequence-related triggerof the MR system, it is possible to determine the exact period after
every scan when this noise is present. To remove the interscan
artifacts, data from the comb filter is subjected to a zero-order hold fora period of 200 ms after each scan. The hold value is taken as theaverage of the absolute cleaned EMG from a short period before thehold. While a shorter averaging period ensures better extrapolation of
the current muscle activity, the chosen averaging period should belong enough to have an adequate number of data points to get a
reliable average value. An average of 50 ms was chosen for our
experiment, but a different period length may be chosen according to
the experiment task. The cleaned EMG attained after passing throughthe comb filter, followed by interscan artifact removal, can now besmoothed as required for visual display. In our present setup (Fig. 3),
we used a 500-ms moving average to obtain the EMG profile,corresponding to previous psychophysical on-line feedback studies(Gomi and Osu 1998; Osu and Gomi 1999). The EMG profile ismonitored by the host PC at 20 Hz and can be used for real-time EMG
feedback to the subject.

Validation experimentThree healthy male subjects (23, 26, and 33 yr of age) participatedin the experiment to validate the EMG cleaning algorithm. The
institutional ethics committee approved the experiments and subjects
gave informed consent before participation. A one degree of freedom
MR compatible interface (Gassert et al. 2006) was used to restrain thesubject’s forearm and wrist to an isometric posture using a plastic
splint and straps while the subject contracted his muscles (Fig. 4).
This device also contained a custom MR-compatible optical torquesensor that was used to collect wrist joint torque during the experiment.
The experiment consisted of isometric wrist contractions in both theflexion and extension direction during scanning. The subject’s hand
was firmly fixed to the handle of the MR compatible manipulandum.

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subjects forearm to avoid any accidental electrode removal and tominimize movements of the electrode wires during the scanning. Oncethe subject was positioned in the scanner, the long braided electrodewires were firmly fixed to prevent movement in the magnetic field. To
avoid external noise being carried into the shielded MR room, theelectrode wires were passed through multiple ferrite filters before
passing through wave guides in the penetration panel of the MR room.
The ferrite filters doubled up as filters for the high-frequency RF noise
induced in the electrodes. The RF filtered signals were band-passfiltered between 20 and 250 Hz (⫺6 dB/octave, low-pass; ⫺12
dB/octave, high-pass) and amplified using a Nihon Kohden filteramplifier (MME-3132). The gain of each channel was adjusted toaccommodate the complete noisy signal within the input range of theamplifier. The amplifier also contained a 60-Hz AC filter that could beswitched on if required to remove any noise from the power-line used.
All EMG equipment except for the electrodes and their wires werelocated outside of the scanning room.
Our primary concern was for the safety of the subjects during theexperiments. The RF and gradient fields can induce large currents inthe conducting loop formed by the subject, electrode wires, andamplifier. This was prevented by the large input impedance of ouramplifier (⬃100 M⍀), narrow gauge of the MR electrode wires, andlow-pass filtering at every input. Local capacitive loops between theelectrode wires were prevented by the low capacitance (0.5 nF) of the
MR-compatible electrodes wires. Furthermore, specially designed
webbings on the MR electrodes helped to decrease eddy currents andprevent heating of electrodes during experiments.
After amplification, the signal was digitized at 5 kHz using a 12-bit
AD converter (on a National Instruments PCI 6024E DAQ card). The
artifact removal algorithm was implemented on a computer runningxPC target real-time system. xPC Target environment uses a target
PC, separate from a host PC, for running real-time applications. In our
experiments, the target PC was used to run the cleaning algorithm
while the host PC handled the visual display to the subjects.

Innovative Methodology
REAL-TIME FEEDBACK OF EMG DURING fMRI

915

FIG. 3. Block diagram of setup used to acquire real-time EMG during functional MRI (fMRI). Raw EMG signal collected during scanning is contaminatedwith imaging artifacts. The raw EMG is radio frequency (RF) filtered and passed into a commercial EMG amplifier where it is band-pass filtered between 20
and 250 Hz, AC filtered to remove noise from power supply, and amplified before being passed into the artifact removal program through a data acquisition(DAQ) board. A pulse sequence acquired from the scanner (fMRI pulse) is also fed in through the DAQ board to synchronize the program with the scanner. Comb
filter and interscan artifact removal are implemented at 5 kHz. Cleaned EMG acquired after interscan artifact removal is rectified and averaged over a 500-ms
moving window to obtain the EMG envelope for visual display.

