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ORIGINAL RESEARCH
published: 10 March 2022
doi: 10.3389/fnins.2022.842420
Edited by:
Surjo R. Soekadar,
Charité – Universitätsmedizin Berlin,
Germany
Reviewed by:
Joao Miguel Castelhano,
University of Coimbra, Portugal
Vlastimil Koudelka,
National Institute of Mental Health,
Czechia
*Correspondence:
Mateusz Rusiniak
mateusz.rusiniak@besa.de
Specialty section:
This article was submitted to
Brain Imaging Methods,
a section of the journal
Frontiers in Neuroscience
Received: 23 December 2021
Accepted: 16 February 2022
Published: 10 March 2022
Citation:
Rusiniak M, Bornfleth H, Cho J-H,
Wolak T, Ille N, Berg P and Scherg M
(2022) EEG-fMRI: Ballistocardiogram
Artifact Reduction by Surrogate
Method for Improved Source
Localization.
Front. Neurosci. 16:842420.
doi: 10.3389/fnins.2022.842420
EEG-fMRI: Ballistocardiogram
Artifact Reduction by Surrogate
Method for Improved Source
Localization
Mateusz Rusiniak1*, Harald Bornfleth1, Jae-Hyun Cho1, Tomasz Wolak2, Nicole Ille1,
Patrick Berg1and Michael Scherg1
1Research Department, BESA GmbH, Gräfelfing, Germany, 2Bioimaging Research Center, World Hearing Center of the
Institute of Physiology and Pathology of Hearing, Warsaw, Poland
For the analysis of simultaneous EEG-fMRI recordings, it is vital to use effective artifact
removal tools. This applies in particular to the ballistocardiogram (BCG) artifact which
is difficult to remove without distorting signals of interest related to brain activity. Here,
we documented the use of surrogate source models to separate the artifact-related
signals from brain signals with minimal distortion of the brain activity of interest. The
artifact topographies used for surrogate separation were created automatically using
principal components analysis (PCA-S) or by manual selection of artifact components
utilizing independent components analysis (ICA-S). Using real resting-state data from
55 subjects superimposed with simulated auditory evoked potentials (AEP), both
approaches were compared with three established BCG artifact removal methods: Blind
Source Separation (BSS), Optimal Basis Set (OBS), and a mixture of both (OBS-ICA).
Each method was evaluated for its applicability for ERP and source analysis using the
following criteria: the number of events surviving artifact threshold scans, signal-to-noise
ratio (SNR), error of source localization, and signal variance explained by the dipolar
model. Using these criteria, PCA-S and ICA-S fared best overall, with highly significant
differences to the established methods, especially in source localization. The PCA-S
approach was also applied to a single subject Berger experiment performed in the MRI
scanner. Overall, the removal of BCG artifacts by the surrogate methods provides a
substantial improvement for the analysis of simultaneous EEG-fMRI data compared to
the established methods.
Keywords: simultaneous EEG and fMRI, artifact removal, optimal basis set (OBS), blind source separation (BSS),
multimodal imaging, spatial filter (SF), independent component analysis (ICA), pulse artifact (PA)
INTRODUCTION
Interest in simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging
(fMRI) experiments has grown, ever since Logothetis et al. (2001) showed a clear relationship
between EEG and the blood oxygenation level-dependent (BOLD) signal. Over the years, it has
become clear that multimodal data acquisition, in particular EEG-fMRI, provides new insights into
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Rusiniak et al. EEG-fMRI: Balistocardiongram Artifact Reduction
neurocognitive functions (Laufs, 2012;Manganas and Bourbakis,
2017). Simultaneous EEG-fMRI recordings benefit from
the advantages of both methods—delivering high spatial
and temporal precision, and observation of electric and
hemodynamic changes at the same time (Mulert and Lemieux,
2010;Rosenkranz and Lemieux, 2010).
The utility of simultaneous EEG-fMRI recordings is limited
by three main interconnected factors: (1) the effectiveness of
fMRI-related artifact reduction from EEG recording; (2) the
usability of analytical tools; (3) the examination cost. Here,
effective methods for artifact reduction are of highest importance
and a prerequisite for generating useful results. Two types of
artifacts are predominant in the EEG signal recorded during
fMRI acquisition. The first type is an imaging artifact induced by
the magnetic gradient coils (Allen et al., 2000). The second type,
the so-called ballistocardiogram (BCG), is related to the heartbeat
(Debener et al., 2008) or pulse artifact (Yan et al., 2010). While
there is general agreement that the adaptive average subtraction
method proposed by Allen et al. (2000) with further improvement
from Moosmann et al. (2009) is a sufficient solution for imaging
artifact removal, the BCG artifact is still not treated efficiently.
The BCG artifact is a complex signal distortion that originates
from multiple physical phenomena. As described by the Maxwell
equations, the changing magnetic field induces a changing
electric field. Therefore, even microscopic head movements in
a strong magnetic field generate a strong electrical current. The
heartbeat and related blood flow cause whole-body movements
when a subject is in supine position (Niazy et al., 2005;Debener
et al., 2008). In addition, when an electrode is located near
a vein, the skin pulsation can generate another component of
artifact (Bonmassar et al., 2002;Yan et al., 2010). Since blood is a
conductive fluid, it can generate electrical potential changes over
the scalp due to the Hall effect (Müri et al., 1998;Mullinger et al.,
2013). Moreover, the BCG artifact can vary over the duration of
a recording (Marino et al., 2018a) because of various factors (i.e.,
position change in MRI, blood pressure change, etc.).
Over the past few years, multiple data processing approaches
have been proposed to reduce the BCG artifact (see for review:
Grouiller et al., 2007;Vanderperren et al., 2010). Three main
trends of BCG artifact reduction can be distinguished: (1)
channel-wise subtraction of a BCG artifact template; (2) blind
source separation (BSS) based on independent component
analysis (ICA); (3) the combination of both methods. The
first method evolved from the original work of Allen et al.
(1998) and was significantly improved by Niazy et al. (2005).
In this approach, the artifact template is created and then
subtracted from the data using the optimal basis set (OBS)—the
combination of the principal components obtained from the
averaged artifact template and the template itself. Further
improvements to the OBS were recently proposed by Oh
et al. (2014) and Marino et al. (2018b). Most of the changes,
however, focus on QRS complex detection and BCG template
creation, where the correction procedure is based on artifact
signal subtraction which can introduce distortion that mostly
manifests itself in topography malformation (Ille et al., 2002).
The second approach—Blind Source Separation (BSS)—is of a
different nature (Jung et al., 2000). The signal is first decomposed
to select artifact-related components. Then, the signal is projected
back to the sensor space leaving out these components. The BSS
approach usually makes use of ICA algorithms for decomposition
as initially proposed by Bénar et al. (2003). Nowadays, there is a
vast number of ICA algorithms and many different approaches
for component selection (see Vanderperren et al., 2010), which
were applied for BCG artifact reduction. Among others, the
Infomax (Bell and Sejnowski, 1995;Lee et al., 1999) and the
FastICA (Hyvarinen, 1999) have been shown to be successful in
reducing the BCG artifact (Infomax: Bénar et al., 2003;Srivastava
et al., 2005;Debener et al., 2007, FastICA: Mantini et al., 2007).
