Simultaneous EEG–fMRI acquisition: how far is it from being a standardized technique?

Magnetic Resonance Im

Simultaneous EEG–fMRI acquisition: how far is it from being a

standardized technique?

Girolamo Garreffaa,b, Marta Bianciardib,c,d, Gisela E. Hagbergc, Emiliano Macalusoc,

Maria Grazia Marciania,e, Bruno Maravigliab,d,*, Manuel Abbafatie, Marco Carnıb,d,

Ivo Brunif, Luigi Bianchia,c,e

aLaboratorio Neurofisiopatologia, IRCCS Fondazione S. Lucia, 00179 ROMA, ItalybEnrico Fermi Center, 00184 Rome, Italy

cLaboratorio Neuroimmagini, IRCCS Fondazione S. Lucia, 00179 ROMA, ItalydDip. Fisica, Universita di Roma bLa Sapienza,Q 00185 ROMA, Italy

eDip. Neuroscienze, Universita di Roma bTor Vergata,Q ItalyfEBNeuro S. p. A., 50127 Firenze, Italy

Received 29 October 2004; accepted 29 October 2004

Abstract

Simultaneous EEG–fMRI is a powerful tool to study spontaneous and evoked brain activity because of the complementary advantages of

the two techniques in terms of temporal and spatial resolution. In recent years, a significant number of scientific works have been published

on this subject. However, many technical problems related to the intrinsic incompatibility of EEG and MRI methods are still not fully solved.

Furthermore, simultaneous acquisition of EEG and event-related fMRI requires precise synchronization of all devices involved in the

experimental setup. Thus, timing issue must be carefully considered in order to avoid significant methodological errors.

The aim of the present work is to highlight and discuss some of technical and methodological open issues associated with the combined

use of EEG and fMRI. These issues are presented in the context of preliminary data regarding simultaneous acquisition of event-related

evoked potentials and BOLD images during a visual odd-ball paradigm.

D 2004 Elsevier Inc. All rights reserved.

Keywords: EEG; fMRI; Artefacts; Synchronization; Simultaneous acquisition

1. Introduction

The first EEG equipment designed for combined use

with MR was employed in 1993 [1] and the first clinical

fMRI applications were made in 1996 [2]. Ever since, a

great deal of work has been devoted to EEG–fMRI studies

because of the unique possibility to obtain measurements

with high spatial and temporal resolution simultaneously.

The technical problems that arise from the integration of the

two methods are many fold and can be classified in the

following categories: magnetic field-related effects (B0

effects); EEG artefacts induced by gradient switching and

radio-frequency (RF) pulses (MRI dynamic conditions); and

0730-725X/$ – see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.mri.2004.10.013

* Corresponding author. Dipartimento di Fisica, Universita di Roma

bLa Sapienza,Q P.le Aldo Moro, 5 CAP: 00185 Rome, Italy. Tel.: +39

0649913473, +39 064454859; fax: +39 0649913484.

E-mail address: [email protected] (B. Maraviglia).

timing issues related to protocols and devices (synchroni-

zation). Furthermore, other problems concern the electric

noise induced by the MR environment. These include

mechanically induced artefacts (MIA) like disturbances

from the magnet cold head pump and acoustic noise caused

by the operating gradient coils. These typically cause

vibrations on scanner structures and consequently electric

guitar-like pick-up effects on the wires and electrodes

placed in the magnetic field. Materials, geometrical and

electrical properties of the wiring and of the EEG electrodes

must thus be considered in order to optimize safety [3],

electrophysiological and MRI conditions.

In the present work we first describe these methodolog-

ical issues and then examine some experimental data,

discussing possible solutions. Furthermore, we report

preliminary results concerning simultaneous acquisition of

evoked potentials and BOLD imaging during event-related

visual stimulation.

aging 22 (2004) 1445–1455

G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–14551446

2. Outstanding issues for simultaneous

EEG–fMRI acquisition

2.1. Artefacts in EEG recordings

2.1.1. MRI pulse sequence artefact

MRI pulse sequence artefact (PSA) consists in strong

perturbations of EEG signals due to the time-varying

magnetic field gradients. Ives et al. [1] and Huang-Hellinger

et al. [4] described the induction of electromotive forces

related to wire loops perpendicular to the direction of the

field gradient. These forces are proportional to the residual

cross-sectional area of wires loop and to the slew rate of

system gradients. The gradients switch and the RF pulses

induce a critical electric noise that obscures all the EEG

traces. A detailed analysis of PSA, based on a priori

knowledge of MRI sequence parameters, can be used to

plan any artefact-removing procedure. The estimation of the

MRI sequence parameters in the raw EEG data can help to

develop analytical expressions of the artefact. For this

purpose the EEG data require acquisition at high sampling

rate. The analytical definition of PSA can in turn be used as

a reference (template) for filtering [5,6].