fMRI
A 1.5-T MR scanner (Shimadzu-Marconi ECLIPSE 1.5T Power
Drive 250) was used to obtain blood oxygen level– dependent

FIG. 4. Arm fixation during validation experiment. Subject’s forearm andwrist were fixed to the handle of the MR compatible manipulandum using
Velcro straps and a hard plastic splint. An elastic cloth sleeve was put oversubject’s forearm to prevent accidental removal of electrodes and minimize
wire movement during experiment. Axis of the MR compatible torque sensor(located below subject hand) was aligned with axis of rotation of the wrist.
Subject performed isometric flexion and extension contractions of the wrist in
this setup.

(BOLD) contrast functional images. Images weighted with the apparent transverse relaxation time were obtained with an gradient-echo
EPI sequence. Data were collected at two different repetition times of1) TR ⫽ 2.5 s with echo time, 49 ms; flip angle, 80°, 25 slices(thickness 4 mm, gap 2 mm) of 64 ⫻ 64 in plane voxels (in-plane fieldof view of 224 mm2) and 2) TR ⫽ 5 s with echo time, 50 ms; flip
angle, 90°, 50 slices (thickness 3 mm) of 64 ⫻ 64 in plane voxels(in-plane field of view of 224 mm2).
In a control session, additional fMRI images were collected in theabsence of the EMG setup but with similar imaging parameters. The
control session was conducted immediately after one of the EMG
sessions to ensure that a direct contrast between the brain images was
possible. Data collected in the EMG and control sessions wererealigned to the first volume of the EMG session, normalized to theMontreal Neurological Institute (MNI) reference brain, and smoothedusing an 8-mm full-width half-maximum Gaussian kernel. The Tcontrasts (P ⬍ 0.05, FDR corrected, 0-voxel threshold) of the active
and rest phases in each of the two sessions was performed andcompared with observations reported in literature for wrist muscle
activations. A T-contrast (P ⬍ 0.001, uncorrected, 0 voxel threshold)of the brain images from the active phases of the EMG and control
sessions was conducted to check for possible imaging artifacts causedby the EMG setup.

Analysis
To evaluate the accuracy of the algorithm, analysis of the cleaned
EMG data was performed and compared with data recorded in theabsence of fMRI scanning. The goal of this technique was to produce
an accurate on-line estimate of the EMG during the scanning. Therefore the critical test of the data are whether the cleaned EMG exhibits
good correlation with actual artifact-free EMGs. However, a direct
correlation between the two integrated EMG signals was not possible
because there can be differences in the force levels and muscle
activation patterns between trials. Second, because the power of the
EMG signal is reduced by the filtering in the cleaning algorithm, theoverall level of the muscle activity after cleaning is lower than what
is recorded without MR scanning. Thus we choose to check thecorrelation between the EMG signals by correlating them individuallywith the corresponding signals from the joint torque sensor because
this is invariant to the MR scanning. EMG has been shown to increase
as a function of joint torque in either a linear or nonlinear fashion
depending on the muscles studied (Gottlieb and Agarwal 1971;
Lawrence and De Luca 1983; Milner-Brown et al. 1973; Woods andBigland-Ritchie 1983). For smaller distal muscles and lower joint
torque or force levels, this relationship is generally found to be linear,although at higher forces, for more proximal muscles the EMG can
increase at a faster rate than the muscle force. In this study, we wished
to confirm that the cleaned EMG is a good estimate of the underlying
motor command. In particular, we tested this by examining whether
the cleaned EMG scales with joint torque during isometric contrac-

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Subjects were presented with visual feedback of the torque applied tothe manipulandum in the form of a torque bar whose length anddirection indicated the magnitude and direction of the applied torque.
One of three flexion and three extension target torque levels (⫾7 Nm
maximum) were displayed in a random order every 10 s. The subject
was instructed to slowly increase the torque bar to the target level in⬃1–2 s and hold it throughout the target display period. Once a newtarget appeared, subjects initially relaxed for a period of 1 s and thenslowly produced enough torque to match the new torque target. Eachtarget level was presented four times during one session. All experiments were performed both with and without functional imaging.
These two experiments were performed one following the other,
without moving the subject or electrodes so that a direct comparisonof the EMG signal can be made. A short rest period of 3– 4 min wasprovided between experiments to ensure that the subject was not
fatigued.
A big advantage of this technique for obtaining an on-line measureof EMG compared with other methods such as interleaved EMG
recording (Tanaka et al. 2004) is that this technique is able to obtain
information about continuously changing EMG levels throughout thescanning. To show this ability, an additional session was performed
with one subject, where the subject was asked to make quick flexionand extension wrist torques in the isometric condition without maintaining a constant force level as in the previous experiment. The
torque values in this session were constantly changing and usuallylasted ⬃3 s before changing directions, for example, from flexion toextension. Data from this experiment were used to check the algorithm’s performance for higher activation frequencies.