Nonetheless, the BSS approach could be questioned due to
the complex nature of the BCG artifact (Grouiller et al., 2007;
Abreu et al., 2016). The independency criterion for ICA might
not be fulfilled since the BCG artifact originates from multiple
phenomena which result from the same physiological process.
One attempt to overcome this limitation is to combine ICA
with QRS detection of an EKG electrode either by performing
ICA on the epoched data relative to R-peak (Debener et al.,
2007) or by clustering approach (Wang et al., 2018). Yet
still, the separation between the components of the artifact,
as well as the separation of artifact and brain signals, might
be insufficient. To address these problems of the mentioned
methods, a third approach that is a combination of both
methods (OBS-ICA or ICA-OBS) has been proposed (Debener
et al., 2007;Abreu et al., 2016;Marino et al., 2018b). Despite
the rationality of such an idea, one should consider that the
pitfalls of both methods can also propagate to this approach,
resulting in high signal distortion when not used carefully
(Vanderperren et al., 2010).
To deal with the BCG artifact, there are also hardware-based
solutions like reference layer (Chowdhury et al., 2014;Luo et al.,
2014) or carbon wire loops (Masterton et al., 2007;Abbott
et al., 2015;van der Meer et al., 2016). In those approaches,
the BCG artifact is reduced by the subtraction of a referential
signal obtained from additional layers/electrodes/loops which
record the currents induced by the movement and not the brain
activity. It was already shown that this approach can reduce the
BCG artifact efficiently (Bullock et al., 2021), however it requires
additional hardware and a setup procedure, and also cannot be
applied to data already recorded.
In the present paper, we propose a semi-automated BCG
artifact reduction method based on surrogate spatial filtering
(Berg and Scherg, 1994a). The measured signal is a superposition
of brain and artifact activities. In the surrogate method, it is
assumed that the artifact signals and the signals originating from
the brain can be separated if their spatial distributions over
the scalp are known. The artifact topographies can be obtained
either by principal component analysis (PCA) performed on an
averaged artifact template (Ille et al., 2002) (similarly to the OBS
method) or by ICA (like in the BSS method). The brain signals
are estimated using a surrogate model consisting of a set of
regional dipole sources distributed over the brain to describe
most of the EEG signal. Therefore, the BCG reduction procedure
should not introduce substantial distortions of the brain signals
and separate out the artifact components sufficiently at the same
time. In this study, the proposed approaches were compared
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Rusiniak et al. EEG-fMRI: Balistocardiongram Artifact Reduction
with the most commonly used OBS, BSS, and OBS-ICA methods
described above.
MATERIALS AND METHODS
Subjects
EEG data were collected from 55 young male adults (mean age 27
years). One additional male subject (27 years) was recruited for
the Berger experiment. This subject data was part of a previous
study (Rusiniak et al., 2018).
All subjects had no history of neuropsychiatric disorders or
head injury. Subjects provided written informed consent prior
to participation. EEG data processing was performed using
BESA Research software (version 7.1 March 2021, BESA GmbH,
Gräfelfing, Germany) unless otherwise stated.
Data Acquisition
Data was collected using an MRI compatible 64-channel EEG
system (SynAmps2, Neuroscan, Texas, United States). EEG
recordings were performed in a 3 T MR scanner (Magnetom
Trio, Siemens, Erlangen, Germany) simultaneously with an fMRI
sequence (TR = 3 s, TE = 30 ms, 47 slices, slice thickness = 3 mm,
no gap, pixel spacing = 2 ×2 mm). The EEG recording
was sampled with 10 kHz frequency starting before the fMRI
session and ending after finishing the image acquisition. The
EEG sampling clock was synchronized with the MRI machine.
Simultaneous EEG-fMRI sessions lasted 6 min (120 volumes).
Subjects were asked to observe a black screen (resting-state
paradigm) and remain calm. Each fMRI volume acquisition was
marked by a trigger event in the EEG data.
A second experiment designed to evoke alpha rhythm in
occipital cortex [Berger, 1929 experiment (1929)] was conducted
using the same EEG and MRI setup. The subject was asked to
open his eyes (when the beep sound was presented) or close them
(when the screen was switched to black). Each block lasted for
30 s and the whole recording lasted 6 min.
Superimposition of Simulated Auditory
Evoked Potentials Data
To analyze the efficiency of BCG artifact reduction, simulated
auditory evoked potentials (AEP) were added to each resting-
state EEG recording using BESA Simulator (version 1.4.0, BESA
GmbH, Gräfelfing, Germany). 200 replications of the same
simulated AEP signal were added to the original EEG to mimic an
auditory EEG-fMRI experiment (inter-stimulus interval = 1.5 s
with jitter = 0.2 s) prior to artifact correction or any other
signal processing. Two dipoles, oriented perpendicular to the
right and left Sylvian fissure, were used to generate the AEP.
Source activities were simulated as near-to synchronous mono-
phasic Cz-negative deflections (2 ms time lag, parameters detailed
in Table 1) and some noise was added with a signal-to-noise
ratio (SNR) of 6.
Figure 1 illustrates the locations, waveforms and topographies
of the two-dipole AEP simulation (the plots were created using
BESA Plot, Version 1.2.3, BESA GmbH, Gräfelfing, Germany).
TABLE 1 | Description of the dipolar model used for auditory ERP simulation.
Source Location in Talairach
coordinates
Orientation N100 peak
X Y Z X Y Z Latency
Left −49 −18 12 −0.17 −0.25 −0.95 101 ms
Right 49 −15 13 0.15 −0.24 −0.96 103 ms
Using this model, the scalp AEP distribution was generated at
the 64 recording electrodes and overlapped with the original
EEG at the 200 predefined trigger times as specified above. This
overlap of AEP and EEG served as the same, identical input for
each pipeline of artifact correction to evaluate the differences
between methods and to observe the specific distortions of the
AEP introduced by each method.
The resulting signal dk(t) at electrode kcan be described as
the sum of the measured electrical potential uk(t) and simulated
AEPk(t):
dk(t)=uk(t)+AEPk(t).(1)
Since the measured electrical potential uk(t) is a mixture of
the brain signal bk(t), imaging artifact IMGk(t), BCG artifact
BCGk(t), and noise nk(t), Eq. 1 can be formulated as follows:
dk(t)=bk(t)+IMGk(t)+BCGk(t)+nk(t)+AEPk(t).(2)
Pipeline of Artifact Processing
The pipeline of removing artifacts and retrieving the
superimposed, averaged AEP consisted of several steps.
First, the imaging artifact IMGk(t) was estimated and removed
from the data dk(t), as described in the fMRI artifact removal
section. Second, different BCG artifact reduction approaches
were applied to reduce BCGk(t). Third, bad trials were rejected,
and the accepted NAEP trials were averaged as detailed below.