2.1.2. Quantum noise of the analogue-to-digital converter

The PSA is a signal that has a larger dynamic range

(usually several millivolts) than the EEG signal (tens to

hundreds of microvolts). This implies that the parameters

for EEG acquisition during combined EEG–fMRI have to

be different from those utilized outside the MR scanner in

order to avoid saturation of the analogue-to-digital con-

verters (ADCs). As a consequence of this, the digital

resolution power of the ADC will decrease, thus leading to

an increase of the quantum noise.

2.1.3. Ballistocardiogram artefact

The ballistocardiogram artefact (BCA, see Fig. 1) relates

to cardiac activity and it represents a relevant source of

noise to the EEG recordings [7–9].

The amplitude may exceed 150 AV at 1.5 T field

strength which is between one and two orders of magnitude

greater than most event-related potential (ERP) signals.

Moreover, the BCA varies across subjects and across time,

Fig. 1. Ballistocardiogram artefact on a

as does cardiac activity. Its frequency range is mainly in the

theta (4 –8 Hz) band [7], but extends to the alpha (8–13 Hz)

and delta (0.5–4 Hz) bands, thus overlapping with the

EEG/ERP signals. For this reason, BCA removing cannot

simply employ band-pass or reject-band filters. Instead the

shape of the BCA signal needs to be estimated and

subtracted from the raw EEG recordings. However, the

efficacy of these methods depends on many factors and it is

still debated. Some authors [10] do not use any BCA

removal procedure as they do not consider the discrimina-

tion power of these methods adequate for ERP signals,

which are one or two orders of magnitude smaller than the

usual EEG signal.

Allen et al. [7] and Benar et al. [11] described two

different methods to remove BCA artefacts. Both methods

resulted adequate for applications aimed at identifying

single epileptic spikes, with peak amplitudes in the order of

10 –150 AV. Allen et al. described a method that subtracts

an averaged BCA waveform calculated for each electrode

during 10 s previous to MRI scanning. On the other hand,

Benar et al. [11] used a filtering method based on principal

and independent components analysis. Another possibility

is to actually measure the BCA artefact in a location that

does not interfere with the full EEG recording. For

instance, Bonmassar et al. [12] proposed the use of a

piezoelectric motion transducer positioned on the tempo-

rary artery enabling simultaneous removal of both motion

artefacts and BCA. Kim et al. [13] described an adaptive

filter that was tested on experimental data of alpha waves

that dominate conventional EEG and epileptic EEG.

Finally, Ellingson et al. [14] proposed a method that was

applied in the simultaneous acquisition of auditory ERPs

and fMRI with results that were comparable to those

obtained outside the scanner, but combined recording

required a great number of trials to be averaged (N250).

2.2. ERP–fMRI synchronization issues

2.2.1. Stimulation timing errors

Devices typically used to present stimuli during EEG

and fMRI experiments have an intrinsic timing uncertainty

due to hardware and software reasons. For example, we can

consider a Microsoft Windows XP-based system that

9-s section of an EEG recording.

G. Garreffa et al. / Magnetic Resonance Imaging 22 (2004) 1445–1455 1447

provides visual or acoustic stimuli for evoked related

potential studies by means of a secondary monitor or an

audio card. Microsoft Windows XP, as well as Linux, OS-

X and many other operating system (OS), is not a real-time

OS. This implies that when the stimulation software sends a

command to the OS, for example, to draw an image to the

screen or to play an audio file, it is generally not possible to

know exactly when the command is executed and when the

stimuli are delivered. This generates two kinds of timing

errors: systematic and random errors, which have different

consequences on acquired and processed data. Usually,

systematic errors affect the overall accuracy of the measure

while random errors tend to produce filtering effect, when

several repetitions of the same stimulus type are averaged.