Innovative Methodology
916

GANESH, FRANKLIN, GASSERT, IMAMIZU, AND KAWATORESULTS

Before cleaning was performed, the EMG signal largely
consisted of imaging artifacts from the scanner (Fig. 5A). After

passing through the comb filter, a majority of the image
artifacts were removed. The EMG can already be seen in thesignal (Fig. 5B), but it is still corrupted by interscan artifacts.
The interscan artifacts were concentrated over a period of⬃100 ms and repeated after time intervals corresponding to the
TR of the scan. The signal after removal of the interscan
artifacts (Fig. 5C) looks very similar to the artifact-free (nofMRI) EMG (Fig. 5D). Figure 5E shows the corresponding
torque profile over the same time period. On zooming in to asmaller time interval (Fig. 5F), the cleaned and actual EMG
still look similar but at this scale, the lower density of thecleaned EMG, because of removal of part of the EMG power,
is clearly visible. Also visible at the higher magnification is the
200-ms hold used to remove the interscan artifacts. The lower
density of the cleaned signals leads to a lower integrated value
for the cleaned EMG in comparison with the actual EMG (Fig.
5G).
The mean EMG was compared against corresponding joint
torque. The correlation coefficients r2 for the cleaned EMG and
no-fMRI EMG for all the subjects and muscles are given in
Table 1. In one subject, the ECU muscles showed very low
signal to noise ratio in recordings without scanning and wastherefore not recorded during scanning. The plot of mean EMG
against torque for one flexor and one extensor muscle for each

FCU

10 m VA

FCU

0.2 m V

ECRL

B

0.2 mV

ECRL
FCU

0.2 mV

ECRL

D

FCU

EMG [V]

C

2 Nm

0

1 sec

0

0

0.3 mV1 Nm

0.2 mVzero order holdmean FCU EMG [V]torque [Nm]with fMRI
no- fMRIG

no- fMRI

with fMRI

10 secF
FCU EMG [V]torque [Nm]Emean EMG
torque
1 sec

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FIG. 5. Comparison of cleaned and nofMRI EMG. A: signal in FCU channel after
the application of band-pass filter of 20 –250
Hz is contaminated by artifacts. B: 2 EMG
channels: 1 flexor (FCU) and 1 extensor(ECRL) for subject 1, after passage throughthe comb filter shows EMG profile clearly.
EMG is still contaminated by interscan artifacts, which are seen as thick vertical lines in
the figure, repeat every 5 s, corresponding to
the TR of this session. C: the same channels,
after removal of interscan artifacts, show
very good similarity to (D) EMGs collectedin the absence of scanning over the sametime period. E: torque value from torquesensor. Note that extensor and flexor muscle
activities correspond to positive and negative
torque values, respectively. F: time-magnified view of flexor carpi radialis (FCR) activity after artifact removal compared withactivity of the same muscle in the absence of
scanning shows the 200-ms hold period(highlighted) used to remove interscan artifacts. However, cleaning decreases power
content of EMG signals, which is apparent in
G, where a 500-ms moving average is applied on rectified EMG signals.

Downloaded from jn.physiology.org on April 30, 2007

tions was similar to EMG recorded without the MR scanning. If both
exhibit a linear relation with the joint torque, we can conclude that thecleaned EMG is well correlated to the actual muscle activity. A
500-ms moving window was used to calculate the integrated, rectified
EMG. The mean integrated value over 2.5 s at the beginning of eachsession when no torque was applied was used to estimate the zerooffset. This offset was subtracted from the integrated EMG values.
This integrated EMG value was compared with the measured wrist
torque every 250 ms for EMG recorded with and without fMRI
scanning.
The data for each muscle were plotted against joint torque andlinear regression against joint torque was performed for flexion andextension directions separately. The correlation coefficient (r2) wasused to determine the extent to which the EMG measure was correlated with joint torque for sessions both with and without scanning.
Best-fit lines were calculated using linear regression to examine theway in which the muscle activity scaled with joint torque.
For graphing purposes, the data for each muscle were collected acrossall of the trials. The EMG values were separated into 12 equally sizedbins depending on the joint torque level recorded at that time. For eachbin, the mean and standard deviation of the data were determined andplotted at the location of the mean joint torque for the bin.