The number of rejections depends on the noise level of the
EEG, as described in the Evaluation metrics section. Finally, the
averaging enhanced the time-locked AEP while minimizing bk(t)
and nk(t). Leaving away the latter terms, this leads to the formula
of the averaged AEP:
AEPk(t)=
N
X
n=1
AEPn,k(t)
∼
=
N
X
n=1
(dn,k(t)−IMGn,k(t)−BCGn.k(t)). (3)
Thus, an optimal IMG and BCG artifact reduction should
result in an averaged AEP similar to the simulated AEP.
Functional Magnetic Resonance Imaging
Artifact Removal
The imaging artifact IMGk(t) was removed from dk(t) by
applying the realignment parameter informed moving average
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FIGURE 1 | Dipole model used for the simulation of auditory ERP. In the first row, the locations and orientations of the two dipoles are depicted in the head scheme.
On the right, the modeled dipoles are shown in the presence of the surrogate model used for BCG artifact correction. The 29 regional sources for surrogate brain
model are color-coded (red for right hemisphere, blue for left hemisphere, gray for midline). The modeled sources (which are not part of surrogate brain activity
model) are shown in white. The second and third row show the left and right dipole source waveforms along with their topographies. Below, the evolution of the
N100 topography from 80 to 120 ms is depicted.
artifact subtraction method as described by Moosmann et al.
(2009). We used 16 averages as a parameter for moving
template creation and a realignment threshold of 0.5 mm. The
realignment information was obtained from fMRI preprocessing
using Statistical Parametric Mapping software (version SPM12,
the Wellcome Centre for Human Neuroimaging, London,
United Kingdom) in MATLAB (version 2007, MathWorks,
United States). After fMRI artifact removal, EEG data were down-
sampled to 1 kHz.
Ballistocardiogram Artifact Removal
To reduce the BCG artifact, five different approaches were used
independently as described below and illustrated in Figure 2.
Ballistocardiogram Artifact Removal by the PCA
Surrogate Method
The PCA Surrogate method (PCA-S) consisted of the following
steps: First, for the purpose of creating an averaged template of
the artifact, EEG data were band-pass filtered in the frequency
range of BCG (1–20 Hz) and re-referenced to the average
reference. Then, one representative occurrence of the BCG
artifact was manually selected from the EEG data based on visual
inspection of all channel waveforms and used for automated
pattern search (Scherg et al., 2002;Bast et al., 2004) to create
an averaged template of the artifact. Each detected pattern that
had a spatio-temporal correlation with the template higher than
60% was accepted. In the next step, a PCA was performed
on the averaged template. All principal components accounting
for more than 0.5% of the artifact template signal variance
were used for spatial filtering. The number of components
varied between 4 and 8 (mean 5.7). The accepted artifact-
related principal components were combined with predefined
regional sources (surrogate model) distributed evenly throughout
the brain to calculate a spatial filter that separated the BCG
artifact from brain activity as described by Berg and Scherg
(1994a). We used a brain surrogate model that included 29
regional sources. A regional source in EEG consists of 3 dipoles
at the same location with orthogonal orientations to describe
the surrounding brain activity in any direction. Thus, the brain
activity was approximated with high goodness-of-fit (>99%) by
87 dipoles (Beniczky et al., 2016). The surrogate model (together
with simulated sources which are not part of it) are shown in the
first row of Figure 1. By combining the artifact-related principal
components and brain-related source components, the inverse
spatial filter of PCA-S was created. When applying this linear
filter to the original, unfiltered EEG signals, source waveforms
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Rusiniak et al. EEG-fMRI: Balistocardiongram Artifact Reduction
FIGURE 2 | Schematic representation of all five methods used for Ballistocardiogram (BCG) artifact reduction. Data processing was performed from top to bottom,
each column represents one method.
with BCG artifact were calculated. Then, data can be projected
back onto the scalp EEG using only non-BCG-related data to
reconstruct the BCG artifact corrected EEG in sensor space. The
brain surrogate model was applied with regularization of 2% and
artifact coefficients were applied without regularization.
Ballistocardiogram Artifact Removal by ICA
Surrogate
The ICA surrogate (ICA-S) method is comparable to the
PCA-S method. Instead of using PCA topographies, the BCG
artifact components were determined by ICA using the same
manual selection as described for BSS (section Ballistocardiogram
Artifact Removal by Optimal Basis Set). Then, the spatial
components of the ICA traces that were identified as artifact
were combined with the 87 spatial brain source components as
described for PCA-S (cf. Figure 2).
Ballistocardiogram Artifact Removal by Optimal
Basis Set
The OBS approach of BCG artifact reduction (Niazy et al.,
2005) was used as implemented in the FMRIB plug-in (version
2.0, Nuffield Department of Clinical Neuroscience, Medical
Sciences Division, Oxford, United Kingdom) for the EEGLAB
toolbox (version 13.6.5.b, Swartz Center for Computational
Neuroscience, San Diego, United States). After removing the
fMRI imaging artifact, EEG was exported from the BESA
Research software into European Data Format (EDF) and
loaded into EEGLAB. First, QRS complexes were detected in
the ECG channel by the FMRIB plugin (combined algorithms
of Christov, 2004;Kim et al., 2004). Second, by averaging
the epochs around the detected QRS complexes, an averaged
template of the BCG artifact was created. Finally, using the
OBS approach, the principal components of the averaged artifact
template were subtracted from the EEG. The number of removed
components was fixed to 4 which is the default value in the
FMRIB plugin. After BCG artifact reduction, EEG data were
converted to EDF and reloaded into the BESA Research software
for further analysis.
Ballistocardiogram Artifact Removal by Optimal
Basis Set and ICA
The OBS-ICA method used the outcome of the procedure above
(OBS) followed by ICA. This computation was performed in the
EEGLAB toolbox following the Debener et al. (2007) processing
pipeline. First data was filtered in the range of 0.3–40 Hz and
epoched around each detected BCG event (in a range of −50
to −750 ms). Then ICA was computed on concatenated epochs
using the Extended Infomax approach (Lee et al., 1999). The
component that had the highest spatial correlation with the
topography of maximum signal of BCG template was removed
during back projection of scalp EEG.
Ballistocardiogram Artifact Removal by Blind Source
Separation
The BSS approach (Jung et al., 2000) is based on ICA. After
filtering with a time-constant filter (low cutoff 0.1 Hz) and
a high cutoff filter (30 Hz), a 40 s block of data with clearly
visible BCG artifact was selected to perform ICA using the
Extended Infomax algorithm (Lee et al., 1999). The largest
components were displayed for inspection, and the following
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visual cues were used to manually identify and mark traces
with BCG artifact: waveform shape, and temporal relationship to
the electrocardiography (ECG) channel. The number of marked
components varied from 3 to 9 (mean 5.5), depending on data.
Finally, the BCG artifact corrected scalp EEG was calculated by
back projecting only the unmarked ICA components.