Timing errors are introduced when the delays introduced by

all the equipment of the experimental setup are not

accurately measured or taken into account. This includes,

for example, transmission and processing delays introduced

by serial ports, video and audio cards, electronic circuits, as

well as context switch in multithreading environments of

modern operating systems.

Systematic timing errors can be easily subtracted if

accurately measured. A typical problem occurs if one

assumes that all the parts of the screen are drawn simulta-

neously: drawing a video frame requires time so that the top

part of the screen is usually drawn before the bottom one

(about 16 ms in the case of a video refresh rate of 60 Hz). If

stimuli are displayed on different part of the screen this

should be taken into account.

Random timing errors have an impact on averaging

techniques, which are employed to increase the signal-to-

noise ratio (SNR) in both fMRI and ERP. These techniques

can be applied provided that the timing of the stimuli and

the acquired data are known. Usually, fMRI and ERP use

Fig. 2. Stimulation designs employed. (A) In Design 1, a sparse sampling scheme w

(B) Stimuli were presented both during MR acquisition and during delay betw

respectively). Time was kept invariant between stimulation types (15 min).

different devices to acquire physiological data and to present

the stimuli to the volunteer, requiring some coordination

among the devices. However, if this is not properly taken

into account, the so-called trigger jitter (TJ) may occur.

Possible sources of this effect are examined below and some

solutions are proposed.

2.2.2. ERP–fMRI design optimization

Two design options are available whenever acquiring

both ERP and fMRI data on a subject inside an MR scanner:

simultaneous and interleaved.

Whenever a perfect filtering of the MPSA is unavail-

able, interleaved acquisition is necessary to measure event-

related EEG responses in the presence of the static

magnetic fields and time-varying magnetic field gradients.

In this context the different time resolution of fMRI and

EEG offers an important advantage: the cerebral-evoked

response to stimulation is observable at different and

usually nonoverlapping times, with longer delays for the

hemodynamic response, thus enabling interleaved acquisi-

tion of the two signal types. In order to avoid effects due to

gradient and RF switching during EEG recordings, time

delays are introduced in the MRI acquisition and hence the

interstimulus interval (ISI) will increase. For the measure-

ment of BOLD activation patterns, such an approach will

inevitably lead to reductions in detection power, and hence

less sensitivity toward the BOLD effect.

3. Data acquisition and analysis

Having discussed the most important methodological

issues concerning simultaneous EEG–fMRI recording, we

now turn to the description of these effects in the

experimental context and we consider possible solutions.

as used, with a resulting number of rare (frequent) stimuli equal to 25 (100).

een volumes (total number of rare/frequent stimuli equal to 60 and 240,

Fig. 3. Initial MRI pulse sequence (EPI) frame of 25 ms where it is possible

to see that the PS events timing correspond to the first part of PSA (this is

also true for the remaining part of PS).


3.1. Paradigm

Four healthy subjects (mean age, 28 years) gave their

written informed consent to participate in the study, which

was approved by the local ethics committee. Three subjects

underwent fMRI-EEG while performing a visual odd-ball

task (see below). The aim of the study was to describe any

interaction between the EEG and the fMRI systems (see

artefacts described in the previous section) and to optimize

the acquisition of simultaneous EEG–fMRI, comparing two

different acquisition protocols (Fig. 2).

In the first protocol we used a long fMRI repetition time

(TR=9.5 s) and presented visual stimuli only in the gaps

between MR volumes (i.e., mean intertrial interval=9.5 s).

In the second protocol we used a shorter TR of 5.5 s and we

presented two visual stimuli for each TR (i.e., mean

intertrial interval=2.75 s). The fourth subject underwent

EEG at rest (eyes closed, no task) inside MR scanner,

providing further data on BCA and MIA.

Fig. 4. Pulse sequence artefact relative to the Pz electrode acquired over 209 m

two slices.

3.2. EEG acquisitions

A standard portable 40-channel digital EEG amplifier

(Mizar, EBNeuro, Florence, Italy) was adapted to operate

inside the MR room. Conic Ag-AgCl electrodes prear-

ranged on a cap of a magnetic material were filled with

conductive gel and placed on the scalp of the participants

prior to positioning in the bore of the MR scanner. The

positioning of the electrodes was performed according to

the 10/20 scheme. The reference electrode was positioned

on the AFz position (according to the 10/10 scheme)

whereas the ground was positioned halfway between Fz

and Cz.