Innovative Methodology
REAL-TIME FEEDBACK OF EMG DURING fMRI
TABLE 1. Results of correlation between mean muscle activity andjoint torques for three subjects

r2
FCU

ECRL

FCR

ECU

NoNoNoNoCleaned fMRI Cleaned fMRI Cleaned fMRI Cleaned fMRI
Subject EMG EMG EMG EMG EMG EMG EMG EMG
1
2
3

0.816
0.798
0.718

0.916
0.857
0.955

0.791
0.914
0.763

0.918
0.913
0.914

0.742
0.667
0.483

0.795
0.806
0.807

0.642
—
0.470

0.798
—
0.889

Values are r2. FCU, flexor carpi ulnaris; ECRL, extensor carpi radialis
longus; FCR, flexor carpi radialis; ECU, extensor carpi ulnaris.

a smaller torque range (⫾1.5 Nm) was also used with two of
the subjects. Over this smaller torque range, the cleaned EMG
still shows a linear response to joint torque with large r2 values(Fig. 6, B and C). To show that the algorithm works forscanning with different TR, an additional session was conducted for subject 1 with a TR ⫽ 2.5 s and two muscles (FCUand FCR). This gave (r2cleaned; r2no fmri) values of (0.773;
0.916) and (0.473; 0.640), respectively. For the same subject,muscles ECU and ECRL were recorded during the session withhigher frequency activations of ⬃1–1.5 Hz and gave r2 valuesof (0.637; 0.796) and (0.832; 0.928), respectively. Again boththe no fMRI EMG and the cleaned EMG exhibited linear
relations with joint torque and had large r2 values. Figure 7A
shows the torque and the integrated cleaned EMG profile for
the ECRL muscle during this session. The torque EMG regression for the same muscle is shown in Fig. 7B.
Experiments performed in the control session, in the absenceof the EMG setup, showed similar brain activations as in thesessions with EMG (Fig. 8, A and B). Activation was observedin parts of the left motor cortex including M1 and the premotor
regions, SMA and in the right cerebellum, in agreement withthe literature on activation of the muscles in the right hand
(Kawashima et al. 1995; Milner et al. 2005). Furthermore, the6030

r2=0.916
r2=0.867

40

20

20

10

0

0

-20
-10

-5

0

5

-10
10

16

80

ECRL
60

12

r2=0.918
r2=0.791

40

8

20

4

0

0

-20
-10

Joint Torque [Nm]

-5

0

5

EMG [µV] (fMRI)

FCU

EMG [mV] (no fMRI)

40

80

EMG [µV] (fMRI)

EMG [mV] (no fMRI)

A

-4
10

Joint Torque [Nm]

0

0

1

40

40
30

40

0

0

-20
-1

0

1

2

Joint Torque [Nm]

FCU

15

r2=0.995
r2=0.718

10

20
5
10
0

0

EMG [mV] (no fMRI)

50

r2=0.913
r2=0.914

20

Joint Torque [Nm]

C

60

20

-20
-2

2

ECRL

30

ECRL

15

20

r2=0.914
r2=0.763

10

10

5

0

0

-10
-2

-1

0

1

Joint Torque [Nm]

2

-2

-1

0

1

2

Joint Torque [Nm]J Neurophysiol • VOL 97 • JANUARY 2007 • www.jn.org

EMG [µV] (fMRI)-1

EMG [mV] (no fMRI)

0

EMG [µV] (fMRI)

5

10

-10
-2

EMG [mV] (no fMRI)

r2=0.857
r2=0.798

20

60

10

FCU

EMG [µV] (fMRI)

EMG [mV] (no fMRI)

30

EMG [µV] (fMRI)