Auditory Evoked Potentials Averaging
Using the artifact-corrected EEG, identical analysis steps were
performed for all BCG artifact correction methods. First, bad
epochs with residual artifacts like movement or blink were
rejected using the automated rejection tool of BESA Research.
Epochs with peak-to-peak amplitudes greater than 120 µV and
signal gradients greater than 75 µV/sample were excluded. For
the detection of bad epochs, data were filtered from 0.3 Hz
(forward phase-shift, 6 dB/Oct) to 30 Hz (zero phase-shift,
24 dB/Oct). Second, after rejecting bad epochs, filters were turned
off to average the AEP in a window of −300 to +800 ms around
the accepted triggers. The averaged signal was filtered using the
same filter settings as previously. Finally, EEG data were re-
referenced to the average reference of the 64 channels of the
artifact-corrected EEG. The grand average AEP was created using
the AEPs averages of all subjects.
Evaluation Metrics
We compared the BCG artifact reduction methods by using
the following evaluation criteria: First, for each data correction
method that was applied, we compared the number of events
that passed the amplitude and gradient acceptance thresholds
for averaging. Second, the SNR values of the averaged AEP
resulting after applying each method were compared. SNR per
channel was computed using the root mean square value of pre-
stimulus interval (−300 to 0 ms) as baseline and the root mean
square value of the first 300 ms of post-stimulus data as the
signal of interest. The mean SNR value across all channels was
computed. Third, the averaged AEP waveforms were examined
by comparing the latency and amplitude at Cz as detected
automatically by the peak detection algorithm of BESA Research
in the time range from 0 to 200 ms.
We examined the accuracy of source reconstruction after
each BCG artifact reduction method. Since the AEP had been
generated by fixed simulated bilateral dipoles (Figure 1 and
Table 1), we assessed how much of the averaged signal after
BCG correction was explained by the initial AEP model. For this,
explained variance was calculated both for the grand average AEP
and individual AEP in the full width of half maximum (FWHM)
range (81–114 ms). This would amount to 100% if data variance
over all channels was fully explained by the model. Lower values
indicate higher distortion of the AEP topography.
We also evaluated the location and angle error from single
subject source localization. For this purpose we computed a
source solution containing two symmetric dipoles for every
subject. Dipole locations and orientations were fitted to
the artifact corrected averaged ERP using the Nelder-Mead
optimization algorithm (Nelder and Mead, 1965) in the range
81–114 ms. A 4-shell ellipsoidal head model was used (the same
as for the ERP simulation) (Berg and Scherg, 1994b). The
localization error was computed as a norm of difference between
obtained and seeded dipole position (c.f. Figure 1 and Table 1).
The difference angle was computed as scalar product between
dipole orientations. Since in each model there was exactly two
dipoles, to simplify further analysis we computed the average
error for each pair of dipoles.
None of the tested variables showed normal distribution as
tested by Shapiro-Wilk test in the SPSS software (version 21.0,
IBM, New York, United States). Therefore, the Kruskal-Wallis
(K-W) test was applied followed by Dunn-Bonferroni post-hoc
pairwise comparison in SPSS.
Alpha Rhythm Data Analysis (Single
Subject)
We compared the eyes-closed state with the eyes-opened
state from Berger experiment session using mean fast Fourier
transform (FFT). The mean FFT was computed in overlapping
blocks (2.05 s) over combined periods of each condition
(c.a. 180 s per condition). To investigate the difference, we
evaluated the spatial distribution in the alpha range as well as
an FFT heat map representing mean amplitude per frequency for
each channel, sub-divided into channel groups (frontal, central,
left temporal, right temporal, and occipital). In addition, for
this data set we compared BCG waveforms from the beginning
of the recording with ones from the end of the recording, to
evaluate the BCG variability. For this purpose, two different
epochs of raw EEG signal after average referencing and filtering
(0.3–30 Hz) were sent to the source analysis module of BESA
Research. We compared two epochs—one from the eyes-opened
state at the beginning of the recording (10 s) and one from
the eyes-closed state at the end of the recording (355 s).
The epochs were time-locked to the R-peak and the epoch
interval was −100 to 600 ms. For both epochs the same model
(spatial filtering) was applied, replicating the PCA-S artifact
reduction—29 brain regional sources (with 2% regularization)
extended with 4 BCG coefficients (with no regularization)
obtained from PCA. These were the same components that we
used for artifact correction.
RESULTS
Mean Trial Number
As an initial measurement of BCG artifact reduction efficiency,
we assessed the number of accepted events after rejecting bad
epochs. The more accepted events, the higher the quality of the
data (fewer residual artifacts).
In Figure 3 (left), the mean numbers of accepted trials were
compared. The highest mean value was observed for the PCA-S
method (¯x= 178 ±16). Lower values were obtained for ICA-S
(¯x= 155 ±41), OBS (¯x= 120 ±44) and OBS-ICA (¯x= 126 ±45)
while the BSS method showed the lowest numbers (¯x= 93 ±62).
There was a statistically significant difference between these
methods as determined by K-W test [H(4) = 101.1, p<0.001]
with a mean rank trial number of 215 for PCA-S, 171 for ICA-S,
106 for OBS, 115 for OBS-ICA and 82 for BSS. Post-hoc testing
(Table 2) revealed that the higher number of accepted epochs was
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FIGURE 3 | Comparison charts of mean trial number and mean SNR for different BCG artifact reduction methods. Boxes show the medians and 1st and 3rd
quartiles, whiskers denote the 1.5 interquartile range, crosses indicate mean values, and outliers are represented by dots.
statistically significant when PCA was compared with all other
methods (p<0.001 for OBS, OBS-ICA, BSS and p<0.05 for
ICA-S). Similarly, ICA-S outperformed OBS (p<0.001), BSS
(p<0.001), and OBS-ICA (p<0.05). There was no significant
difference between OBS and OBS-ICA (p= 1.000), OBS and BSS
(p= 1.000) and between OBS-ICA and BSS (p= 0.307).
Signal-To-Noise
SNR is a good indicator of the averaged data quality. Higher
SNR value indicates a cleaner and less noisy baseline. When
the five BCG artifact reduction methods (Figure 3, right) were
compared, the highest SNR values were observed for PCA-S
(¯x= 3.45 ±1.60) and ICA-S (¯x= 3.42 ±1.77). OBS, OBS-ICA
and BSS had much smaller values (OBS: ¯x= 1.33 ±3.14,
OBS-ICA: ¯x= 1.11 ±3.29, BSS: ¯x= 1.43 ±2.08). The K-W test
showed statistically significant differences between these methods
[H(4) = 81.4, p<0.001]. The mean rank SNR was 193, 188, 100,
96, 112 for PCA-S, ICA-S, OBS, OBS-ICA, and BSS, respectively.
Post-hoc pairwise comparison (see Table 2) revealed that the
higher SNR value observed for both PCA-S and ICA-S was
significantly higher (p<0.001) than for OBS, OBS-ICA and BSS.