The EEG recording unit was placed inside a shielded

box to eliminate any RF disturbance on the MR images.

The unit amplified the signal and performed A/D conver-

sion and multiplexing. The digital signal was transferred via

optical-fiber connection to a host computer (outside the

magnet room) for de-multiplexing, data acquisition, pro-

cessing and storage.

3.3. MRI acquisitions

A Siemens Vision Magnetom MR system (Siemens

Medical Systems, Erlangen, Germany) operating at 1.5 T

and equipped for echo-planar imaging (EPI) was employed

for acquiring functional MR images. A circular polarized

volume head coil was used for RF transmission and

reception. Depending on the experimental paradigm, either

97 (the long TR protocol) or 164 (short TR protocol)

BOLD image volumes were acquired, subdivided in 20

planes, starting from the vertex and stretching caudally.

Each plane was excited with a 908 slice selective RF pulse

followed by echo-planar blipped trapezoidal readout

gradients (bandwidth, 1953 Hz/pixel). The sequence

parameters were duration of acquisition of 22 slices 2 s,

TR 9.5 or 5.5 s, echo time 60 ms, matrix 64�64, field of

view 256 mm, slice thickness 4 mm and gap between

slices 0.8 mm. At the end of each volume, a trigger pulse

was sent from the MR scanner to the PC for synchroni-

zation with EEG and stimulus presentation. Two initial

s at a sampling rate of 8192 Hz and corresponding to the acquisition of


BOLD images were discarded from further analysis to

remove any possible T1 saturation effects.

3.4. Analysis of EEG data

EEG data were analyzed with the Galileo Back Averag-

ing ERP Package (EBNeuro) for the PSA mean artefact

computation and with the Galileo Spectral Cartooning And

Coherence 2003 (by Brainware for EBNeuro) for spectral

analysis. No pre-processing was performed on the data (e.g.,

filtering) unless specified.

Fig. 5. Power spectrum of the PSA on the Pz electrode. (A) Full band. (B) The

amplitude modulation: power peaks are regularly repeated with different amplit

carrier corresponds to the 656 Hz peak. (C) The horizontal axis is zoomed in th

beta bands.

3.5. Analysis of fMRI data

Image analysis was performed by SPM2 (Wellcome

Department of ImagingNeuroscience, Institute of Neurology,

London) and included correction for motion during scanning,

brain normalization to Montreal Neurological Institute

coordinates, sinc interpolation, spatial smoothing by a

Gaussian isotropic kernel (full-width at half maximum=6

mm) and temporal filter (high-pass cut-off: 1/128 s plus a

whitening filter based on autocorrelation estimation

of the residuals). Statistical parametric mapping was then

horizontal axis is zoomed in the range 550–750 Hz to show the artefact

udes every 9.5 Hz, which corresponds to the modulation frequency. The

e EEG-ERP band. In this case the periodical peaks overlap the alpha and

Fig. 6. Effect of a low-pass filter on the PSA peak. The cut-off frequency is on the horizontal axis while the PSA peak amplitude is on the vertical one. More

selective filters reduce the PSA peak.


performed by a fixed-effects analysis including three

subjects, for each design separately, using a statistical

threshold of Pb.001.

Table 1

Effects of the averaging on the BCA

Trials Mean abs.

(AV)Peak abs.

(AV)S.D.

(AV)Power

(AV2/s)