B

FIG. 6. Cleaned EMG scales with joint
torque like normal EMG. A: EMG recorded
without scanning (thick grey trace) andcleaned EMG recording during scanning(thin black trace) plotted as a function ofwrist joint torque for subject 1. Wrist flexor(FCU; left) increases activity as wrist flexion
increases, whereas wrist extensor (ECRL;
right) increases activity as the extensor
torque required is larger. Muscle activity has
been separated into 2 groups depending onwhether the joint torque was an extensor
torque or a flexor torque. Each group was
fitted by a line using linear regression (leastsquares error). Resulting best-fit lines areshown with solid lines. Error bars indicate
SD of equal-sized groups of actual EMG
values. Above each plot is the r2 value
shown only for the region where the muscle
is active (flexion region for flexor muscles;
extensor region for extensor muscle). B: datafrom the same muscles for subject 2 and (C)subject 3 show that linearity of mean EMG
with joint torque is maintained even for low
levels of joint torques.

Downloaded from jn.physiology.org on April 30, 2007of the three subjects is shown in Fig. 6. Clearly both thecleaned EMG and the EMG recorded without scanning exhibited linear relationships with either the negative or positive
torque depending on the action of the muscle, thus showing
good correlation between cleaned and the no fMRI (actual)
EMG. The initial trials were performed with large target torquerange (⫾7 Nm) and therefore large muscle activity. To confirm
that the method works well over all ranges of muscle activity,917

Innovative Methodology
918

GANESH, FRANKLIN, GASSERT, IMAMIZU, AND KAWATO

4

120

2

60

0

0

-2
-4
-6

0

40

80

120

80

r2=0.928
r2=0.832

60
40

0

0

-5

5

-4
10

Our work shows that a simple comb filter can provide
real-time noise reduction in EMG signals during fMRI scanning with minimum complication. Comparison with artifactfree EMG collected in the absence of scanner noise showed
good similarity between the EMG profiles between the two,even when viewed over a shorter time period (Fig. 5, C, D, and
F). The cleaned integrated EMG linearly scaled with torque,similar to EMG recorded without scanner noise (Fig. 6), hence
also showing that cleaned EMG has a good correlation to theunderlying muscle activation over a wide range of levels. This

A

B

C

left

right

Brain activity during EMG acquisition. Contrast of brain activity
during the active and rest phases (P ⬍ 0.05, FDR corrected) of a subject in (A)the session with EMG and (B) in the control session without the EMG setup
shows very similar activity. Activations were observed in parts of the left M1,
premotor, SMA, and right cerebellum corresponding to observations reported
in literature. C: contrast of the active phases of the 2 sessions gave nosignificant activity even at a low threshold of P ⬍ 0.001, uncorrected, 0 voxel
threshold. This showed that EMG setup does not indicate any artifacts in fMRI
images.

FIG. 7. Cleaning technique can handle quickly
changing EMG activity. A: subject was instructed
to make quickly changing isometric flexion andextension torques (thick grey trace). Cleaned EMG
was recorded, and the 500-ms moving window ofintegrated EMG was calculated during recording(thin black trace). ECRL muscle is an extensor
muscle so it was only active when an extensor
torque (positive joint torque) was produced. B:
integrated EMG plotted against joint torque forthese fast contractions. Cleaned EMG (thin black
trace) scales with joint torque exactly as does normal EMG (thick grey trace) recorded without scanning. Data are plotted as in Fig. 6.