TABLE 2 | Dunn-Bonferroni post-hoc results of pairwise comparison between
BCG artifact reduction methods for trial number and SNR.
Pair Trial number SNR
PCA-S vs. ICA-S 0.035* 1.000
PCA-S vs. OBS 0.000** 0.000**
PCA-S vs. OBS-ICA 0.000** 0.000**
PCA-S vs. BSS 0.000** 0.000**
ICA-S vs. OBS 0.000** 0.000**
ICA-S vs. OBS-ICA 0.002* 0.000**
ICA-S vs. BSS 0.000** 0.000**
OBS vs. OBS-ICA 1.000 1.000
OBS vs. BSS 1.000 1.000
OBS-ICA vs. BSS 0.307 1.000
Statistically significant values are indicated in bold print, *p <0.05, **p <0.001.
The difference in SNR between PCA-S and ICA-S, as well as
between OBS, OBS-ICA, and BSS was not significant (p= 1.000).
Auditory Evoked Potentials Waveform
Properties
To reflect typical ERP evaluation, we compared the averaged AEP
signals resulting from the different methods (Figure 4A). The
overall waveforms for grand average after BCG artifact reduction
were similar to the modeled ones. However, the AEP amplitudes
after BCG reduction were slightly reduced as compared to
the simulated model for all the methods. Peak latency and
amplitude differences between BCG artifact reduction methods
were evaluated for N100 at the central electrode (Cz) but no
significant differences were found. [K-W test for amplitude:
H(4) = 3.0, p= 0.553, K-W test for latency: H(4) = 1.3, p= 0.866].
Despite of no difference in amplitude and latency at Cz electrode,
some differences in the scalp topography of the grand-mean AEP
averaged over the latency range of 81–114 ms were observed
(Figure 4B). This could affect source localization which was
furtherly evaluated.
Explained Variance
The quality of source reconstruction as defined by the explained
variance of the grand average data was highest with PCA-S
(97.3%) and ICA-S (96.9%), whereas it was reduced for OBS
(93.8%) and OBS-ICA (94.0%), as well as for BSS (90.3%), as
shown in Figure 5 (left). Due to the noise of the corrected
individual AEPs the mean values of explained variance were
lower when considering the mean values over all subjects
(Figure 5, right). They were still considerably smaller in OBS
(¯x= 67.3% ±16.0), OBS-ICA (¯x= 62.8% ±21.1), and BSS
(¯x= 40.5% ±23.8) as compared to PCA-S (¯x= 80.0% ±9.9)
and ICA-S (¯x= 77.3% ±12.9). The statistical evaluation by the
K-W test showed a significant difference between the five BCG
reduction methods [H(4) = 102.0, p<0.001] with a mean rank
explained variance of 194 for PCA-S, 182 for ICA-S, 133 for
OBS, 132 for OBS-ICA, and 58 for BSS. The post-hoc pairwise
comparison is depicted in Table 3. The explained variance
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FIGURE 4 | Comparison of the BCG artifact reduction methods. (A) The signals recovered at T7, Cz and T8 electrodes (virtually re-referenced to the average
reference) are compared to the noise-free, simulated AEP signal. Each solid color line represents one of the 5 different BCG artifact reduction methods; the dashed
line (Model) represents the simulated AEP signal. (B) Topographic plots of averaged ERP response for the simulated AEP signal and the BCG artifact reduction
methods in the range 81–114 ms (full width at half maximum of the modeled signal power, as illustrated by the gray shaded areas in the top row).
FIGURE 5 | Explained variance of grand averaged data and individual data. Explained variance was averaged across the full width at half maximum of modeled
signal power for the different BCG artifact reduction methods. For individual data the boxes show the medians and 1st and 3rd quartiles, whiskers denote the 1.5
interquartile range, crosses indicate mean values, and outliers are represented by dots.
obtained for PCA-S was significantly higher than any other non-
surrogate-based methods: OBS (p<0.05), OBS-ICA (p<0.001)
and BSS (p<0.001). Similarly, ICA-S values were significantly
higher in comparison to non-surrogate-based methods, but the
difference was slightly smaller (p<0.05 for OBS and OBS-ICA,
p<0.001 for BSS). BSS had significantly lower explained variance
as compared to both OBS and OBS-ICA (p<0.001). There was
no statistical difference between PCA-S and ICA-S and between
OBS and OBS-ICA.
Localization Error and Angle Error
Furthermore, we evaluated how the observed difference in
explained variance translates to source analysis efficiency. To
measure this, we verified the deviation between source model
fitted to the artifact corrected data and the seeded model (see
Figure 1 and Table 1). As shown in Figure 6, PCA-S and
ICA-S had most of the dipoles located around the auditory
cortex (where the seeded dipoles were located). For the OBS,
and even more so for OBS-ICA, more outliers can be observed.
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TABLE 3 | Dunn-Bonferroni post-hoc results of pairwise comparison between
BCG artifact reduction methods for explained variance, localization error
and angle error.
Pairwise comparison Explained variance Localization error Angle error
PCA-S vs. ICA-S 1.000 1.000 1.000
PCA-S vs. OBS 0.001* 0.016* 0.040*
PCA-S vs. OBS-ICA 0.000** 0.002* 0.002*
PCA-S vs. BSS 0.000** 0.000** 0.000**
ICA-S vs. OBS 0.013* 0.073 0.264
ICA-S vs. OBS-ICA 0.001* 0.012* 0.026*
ICA-S vs. BSS 0.000** 0.000** 0.000**
OBS vs. OBS-ICA 1.000 1.000 1.000
OBS vs. BSS 0.000** 0.000** 0.000**
OBS-ICA vs. BSS 0.000** 0.001* 0.006*
Statistically significant values are indicated in bold print, *p <0.05, **p <0.001.
The BSS method resulted in dipoles widely distributed over
the whole brain volume. This observation is supported by
the numerical verification of localization and angle error, as
shown in Figure 7. The localization and angle error were
smallest for PCA-S (localization error: ¯x= 17.2 mm ±10.4,
angle error: ¯x= 22.5◦±9.8) and ICA-S (localization error:
¯x= 18.2 mm ±11.3, angle error: ¯x= 24.0◦±10.7).