1 24.7F4.9 113F17 31.9F5.0 (104F39)�10

2 17.7F2.8 71F12 22.43F3.3 (51F16)�10

4 12.8F2.1 48.2F8.9 16.0F2.5 261F88

8 9.0F1.4 32.7F6.3 11.2F1.7 129F41

16 6.4F1.0 22.6F4.4 8.0F1.2 65F21

32 4.53F0.73 15.8F3.2 5.62F0.86 32.F10

64 3.21F0.51 11.0F2.3 3.98F0.60 16.2F5.1

128 2.27F0.36 7.9F1.6 2.81F0.42 8.1F2.5

256 1.61F0.26 5.6F1.1 2.00F0.30 4.1F1.3

512 1.15F0.19 4.09F0.83 1.42F0.22 2.08F0.68

1024 0.83F0.14 2.95F0.58 1.02F0.16 1.07F0.35

2048 0.60F0.10 2.16F0.41 0.74F0.12 0.56F0.19

4096 0.45F0.08 1.62F0.29 0.55F0.09 0.31F0.10

8192 0.34F0.06 1.25F0.21 0.42F0.07 0.19F0.06

Effects of the averaging on the BCA. Each data (average and S.D.) are

computed from 100,000 simulations obtained by averaging 1 s of signal

randomly selected from an EEG recording. The first column represents the

number of trials that have been averaged, the second column the mean

absolute value of the averaged signal, the third column its peak absolute

value, the forth its S.D. and the fifth the power.

4. Results and discussion

In this section we first consider different types of artefacts

induced during simultaneous EEG and fMRI measure-

ments. Then we discuss timing issues related to combined

EEG–fMRI acquisitions, including the optimization of TR

and ISI for a visual odd-ball study.

4.1. Artefacts in EEG recordings

4.1.1. MRI PSA

Fig. 3 shows a diagram of the first 25 ms of the MR pulse

sequence (see Data Acquisition and Analysis section). Fig. 4

shows the PSA induced by two slices acquired using this

MRI pulse sequence on the EEG traces. The sampling rate

of the EEG was 8 kHz.

The effects shown in Fig. 4 can be predicted by

comparing the detailed pulse sequence events scheme. This

approach can be used to fully identify and describe the

EEG artefacts due to gradients switching effects in MRI

sequence [5,6].

Fig. 4 also shows an amplitude peak modulation well

visible on frequency-encoding gradient effect. This is due to

the absence of synchronization with EEG sampling [6].

Furthermore, the frequency-encoding block (in multislice

single-shot EPI) can be described as a periodical signal

modulated in amplitude by a square wave. In the frequency

domain this will result in spectral components that are

distributed according to some related pulse sequence

parameters. Part of these spectral components may overlap

with electrophysiologic frequencies of interest, and this

must be taken into account when selecting experimental

designs and acquisition protocols (e.g., in steady-state EP/

fMRI study). Fig. 5 shows spectral analysis of 1 s of the

EEG data, with different zooming windows.

Spectral components are determined by a square wave

modulation (corresponding to encoding frequency block) on

a periodic signal (readout switching gradient). Fig. 5C

displays the EEG band in the low-frequency region of the

spectrum. In our data, the frequency of the blocks was

approximately 9.5 Hz and the carrier frequency can be

estimated with the expression:

Mx

2NspDro

where Mx is equal to the effective image matrix size, Nsp is

the number of sampling points on each readout gradient

inversion and Dro the readout gradient encoding duration.

Fig. 6 shows that low-pass progressive filtering reduces

the amplitude of the MRI artifact on the EEG traces and this

means that almost all the energy of the artefact is distributed

in the high frequencies (see also Fig. 5).

Fig. 7. Effect of the averaging on the mean absolute value of the BCA: increasing the number of trials to be averaged reduces its mean amplitude. Simulations

(100,000) were performed for each of the 14 different averaged trials by randomly extracting a section of 1 s from an EEG signal affected by the

ballistocardiogram.


As expected, a more selective filter (lower cut-off

frequency) reduces the peak amplitude of the PSA. In

particular, a 4-kHz LPF causes the peak PSA amplitude to

be in the order of 50 mV, while filtering the same signal at

200 Hz reduces the peak PSA amplitude to 4.6 mV. In the

most common cases (EEG, ERP), a 200-Hz LPF can be

applied without affecting frequencies of physiological

interest. In some cases, which depend on the EEG

acquisition device, this allows a reduction of the analog to

digital input dynamic range and, as a direct consequence, of

the quantization noise (see below).