was further supported by evidence that the algorithm can evendetect activity during weak contractions (ⱕ0.3 Nm). These
properties suggest that the cleaned EMG resulting from thealgorithm can be used for providing EMG feedback during
fMRI experiments.
In our present prototype system, we used a simple linear
filter for noise removal. The simplicity of the filter makes it
easy to implement, computationally inexpensive, and easy torealize for real-time applications. However, the gain profile offilter removes parts of the EMG spectrum (Fig. 2), leading toa loss of ⬃35% of the EMG spectrum in theory. Thus forexperiments involving EMG spectral analysis, other off-line
cleaning methods are recommended. In the future, nonlinear
comb filters may be implemented with sharper gain profiles toreduce the spectrum loss considerably. On the other hand, evenwith the simple linear filter, the output cleaned EMG looks
very good because of the fact that the EMG power spectrum isrelatively smooth and there are no sudden changes in the power
content across neighboring frequencies. Thus even with theloss of a relatively large percentage of the spectrum thealgorithm is able to capture the EMG waveform well. The loss
in power is apparent in the y-axis labels of the results figureswhere the integrated cleaned EMG is always less than thenormal EMG for a given level of joint torque. However, this issimply a scaling down of the EMG level and does not affect theability to detect the muscle activity itself. The variability of the
EMG, as indicated by the SD of the values (Fig. 6), is similarboth with and without scanning, relative to the mean value of
the EMG.
Even though it was expected that the ratio of the slopes of
the cleaned and no fMRI EMG would be same for all channels,
this was found not to be so in practice (Fig. 6). This was
because, although the major part of the noise is concentrated in
the slice frequency harmonics and is removed by the algorithm
completely, there is some noise present in the frequencies
adjacent to the harmonic frequencies that is attenuated but notcompletely removed. This residual noise varies within channels depending on their individual spatial placements and leads
to differences in the slopes of the integrated cleaned EMG in
Fig. 6. However, the noise, and hence the slopes, remains thesame as long as the position of the subject was not changed, as
seen from Fig. 6A and Fig. 7B, where the same muscle isplotted in two different sessions.
The order of the digital filter is extremely important for thegood performance of the algorithm. This order is determinedby the slice acquisition time (ta ⫽ TR/number of slices) andhence the time period of the noise cycle. Commonly specified
TR values are usually rounded off to the nearest half second.

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Downloaded from jn.physiology.org on April 30, 2007

DISCUSSION

FIG. 8.

0

Joint Torque [Nm]contrast of the brain images from the active phases of the EMG
and control sessions gave no significantly active voxels even ata low threshold (P ⬍ 0.001, uncorrected, 0-voxel threshold;Fig. 8C). Thus it was concluded that the EMG system iscompatible with the scanner and does not induce any significant noise in the brain images.

ventral8
4time [s]dorsal

12

20

-20
-10

160

16

ECRL

EMG [µV] (fMRI)

180

EMG [mV] (no fMRI)

B
6

Integrated EMG [µV]

Joint Torque [Nm]A

Innovative Methodology
REAL-TIME FEEDBACK OF EMG DURING fMRI

muscle activation varies. Control of individual muscle activation can therefore increase the variety of motor experiments
possible during fMRI and increase the reliability and repeatability of the results obtained. Using the proposed setup andalgorithm, we have shown for the first time that real-time
acquisition of EMG signals during fMRI is possible, opening
up a new experimental design paradigm in the MR scanner: theconduction of motor experiments that use feedback of muscle
activity to control the actions of the subject.
ACKNOWLEDGMENTS

We thank Dr. Theodore Milner at Simon Fraser University and Drs.
Masahiko Haruno and Eiichi Naito at Advanced Telecommunication ResearchInstitute for valuable comments.
GRANTSThis study was supported in part by the National Information and Communications Technology (NICT-KARC) and by grants to M. Kawato from the
Human Frontier Science Program (HFSP).
REFERENCES