A larger error was observed for both OBS (localization error:
¯x= 27.6 mm ±18.4, angle error: ¯x= 28.9◦±11.2) and
OBS-ICA (localization error: ¯x= 29.0 mm ±18.5, angle error:
¯x= 31.1◦±11.9). The largest deviation from simulated model
was observed for BSS (localization error: ¯x= 49.6 mm ±24.9,
angle error: ¯x= 43.0◦±16.3). Further statistical evaluation
confirmed that these differences were statistically significant
[H(4) = 76.2, p<0.001 for localization error, H(4) = 63.8,
p<0.001 for angle error]. The mean rank values for localization
error were 92.3, 99.6, 140.2, 148.8, 209.2 for PCA-S, ICA-S,
OBS, OBS-ICA, and BSS, respectively. The mean rank values
for angle error were 94.5, 104.5, 138.2, 150.2, 202.6 for PCA-S,
ICA-S, OBS, OBS-ICA, and BSS, respectively. The pairwise
comparison showed that both localization and angle error for
PCA-S was lower than for OBS and OBS-ICA (p<0.05),
as well as for BSS (p<0.001). Similarly, ICA-S had lower
localization and angle error than OBS-ICA (p<0.05) and BSS
(p<0.001). Also, BSS was outperformed by OBS (p<0.001)
and by OBS-ICA (p<0.05). There were no statistical differences
between PCA-S and ICA-S (p= 1.000), ICA-S and OBS (p= 0.073
for localization error, p= 0.264 for angle error), OBS and
OBS-ICA (p= 1.000).
The Evaluation of Single Subject
Recording (Berger Experiment)
No BCG artifact was visible in the data after PCA-S artifact
correction, as shown in Figure 8. The blink related to
the closing of the eyes is clearly visible in the middle of
the shown interval, followed by prominent alpha rhythmical
activity. No such activity can be observed before the closing
of the eyes. In raw data the blink is also visible, yet due
to high contamination with BCG artifact no other data
features can be distinguished, even when investigating heat
maps and topography maps which are shown below the
data interval in Figure 8. Conversely, after correcting data
using the PCA-S method, the heat maps depicted a strong
differentiation between eyes-opened and eyes-closed states—the
activity in the alpha frequency range can be noted, especially
in occipital channels. Importantly, in both heat maps, no other
atypical oscillatory activity can be observed. The alpha rhythm
topography also reflected the normal topography typically
observed for the Berger experiment—strong activity in the
occipital lobe in eyes-closed state, which is absent during the
eyes-opened state.
Ballistocardiogram Variability Evaluation
in a Single Subject Recording (Berger
Experiment)
In Figure 9 the waveforms for four components of BCG obtained
at the beginning (10 s) and at the end of the recording (355
s) are shown. In addition, the first sample was obtained during
eyes-opened state, the second during eyes-closed state. While all
waveforms are similar, some minor differences can be observed,
especially for the second component.
FIGURE 6 | Distribution of source localization obtained by fitting two symmetric dipoles to individual data, for the different BCG artifact reduction methods. In each
image the seeded dipole model in the right and left Sylvian fissure is depicted in black.
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FIGURE 7 | Localization error and angle error for individual model fitting for the different BCG artifact reduction methods. Boxes show the medians and 1st and 3rd
quartiles, whiskers denote the 1.5 interquartile range, crosses indicate mean values, and outliers are represented by dots.
DISCUSSION
In this study, we applied the spatial filtering method (Berg and
Scherg, 1994a) to EEG data measured during fMRI acquisition
using a standard surrogate source model in order to reduce the
BCG artifact. To compare this approach with the established
methods of OBS, OBS-ICA, and BBS, we combined real resting-
state EEG data measured during fMRI acquisition with simulated
AEPs. Thus, we could evaluate the strength of artifact reduction
and signal distortions introduced by the different methods.
This approach is justified by the assumption that the fMRI
environment introduces only contaminations of the EEG signal
and does not influence the brain signals themselves. Importantly,
we evaluated our method using auditory ERPs. This bilateral,
synchronous activity with tangential dipolar orientation makes
the source analysis challenging (Scherg et al., 2019) and highly
dependent on SNR. Also, as shown by Shams et al. (2015), the
auditory ERPs are more troublesome for BCG artifact correction
when compared to e.g., visual ERPs, due to differences in the BCG
characteristic across different channels since the generators lie on
distant and opposite sites relative to the to head center.
While there was no significant difference between methods
for AEP waveform properties, both surrogate-based BCG artifact
reduction methods—PCA-S and ICA-S—outperformed the OBS,
OBS-ICA, and BSS approaches in the following evaluation metric:
the basic signal features (number of events accepted for averaging
and SNR), the quality of source reconstruction for the grand
average, and source localization error for single subjects. The
method used to estimate the artifact topography in the surrogate
methods (PCA or ICA) did not have an impact on the AEP
outcome, apart from a higher mean trial number accepted for
ERP averaging when the PCA method was used.
The number of events accepted for averaging was significantly
higher when comparing PCA-S with ICA-S (p<0.05) and OBS,
OBS-ICA, BSS (p<0.001, Figure 3, left). A higher number of
accepted events for averaging is of major importance since it
may lead to shorter experiments and allow for more sophisticated
methods of data analysis, for example, the comparison of the
first and second part of an experiment, single-trial analysis,
time-frequency analysis (Castelhano et al., 2014), or EEG-
driven fMRI analysis (Abreu et al., 2018). Furthermore, the
higher the number of averaged events, the less biological noise
contaminates the waveforms, which can be evaluated using SNR
and peak amplitudes.
The highest SNR was observed for PCA-S, followed by ICA-S
(Figure 3, right). The significant reduction of SNR in the OBS,
OBS-ICA, and BSS methods was mainly due to increased noise
introduced before and after the AEP. These findings are in general
agreement with the observations of Debener et al. (2007) and
explain why the number of detected events was significantly
reduced both in OBS and BSS. Interestingly, a slightly lower
SNR value was observed when OBS-ICA was compared to OBS,
while the number of mean number of trials showed the opposite
relationship. This observation stands in contrast to Debener et al.
(2007), yet it was not statistically significant here.
Amplitude reduction was observed in all BCG artifact
reduction methods, but we did not notice any significant
difference between tested methods for single (central) electrode
amplitude and latency evaluation. For the first time, this study
showed this effect is clearly a product of data processing, as
the testing procedure combined modeled AEP activity with real
EEG resting state data instead of using test-retest comparisons.
However, the cause of this reduction is unclear since it might
be due either to the specific BCG artifact reduction process
or to the fMRI artifact removal. For simultaneous EEG-fMRI
studies, it is widely accepted that signal quality and amplitude
is decreased to some extent (i.e., Rusiniak et al., 2013;Marino
et al., 2018b). Yet, it is crucial that the MR environment and
EEG post-processing do not distort signal topography, in order
to minimize the bias of statistical comparisons and source
localization. The stability of the signal distribution after BCG
correction is also of major importance for a direct comparison of
the AEP within and outside of the magnetic resonance device, as
well as for longitudinal experiments apart from the documented
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FIGURE 8 | The comparison of Eyes-Opened and Eyes-Closed state for both raw (A) and PCA-S artifact corrected data (B). At the top of each sub-figure an
example of the same 30 s of recording is shown, in which the transition from eyes-opened to eyes-closed state occurs in the middle. 20 electrodes are shown (every
second channel from the 64-channel montage that was used). Below, an FFT heat map for both states, respectively, is shown, showing each recorded channel
(grouped by brain lobes) along with the alpha rhythm topography.