4.1.2. Quantum noise of the ADC

The typical digital resolution of ERP acquisition outside

the MR scanner allows for a dynamic range of F4 mV

(quantum size equal to 0.125 AV) at 16 bits. Instead, ERP

acquisition during fMRI requires higher dynamic ranges,

thus, reducing the digital resolution (here the dynamic range

Fig. 8. Five seconds of an EEG recording in which the cold head of magnet refrigerator system artefact is visible: a fast oscillation (48 Hz) is modulated in

amplitude (2 Hz). Ballistocardiogram is also visible.

was F65 mV; quantum size equal to 2 mV). This

corresponds to a loss of 4 bits (i.e., a 24-dB attenuation).

Averaging techniques (e.g., ERP) may reduce such effect

depending on the number of averaged data since the

quantum noise is divided by the number of averaged trials.

Likewise, digital filtering (e.g., low-pass FIR filters) easily

remove part of this noise.

4.1.3. Ballistocardiogram artefact

Generally, in ERP studies, classical averaging methods

can reduce any uncorrelated source of noise by a factor offfiffiffin

p, where n is the number of averaged trials. Here,

simulations were performed to verify if this was the case

for the BCA. We considered a data segment of 60 s

(corresponding to 491,520 samples) from the Pz electrode

during rest (eyes closed, no task) inside the bore of the MR

scanner, but without any fMRI acquisition (see Fig. 1). The

EEG data were analyzed after removing the mean value. The

Fig. 10. Trigger jitter transfer function effect for a jitter uniformly

distributed in the range of 0–16.6 ms.


S.D. of the signal was 31.8 AV, the mean power was 1040

AV2/s and its mean rectified value was 24 AV. For 100,000times, randomly chosen portions of 1 s of signal were

averaged over a number of trials of 2^N (N=0, 1,. . ., 13). For

each of the 1,400,000 computed averages the mean of the

rectified signal, the S.D. (an estimator of the signal power),

the power and the peak of the rectified signal were computed.

Their mean and S.D. values are illustrated in Table 1.

As expected, all these parameters decreased as the

number of averaged trials increased. Fig. 7 illustrates how

the mean of the rectified signal decreases with increasing

number of averaged trials. As can be noted, a large number

of trials have to be combined in order to minimize the BCA

at levels close to the expected ERP amplitude.

4.1.4. Mechanically induced artefacts

Examination of the EEG traces revealed undue signal

components. Fig. 8 shows these periodic oscillations in the

EEG signal that may be mechanically induced by the cold

head of magnet refrigerator system.

This effect can be described in terms of amplitude-

modulated mechanical oscillations (48 Hz) superimposed

onto the EEG signal. An FFT analysis of this signal (Fig. 9)

highlights the typical spectrum of an amplitude-modulated

signal which results from the convolution of the carrier (50

Hz) and the modulator (2 Hz) frequencies.

The 2 and 50 Hz are, respectively, related to the

operating rate of the control piston and the rotary valve

activated by means of an electric synchronous motor that

are part of the magnet cold head. The lower side band

(46 – 48 Hz) is enhanced probably because of the acoustic

resonance determined by drum-like effect of the control

piston on magnet room that has, in our case, one

dimension corresponding to the 48-Hz acoustic wave-

length. These mechanical effects induce vibrations of the

scanner structures that are in contact with the EEG wiring

determining further noise on EEG traces (microphone

effect). Further and similar effects may also be determined

by gradients switching-related vibrations induced inside

the scanner (patient bed, external/internal scanner surfa-

ces, etc.).

Fig. 9. Power spectrum of the Pz electrode signal of Fig. 7 in which the

carrier frequency (48 Hz) and the modulation (2 Hz) effect are visible.

4.2. ERP–fMRI synchronization issues

4.2.1. Stimulation timing errors

The TJ effect is a temporal shift that propagates

differently across successive acquisitions. If the acquisition

and the stimulation are not synchronized this problem

occurs and averaging methods are not able to correlate the

evoked response and the stimulation accurately.

The main consequence of TJ is a filtering effect on the

averaged data. The characteristics of the filtering will

depend on the TJ distribution (TJD). The averaged data

will be a convolution of the TJ bfreeQ data with the TJD.

There are many possible sources of TJ in fMRI/ERP

paradigms that can arise at different stages of the record-

ing/stimulation apparatus.

4.2.1.1. Stimulators. Two technical limitations may induce

TJ effects: the first regards the frame rate of the video card

employed. When a command is sent to the video card to

draw something on the screen, the latter can be updated only

when a video frame is redrawn. If the operating frame rate

of a video card is 60 Hz this occurs every 16.6 ms and the

TJD will be uniform in the interval (0–16.6 ms). The

frequency response and the power spectrum of the TJD for

this case are illustrated in Fig. 10.