Allen PJ, Josephs O, Turner R. A method for removing imaging artifact fromcontinuous eeg recorded during functional mri. Neuroimage 12: 230 –239,
2000.
Anami K, Mori T, Tanaka F, Kawagoe Y, Okamoto J, Yarita M, Ohnishi
T, Yumoto M, Matsuda H, Saitoh O. Stepping stone sampling for
retrieving artifact-free electroencephalogram during functional magnetic
resonance imaging. Neuroimage 19: 281–295, 2003.
Baudewig J, Bittermann HJ, Paulus W, Frahm J. Acquiring simultaneouseeg and functional mri. Clin Neurophysiol 112: 1196 –1200, 2001.
Burdet E, Osu R, Franklin DW, Milner TE, Kawato M. The central nervous
system stabilizes unstable dynamics by learning optimal impedance. Nature
414: 446 – 449, 2001.
Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R. Neural correlates ofreach errors. J Neurosci 25: 9919 –9931, 2005.
Ehrsson HH, Fagergren A, Forssberg H. Differential fronto-parietal activation depending on force used in a precision grip task: an fmri study.
J Neurophysiol 85: 2613–2623, 2001.
Franklin DW, Burdet E, Osu R, Kawato M, Milner TE. Functionalsignificance of stiffness in adaptation of multijoint arm movements to stableand unstable dynamics. Exp Brain Res 151: 145–157, 2003a.
Franklin DW, Osu R, Burdet E, Kawato M, Milner TE. Adaptation tostable environments achieved by combined impedance control and inverse
dynamics model. J Neurophysiol 9: 3270 –3282, 2003b.
Garreffa G, Carni M, Gualniera G, Ricci GB, Bozzao L, Carli DD,
Morasso P, Pantano P, Colonnese C, Roma V, Maraviglia B. Real-time
mr artifacts filtering during continuous eeg/fmri acquisition. Magn Reson
Imaging 21: 1175–1189, 2003.
Gassert R, Moser R, Burdet E, Bleuler H. MRI/fMRI-compatible robotic
system with force feedback for interaction with human motion. IEEE/ASME
Trans Mechatron 11: 216 –224, 2006.
Goldman RI, Sternb JM, Engel J Jr, Cohen MS. Simultaneous eeg andfunctional mri of epileptic activity: a case report. Clin Neurophysiol 111:
1974 –1980, 2000.
Gomi H, Osu R. Task-dependent viscoelasticity of human multijoint arm andits spatial characteristics for interaction with environments. J Neurosci 18:
8965– 8978, 1998.
Gottlieb GL, Agarwal GC. Dynamic relationship between isometric muscle
tension and the electromyogram in man. J Appl Physiol 30: 345–351, 1971.
Hoffmann A, Jager L, Werhahn KJ, Jaschke M, Noachtar S, Reiser M.
Electroencephalography during functional echo-planar imaging: detection of
epileptic spikes using post-processing methods. Magn Reson Med 44:
791–798, 2000.
Hogan N. Neural, mechanical and geometric factors subserving arm posture in
humans. J Neurosci 4: 2745–2754, 1984.
Kawashima R, Itoh H, Ono S, Satoh K, Furumoto S, Gotoh R, Koyama M,
Yoshioka S, Takahashi T, Yanagisawa T. Activity in the human primary
motor cortex related to arm and finger movements. Neuroreport 6: 238 –240,
1995.
Lawrence JH, De Luca CJ. Myoelectric signal versus force relationship in
different human muscles. J Appl Physiol 54: 1653–1659, 1983.

J Neurophysiol • VOL 97 • JANUARY 2007 • www.jn.org

Downloaded from jn.physiology.org on April 30, 2007

However, because of the preparation time taken by somescanners, the actual TR during scanning is 60 –70 ms (70 ms
for our scanner) less than the specified TR value. This leads toa difference of 2–3 ms in ta. Because of the large magnitude of
the artifacts, this small error can reduce the signal to noise ratioof the cleaned EMG significantly. We thus provide the possibility of fine-tuning the filter order on-line during EMG collection.
The interscan artifact are removed using a zero order holdfor every 200 ms between scans. EMG during this period is
estimated from the previous 50 ms. Again this method wasused in the prototype system because it is simple to implement.
However, more accurate methods like mean waveform subtraction may be used for this period because the frequency
content of the interscan artifact is low. Furthermore, during amajor part of the interscan period, the artifacts are negligible,and clean EMGs can be obtained without any artifact filter (Liuet al. 2000, 2002). The RF artifact magnitude varied between
channels, possibly because of the positioning of the electrode.
In some of the channels, an additional zero-order hold during
the period when the RF noise is present (in our setup, the 1st
25 ms of a slice acquisition) improved the signal to noise ratio
significantly, especially when the EMG values were low.
Movement artifacts are induced by the movement of theelectrodes and wires in the magnetic field. In isometric experiments, both single and multi-joint, these artifacts originate
from the small movement of the skin and the limb on which theelectrodes are fixed and are generally small in magnitude.
Among non-isometric experiments involving limb movements,
wrist and finger experiments produce less artifacts as there islittle movement of the forearm where the electrodes are fixed.
However, in the case of multi-joint movements, artifacts can belarger in magnitude and frequency. To minimize movement
artifacts, EMG wires should be fixed and run along limbs andthe fMRI bed without forming any loops near the scanner. The
workspace of the experiment should be made as small as
possible and should be located as close as possible to the axisof the bed and the center of the scanner bore, where themagnetic field is more uniform. Generally speaking, the movement frequency content in most of the motor tasks is ⬍5 Hz
and a high pass cut-off of ⬎20 Hz should be sufficient toremove movement artifacts. However, for motor tasks involving sudden obstacles and perturbations, a higher cut-off frequency may be chosen if required. Note that the algorithm isable to remove the movement artifacts provided the input
channels are not saturated. An EMG amplifier with a large
input range is thus preferred for multi-joint movement studies.
Even though we collect EMG in a 1.5-T scanner, present day
experiments often use EPI at 3-T for human subjects. However
because EPI principally involves repetitive application of magnetic fields, the noise is always concentrated in the harmonics
of the slice acquisition frequency of the scanner. Thus withproper tuning of the filter order, the algorithm can be effectively used for stronger scanners as well.
Visual feedback of the EMG can enable monitoring andcontrol of individual muscles during motor experiments. Because of the redundancy in the human muscle system, this is
not possible from recordings of joint torque and kinematics
alone, which contain no information about muscle co-contractions. Proper control of muscle activation is critical during
brain imaging, because brain activation is likely to change as919