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amplitude reduction. Inspecting topographies obtained on the
grand average level for all the methods (Figure 4B) it can be
noted that each method affected the topography, yet the outcome
of PCA-S and ICA-S reassembled the modeled signal, whereas it
looks like the OBS and OBS-ICA introduced some frontal shift
in the map. The topography after BSS artifact correction seems to
be distorted most.
The differences in topography may likely translate into a
distortion of source localization. Therefore, we checked the
explained variance of the corrected AEP data when using the
simulated AEP source as model. The percentage of explained
variance was significantly higher using PCA-S and ICA-S as
compared to the OBS, OBS-ICA, and BSS methods. For both
surrogate methods the explained variance was around 97%. This
value clearly indicates that most of the signal was explained, and
that the model is adequate for data explanation. The lower the
value, the larger the risk that the model might be considered
not sufficient for the data, leading to a perceived need to
introduce additional sources to the model. This observation
was investigated in more detail by performing analysis on the
individual subject level. Obviously, due to much larger noise
contamination compared to the grand average, the explained
variance at the single subject level was lower in general. The
difference between surrogate methods and other methods was
even more prominent in this case. For PCA-S as well as for ICA-S,
we obtained a mean value around 80% with very low inter-subject
variability, while the other methods performed much worse,
especially BSS. This indicated the need for a larger number of
cases for non-surrogate-based methods to achieve trust-worthy
grand average generation. Furthermore, source analysis on a
single subject level analysis might not be fully trust-worthy
for these methods.
To investigate the reproducibility of single subject source
analysis we performed the source fitting procedure on single
subjects and evaluated how far from the modeled sources the
results were. The source distribution shown for every method in
Figure 6, followed by the statistical analysis of localization and
angle error shown in Figure 7 and Table 3, clearly indicated that
PCA-S, as well as ICA-S provided robust and focused results close
to the modeled signal (with mean error values around 20 mm
and 20◦). Most importantly, these methods successfully located
activity in the temporal lobe, with just sparse outliers. The OBS
and OBS-ICA seem to also lead to correct localization of sources
in the vicinity of the modeled sources, yet the high number of
outliers as visible in Figure 6 indicate that both methods distort
the signal in many cases. The BSS method did not allow for
trust-worthy source localization at single subject level at all.
Finally, we verified if the PCA-S method works also for non-
ERP data. The Berger experiment is clinically relevant and by far
the oldest procedure for evaluation of a non-dipolar, rhythmical
signal on continuous data level. In Figure 8 we show that the
PCA-S method proposed here allowed for successful BCG artifact
reduction and signal evaluation on both visual and computational
level (FFT heat maps, alpha rhythm topography). No signal
distortion and residual artifacts were observed.
Using this dataset, we also evaluated how PCA-S approach
handles BCG variability (Figure 9). Due to changes in cardiac
FIGURE 9 | BCG waveforms from a single subject (Berger experiment) for all
four artifact components used for artifact correction. Waveforms at the
beginning of the recording (10 s, blue line) and at the end of the recording
(355 s, red line) are shown. For each component the percentage value of
explained variance in the BCG template is indicated in the waveform caption.
FIGURE 10 | Different BCG artifact reduction pipelines for surrogate artifact
reduction methods.
rhythm and blood pressure the artifact changes over time (Oh
et al., 2014;Marino et al., 2018a). However, unless the subject
changes head position in the MR machine, the spatial distribution
is not affected by the aforementioned changes that only impact
the temporal aspects of BCG. In this regards the spatial filtering
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should be unaffected by the physiological artifact variation. The
artifact waveforms extracted by spatial filter from data at the
beginning and at the end were reviewed. There is a noticeable
difference in the waveforms that proved adaptation of data
correction to changes in temporal aspects of the artifact. Also,
even though first data block was extracted during the eyes-
opened state and the second during eyes-closed state, there is no
prominent oscillatory activity visible in any of the waveforms.
Furthermore, PCA-S, as well as ICA-S, can be easily extended
to account for spatial changes by applying regularization to the
artifact components, however, that could introduce a risk of
data distortion. Here it is worth adding that subject movement
in the MR machine is an even bigger issue for fMRI gradient
artifact removal, which is based on moving average artifact
subtraction, and happens prior to BCG artifact correction in
simultaneous EEG-fMRI data processing. Therefore, subject
movement should be avoided.
The surrogate approach presented here was already
successfully applied in our previous study where simultaneous
EEG-fMRI was used for time-frequency analysis of the
relationship between alpha rhythm and default mode network
in a group of adults (Rusiniak et al., 2018). Recently Plaska
et al. (2021, Preprint) applied this approach to evaluate
interhemispheric connectivity in working memory during a
visual stimulation task.
The classical implementation of the OBS method requires an
additional ECG electrode to create the BCG artifact template.
Moreover, the OBS method assumes a fixed delay (210 ms) of the
BCG artifact relative to the R-wave of the ECG (Niazy et al., 2005).
As mentioned before the recent findings show that this value
varies with blood transverse time (Oh et al., 2014;Marino et al.,
2018a). This problem was partially solved by deriving the triggers
for template averaging from the EEG (Marino et al., 2018b),
similarly to the creation of the averaged template in PCA-S.
In both approaches, the additional ECG channel is no longer
required, and the QRS-BCG timing difference is not an issue.
However, if no trigger is detected in the OBS-based methods, the
BCG artifact will not be subtracted from the signal at this time
point. Moreover, in the OBS approach there is no brain signal
modeling and artifact components are simply subtracted from the
data. In contrast, PCA-S and ICA-S provide a stationary spatial
filter (as defined by the inverse separating BCG and surrogate
components) that projects out all BCG-artifact components from
the EEG at each time point when an artifact occurs. A potential
disadvantage of spatial filtering is that a sufficient number of
recording channels is needed, amounting to at least the number
of artifact and brain source components to enable their separation
(Berg and Scherg, 1994a;Scherg et al., 2002). In contrast, a
subtraction procedure as used in OBS can be used even for
a single channel dataset. However, this limitation is of minor
importance in recent studies which use at least 32 EEG channels.
The BSS method uses a stationary spatial filter derived from
ICA. The ICA approach implemented here is prone to human
error as incorrect components may be selected, or selected
components may contain small parts of the ERP activities that
are removed with the artifact (Marino et al., 2018a). It is worth
mentioning that automatic and semi-automatic approaches do
exist (i.e., Srivastava et al., 2005;Mantini et al., 2007;Debener
et al., 2008), yet they suffer from the same ICA method
limitations. Since ERP signals are typically much smaller than the
background EEG, ICA rarely creates independent components
for the whole ERP that would be spatially orthogonal to the
removed ICA components. Thus, any part of the ERP spatially
correlated with the removed components will result in ERP
amplitude reduction and distortion if not modeled by another
component. In contrast, both PCA-S and ICA-S were specifically
designed to remove the correlation between artifact and brain
source components, based on the spatial filtering method of Berg
and Scherg (1994a). Since the BSS approach is based on ICA
and is independent of QRS detection, it acts as a spatial filter
on the whole EEG similarly to the surrogate model approaches.