The plots show that there is a complete loss of data at

frequencies that are integer multiples of the frame rate of the

video card (60, 120, 180 Hz, . . .) and that there is a loss of 3

dB (the components are divided by two) or more at a

frequency of 36.6 Hz or higher. The second limitation is that

if a non-real-time OS is used as stimulation platform it is not

possible to know in a deterministic way when the OS

actually delivered the stimulus to the subject. This is another

source of TJ filtering effect whose consequences are

difficult to be determined a priori.

4.2.1.2. Synchronization among devices. fMRI and EEG

systems acquire and process signals with different timing

characteristics and they are designed with different timing

specifications. For this reason averaging data with a

uniform TJD in the range 0 –100 ms is adequate for most


fMRI studies but inadequate for almost all EEG/ERP

measurements. This problem increases when fMRI and

ERP acquisitions must be synchronized. Depending on the

design of the experiment and the characteristics of the

acquisition systems some additional delay may also be

introduced. For example, if the visual stimulation is

triggered by some MR device signals one should measure

the jittering of this trigger to verify its compatibility with

ERP studies.

In many cases, a way to solve all timing error issues is

to acquire a representation of the stimulus. This allows

performing synchronous averaging related to the brealQstimulus presentation time, instead of the bdesiredQ stimulus

time. In a visual task this can be done by means of a

photocell positioned on the surface of the video screen and

directed inside it: below the photocell and out of the field

of view of the subject (e.g., on a corner of the video

screen), a small white rectangle is drawn at the same time

the stimulus is drawn, otherwise that video area is black.

The acquisition of the signal coming by the photocell gives

a reasonable measure about when the stimulus was

effectively drawn.

4.2.2. ERP–fMRI design optimization

In the present work, we first adopted a sparse sampling

scheme with a TR of 9.5 s and hence with a 7.5-s-long

delay before start of next volume in order to obtain

a sufficiently long-lasting, undisturbed EEG trace. This

long delay was also necessary to allow jittering of the onset

of stimulus presentation with respect to the MRI acquisi-

Fig. 11. Hemodynamic response for Designs 1 and 2 (red line) and sampling points

only the maximum and minimum BOLD response is sampled in Design 1, Design

for the rare events, but at the cost of a reduced BOLD response (around 50% of De

number of available sampling points for the frequent events is to introduce the s

tion, thereby a full sampling of the hemodynamic res-

ponse across all brain slices could be achieved (see Fig. 2,

Design 1, and Fig. 11 for the sampling points of hemo-

dynamic response).

The expected parameter estimation efficiency can be

calculated according to:

PE¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1

Hð ÞþððHÞþÞT

s

where H is the ideal hemodynamic response obtained by

assuming a linear time-invariant (LTI) system, the + symbol

indicates the pseudoinverse and T the transpose operator.

Design 1 yielded a low parameter estimation efficiency,

being 0.6. Therefore, we aimed at optimizing this value.

Because of the LTI characteristics of the hemodynamic

response, an assumption that is valid at reasonably long

ISIs, an alternative paradigm that allowed full sampling of

the hemodynamic response despite a shorter TR, could be

adopted. For this second design, stimuli were presented

both during MR acquisition and during the delay between

volumes. In order to accommodate the EEG acquisition

after decay of the MR imaging gradients, this delay had to

be 3.5 s, thus yielding a TR of 5.5 s (Fig. 2, Design 2).

With Design 2, the parameter estimation efficiency for

detecting rare events increased twofold (1.26) despite a

slightly lower hemodynamic signal amplitude (Fig. 11:

sampling points of hemodynamic response). Both designs

were tested in the experimental setting by combined EEG–

fMRI acquisitions. In agreement with the predicted increase

for the frequent events (black dots) and the rare events (blue circles). While

2 offers a more complete coverage of the full hemodynamic curve, at least

sign 1). A possible way to overcome such signal reductions and increase the

o-called null events (i.e., absence of visual stimulation).