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GANESH, FRANKLIN, GASSERT, IMAMIZU, AND KAWATO

Liu J, Daib TH, Elsterb TH, Sahgal V, Brown RW, Yue GH. Accessory
hardware for neuromuscular measurements during functional MRI experiments. Magn Res Mat Phys Biol Med 13: 164 –171, 2000.
Liu J, Zhang L, Yao B, Yue GH. Simultaneous measurement of human joint
force, surface electromyograms, and functional mri-measured brain activation. J Neurosci Methods 101: 49 –57, 2002.
MacIntosh BJ, Mraz R, Baker N, Tam F, Staines WR, Graham SJ.
Optimizing the experimental design for ankle dorsiflexion fmri. Neuroimage
22: 1619 –1627, 2004.
Michiro N, Abildgaard M, Nixon T, Constable RT. Removal of timevarying gradient artifacts from eeg data acquired during continuous fmri.
Clin Neurophysiol 115: 2181–2192, 2004.
Milner TE, Franklin DW, Imamizu H, Kawato M. Central representation of
dynamics when manipulating handheld objects. J Neurophysiol 95: 893–
901, 2005.
Milner-Brown HS, Stein RB, Yemm R. The orderly recruitment of human motor
units during voluntary isometric contractions. J Physiol 230: 359 –370, 1973.
Moosmann M, Ritter P, Krastel I, Brink A, Thees S, Blankenburg F,
Taskin B, Obrig H, Villringer A. Correlates of alpha rhythm in functional
magnetic resonance imaging and near infrared spectroscopy. Neuroimage
20: 145–158, 2003.
Osu R, Burdet E, Franklin DW, Milner TE, Kawato M. Different mechanisms involved in adaptation to stable and unstable dynamics. J Neurophysiol 9: 3255–3269, 2003.

Osu R, Gomi H. Task-dependent viscoelasticity of human multijoint arm andits spatial characteristics for interaction with environments. J Neurophysiol
81: 1458 –1468, 1999.
Schaal S, Sternad D, Osu R, Kawato M. Rhythmic arm movement is notdiscrete. Nat Neurosci 7: 1137–1144, 2004.
Scott SH. The role of primary motor cortex in goal-directed movements:
insights from neurophysiological studies on non-human primates. Curr Opin
Neurobiol 13: 671– 677, 2003.
Shadmehr R, Holcomb HH. Neural correlates of motor memory consolidation. Science 277: 821– 825, 1997.
Sijbers J, Michiels I, Verhoyle M, Van Auderkerke J, Van der Linden A,
Van Dyck D. Restoration of mr-induced artifactsin simultaneously recorded
mr/eeg data. Magn Reson Imaging 17: 1383–1391, 1999.
Tanaka Y, Noda S, Tsuji T. A virtual prosthetic hand using EMG signals for
fMRI measurements. International Conference on Intelligent Robots andSystems IROS, September 28 –October 2, 2004, Sendai, Japan.
Todorov E. Direct cortical control of muscle activation in voluntary arm
movements: a model. Nat Neurosci 3: 391–398, 2000.
Van Duinen H, Zijdewind I, Hoogduin H, Maurits N. Surface emg measurements during fmri at 3t: accurate emg recordings after artifact correction. Neuroimage 27: 240 –246, 2005.
Woods JJ, Bigland-Ritchie B. Linear and non-linear surface EMG/force
relationships in human muscles. An anatomical/functional argument for the
existence of both. Am J Phys Med 62: 287–299, 1983.

Downloaded from jn.physiology.org on April 30, 2007

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