However, the BSS approach is fully based on ICA requiring
perfect visual selection and separation of the BCG artifact
components. An additional problem with ICA-based approaches
is the vast amount of possible options for ICA computation
(Vanderperren et al., 2010). The BCG artifact is a complex multi-
dimensional signal due to the head movement in the strong
static magnetic field (translation and rotation in any direction)
caused by ballistic forces generated by the heart as well as single
electrode movements due to skin pulsation and the Hall effect
(Debener et al., 2008;Marino et al., 2018a). It might be scattered
over multiple independent components or might not be well
separated from another activity. While the manual BCG-related
components selection conducted here might be sub-optimal, it
is worth to underline that the same selection of ICA artifact
components was used in our BSS and ICA-S applications and
ICA-S outperformed BSS as well as OBS and OBS-ICA. The
fundamental difference of ICA-S is that these components are
not simply projected out by the spatial filter (potentially together
with some relevant brain activity), but instead they are used
as a model of the artifact when separating artifact and brain
surrogate components by the inverse filter. Thus, the brain
activities are preserved to a large extent in ICA-S as well as by
the PCA-S method.
Considering the basic problems of both approaches (OBS
and BSS) as shown and discussed here, this combination
does not solve the essential problem of subtracting out brain
signal together with the artifact in both approaches. While
OBS-ICA delivered slightly higher number of mean trial number
accepted for averaging, the decrease in all other parameters
(SNR, explained variance and localization error) was observed
as well. There was no statistical difference between OBS and
OBS-ICA but the distortion introduced by the additional ICA
step made the difference between OBS-ICA and both surrogate
methods (PCA-S and ICA-S) more significant. It is likely that
careful manual trimming of OBS and OBS-ICA parameters could
improve the efficiency of these methods, but this can be stated for
all other methods as well.
BCG artifact reduction is one of the major problems limiting
broader usage of simultaneous EEG-fMRI. Taking the above
into consideration, the big advantage of the OBS method is its
automation and ease of use as implemented in the FMRIB plug-in
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for the EEGLAB toolbox. As shown here, the PCA-S approach has
also been automated apart from the initial selection of one good
sample of the BCG artifact. This step could also be automated
for future applications (Marino et al., 2018b). Both PCA-S and
OBS use PCA to obtain the spatial topographies of the artifact.
PCA-S adjusts the number of components using a cutoff criterion
based on the variance of each component but does not subtract
these components directly from the EEG. In contrast, OBS needs
to limit the number of orthogonal PCA components, typically to
four, in order not to subtract too much other activity from the
EEG due to limited separation from the brain signal.
OBS, OBS-ICA, and BSS BCG artifact correction have to be
performed prior to the rejection of other artifacts, averaging,
and source analysis. Contrary to this, the spatial filter defined
by contrasting artifact and surrogate model components is a
linear operator which leads to other advantages: It can be applied
for the first time prior to the detection of other artifacts and
averaging (Figure 10). In addition, the averaging can be done
using the uncorrected EEG and the filter is then reapplied
to the averaged data prior to source analysis, since averaging
and filtering are linear operations and thus commutative (2nd
column in Figure 10). The last combination (3rd column in
Figure 10) would add the spatial artifact components to the
source model of the ERP in order to separate artifact and
ERP source activities. Thus, the impact of the number of
artifact components and their topographies can be assessed
at each stage of data processing in PCA-S and ICA-S and
adjusted if required.
In this study, a standard surrogate model of 29 regional
sources equally distributed over the brain was used in order to:
(a) allow for an automated application of the PCA-S approach
and (b) investigate the potential benefit of the surrogate approach
even in cases where the source model is not a perfect match
of the underlying ERP (e.g., in AEPs), plus for rhythmical
non-focal activity. The general surrogate brain activity model
might not fit perfectly to every brain data and could lead
to some minor distortion, but still the impact of this is
less severe than for any other method presented here. Please
note that the used surrogate model does not have a source
overlap with the seeded model as shown in Figure 1. The
adequate choice of an individually generated surrogate model
can further improve the rendering of an undistorted ERP when
spatial filtering is used for the separation of artifact and brain
source components (Berg and Scherg, 1994a). For example,
Siniatchkin et al. (2007) used an individual surrogate model of
interictal epileptiform discharges recorded outside of the magnet
and averaged previously. However, as they pointed out, the
limitation of such an approach is to procedures where test-retest
can be performed.
While in this paper the focus was on a broad evaluation
of source analysis improvement resulting from usage of PCA-S
and ICA-S, further assessment of this approach is needed
especially for time-frequency and single trial data analysis.
The high number of retained events for averaging (Figure 3)
might suggest that this approach might be effective for these
application types as well. Also, since the surrogate approach is
based on spatial filtering and does not need extensive computing
it potentially could be used for real-time data processing, after
preparing a BCG artifact template during a training phase
at the beginning of the recording. Here we performed the
evaluation using simulated ERPs superimposed on real EEG-
fMRI data. While this allowed for precise assessment of the
artifact correction quality there is a need for further evaluation
of this approach with real data tests, even though some positive
outcomes were already shown (Rusiniak et al., 2018;Plaska et al.,
2021, Preprint).
The present study demonstrates that BCG artifact reduction
techniques provide more reliable results when surrogate-based
spatial filtering is used to correct simultaneous EEG-fMRI
recordings especially for source analysis. While for simple ERP
evaluation all methods gave similar results, the proposed methods
of PCA-S and ICA-S successfully reduced BCG artifacts and
preserved the simulated brain signals much better than the
established methods of OBS, OBS-ICA, and BBS. This finding
was independent of the artifact modeling approach used (PCA
or ICA). We also showed that the approach proposed here can be
used for evaluation of continuous EEG (Berger experiment) and
is unaffected by temporal variation of the BCG artifact. Therefore,
the surrogate model approaches can be automated and applied to
all types of cognitive EEG-fMRI studies. They have already been
implemented in the BESA Research 7.1 software package, and a
detailed whole EEG-fMRI pipeline description is available (BESA,
2022).
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Ethics Committee of the Institute of Physiology
and Pathology of Hearing. The patients/participants provided
their written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
MR conceptualized the study, collected funds, performed data
analysis, and wrote the first draft of the manuscript. MR, HB, NI,
PB, and MS contributed to the methodology. MR, HB, J-HC, NI,
and PB worked on algorithms and their implementation. MR and
TW collected data and administered the project. MS supervised
the project. All authors contributed to manuscript revision, read,
and approved the submitted version.
FUNDING
EEG-fMRI data used in this manuscript was collected as
part of a grant from the Polish National Science Center no.
2011/01/N/NZ4/04985.
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Rusiniak et al. EEG-fMRI: Balistocardiongram Artifact Reduction
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Conflict of Interest: MR, HB, J-HC, NI, and PB were employees of BESA GmbH,
a company which develops and provides software tools for EEG and MEG data
analysis. MS was employee and shareholder of BESA GmbH.
The remaining author declares that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
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