Fig. 12. Activation patterns from a fixed-effects analysis performed for

three subjects and two different experimental designs (voxel-level

significance P b.001, uncorrected for multiple comparisons). Clusters

revealed by Design 1, with a TR of 9.5 s, are shown in red while those

detected by Design 2 (TR=5.5 s) are shown in green. Due to a higher

efficiency of Design 2, brain areas related to visual perception of rare events

combined with mental calculation can be detected, for example, calcarine

cortex (upper row), bilateral BA6, supplementary motor area BA8 and

parietal lobe BA40 (lower row).


in parameter estimation efficiency, the higher detection

power of Design 2 also lead to more activated brain regions

for the rare events, for example, including the calcarine

cortex, a brain area that was expected to activate due to the

visual nature of the stimulus (Fig. 12, activation pattern), as

well as other brain areas supposedly involved in mental

calculation of the rare events.

Moreover, brain areas common to both techniques

contained more voxels within each activated cluster for

Design 2. In summary, Design 2, with shorter TR, and more

importantly shorter ISI, is obtained from Design 1 by

introducing additional stimuli during the MRI acquisition

window. This approach was motivated by the LTI character

of the hemodynamic response that permits detection of

cumulative effects of a series of stimuli. Design 2 lead to an

increased parameter efficiency including more trials within

the experimental run and furthermore allowed a tighter

sampling scheme of the BOLD response. In the experimen-

tal situation, a higher number of activated brain regions

could be detected. Design 2 could be further improved by

introducing so-called null events (i.e., absence of visual

stimulation) in the design, however, at the cost of longer

scanning times.

5. Conclusions

From our brief discussion it emerges that there are some

open questions that introduce a given number of variables

when EEG–fMRI (but particularly ERP/fMRI) experiment

is performed. The number and kind of these variables may

critically affect the performance of the experiment if an

accurate analysis of them is not done. A class of

unexpected effects came out from MIAs and a mechanical

decoupling between EEG cap and wires is recommended,

for example, an enclosure of wires from cap to EEG box

in a tubular soft case and locking it in same way.

Sometimes, additional timing constraints impose mutual

(EEG, fMRI) protocols restrictions that may limit the

effectiveness of the whole protocol (e.g., we have

discussed about different situations related to hemodynam-

ic response sampling vs. ISI).

Solutions are possible but up to now are frequently

customized to the particular protocol setup. For this reason,

it is not straightforward, from a technical point of view, to

adapt these solutions to different operative conditions.

Regarding the ballistocardiogram, from simulations and

from theory, it emerges that to reduce by one order of

magnitude its effects there are two nonmutually exclusive

choices: the increase of the number of trials and the

removal of at least a part of the ballistocardiogram. Both

strategies have, however, some drawbacks. Increasing the

number of trials is not always feasible, for example, to

obtain a P300 signal, 100 rare events, and consequently

400 frequent events, are needed to reduce BCA by one

order of magnitude, which comport an experiment more

than 45 min long, with an ISI of 5.5 s. Estimating and

subtracting the BCA is not a noiseless solution. Probably,

using both techniques will improve the SNR. Ballistocar-

diogram artefact is less predictable than PSA, even if the

amplitude is much lower. For steady-state visual evoked

potentials (SSVEP), one should carefully select the

stimulation rate and the sequence parameters in order not

to overlap amplitude modulation effects of the MRI scanner

with the physiological response (same rate as the SSVEP

stimulation). Setting acquisition parameters can affect the

accuracy of the system (higher SR means a better sampling

of the PSA but generally will reduce the SNR as the A/D

quantum noise will increase as its size). However, filtering

and averaging will reduce this problem. Averaging,

nevertheless, requires a perfect timing between stimulation

and acquisition; otherwise, there is the trigger-jitter

multiband filtering effect.

We also observed that a comfortable head lock together

with another mechanical decoupling may reduce the

amplitude of ballisto. This may be done by interposing

a soft material around the posterior electrodes between

scalp and inferior head coil support surface in order to

realize a sort of shock absorber of the heart beat-related

strengths that may induce an impulsive coherent motion.

This hard solution determines a preliminary reduction


of BCA that may be sufficient and useful in many cases

like continuous or spike-triggered EEG–fMRI study in

epilepsy [5].

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Simultaneous EEG–fMRI acquisition: how far is it from being a standardized technique?

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