International Journal of Psychophysiology 73 (2009) 53–61
Contents lists available at ScienceDirect
International Journal of Psychophysiology
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j p s yc h o
Mining EEG–fMRI using independent component analysis
Tom Eichele a,⁎, Vince D. Calhoun b,c, Stefan Debener d
a
Department of Biological and Medical Psychology, University of Bergen, 5009 Bergen, Norway
The Mind Research Network, Albuquerque, New Mexico, United States
c
Department of ECE, University of New Mexico, Albuquerque, New Mexico, United States
d
Biomagnetic Center, Dept. of Neurology, University Hospital Jena, Erlanger Allee 101, D-07747 Jena, Germany
b
a r t i c l e
i n f o
Article history:
Received 8 September 2008
Received in revised form 26 November 2008
Accepted 23 December 2008
Available online 15 February 2009
Keywords:
ICA
PCA
EEG
ERP
fMRI
Single trial analysis
Group analysis
a b s t r a c t
Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for
analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the
user to parameterize the brain's response to stimuli, ICA allows the researcher to explore the factors that
constitute the data and alleviates the need for explicit spatial and temporal priors about the responses. In this
paper, we introduce ICA for hemodynamic (fMRI) and electrophysiological (EEG) data processing, and one of
the possible extensions to the population level that is available for both data types. We then selectively
review some work employing ICA for the decomposition of EEG and fMRI data to facilitate the integration of
the two modalities to provide an overview of what is available and for which purposes ICA has been used. An
optimized method for symmetric EEG-fMRI decomposition is proposed and the outstanding challenges in
multimodal integration are discussed.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
Since its inception in 1992 (Frahm et al., 1992; Kwong et al., 1992;
Ogawa et al., 1992) functional magnetic resonance imaging has
become a major tool to study human brain function (Amaro and
Barker, 2006; Bandettini et al., 2000; Huettel et al., 2004). This was
propelled by the non-invasiveness, excellent spatial resolution,
flexible experiment design and repeatability. The signal in fMRI is
based on changes in magnetic susceptibility of the blood during brain
activation, it does not directly reflect neuro-electric activity in the
brain. The signal is based upon the complex coupling of neuronal
activity, metabolic activity and blood flow parameters in the brain
(Heeger and Ress, 2002; Logothetis and Wandell, 2004; Raichle and
Mintun, 2006). The hemodynamic response is delayed and smoothed
relative to the neuronal activity, and it is usually not possible to
reconstruct the neuro-electric process from the hemodynamic
process. Nevertheless, the hemodynamic signal remains a very
informative surrogate for neuronal activity. Moreover, what fMRI
alone lacks in temporal resolution and direct relationships with
neuro-electric activity could be compensated by the combination with
concurrent EEG measurements (Debener et al., 2006; Eichele et al.,
2005).
A wide variety of studies have been performed to delineate the
neuronal correlates of cognitive functions (for a review see e.g. Cabeza
and Nyberg, 2000), use activity patterns to predict perception and
behavior, identify aberrant regional activation in clinical populations,
and many more. Inferences made from fMRI data rely most often on
pre-specified temporal models, i.e. the correlation between a
predicted time course that models the activation to stimuli/responses
and the measured data. However, in many studies the temporal
dynamics of event related or intrinsic regional activations are difficult
to predict due to the lack of a well-understood brain-activation model.
In contrast, independent component analysis (ICA) is an exploratory,
data-driven tool that can reveal inter-subject and inter-event
differences in the temporal dynamics of the fMRI signal without a
prior model. ICA is increasingly utilized as a tool for evaluating the
hidden spatio-temporal structure contained within electrophysiological and hemodynamic brain imaging data. The strength of ICA is its
ability to reveal function-relevant dynamics for which a temporal
model cannot be specified a priori (Calhoun and Adali, 2006; Eichele
et al., 2008b), or is not available, such as in resting state data
(Damoiseaux et al., 2006; Fox and Raichle, 2007). Below, we will
introduce an ICA generative model and the extension of ICA to group
data. We will review how the method has been used in fMRI, EEG and
concurrent EEG-fMRI research, and identify some of the outstanding
challenges for future work.
2. Independent component analysis
⁎ Corresponding author. Tel.: +47 45224919; fax: +47 555 89872.
E-mail address: tom.eichele@psybp.uib.no (T. Eichele).
0167-8760/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.ijpsycho.2008.12.018
ICA is a multivariate statistical method used to uncover hidden
sources from multiple data channels (e.g., electrodes, microphones,
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T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
images) such that these sources are maximally independent. Typically, it
assumes a generative model where observations are assumed to be
linear mixtures of statistically independent sources. Unlike principal
component analysis (PCA), which decorrelates the data, ICA includes
higher-order statistics to achieve independence. An intuitive example of
ICA can be given by a scatter-plot of two independent signals s1 and s2.
Fig. 1a (left, middle) show the projections for PCA and ICA, respectively,
for a linear mixture of s1 and s2 and Fig. 1a shows a plot of the two
independent signals (s1 and s2) in a scatter-plot. PCA finds the orthogonal
vectors a1,a2, but cannot identify the independent vectors. In contrast,
ICA is able to find the independent vectors of the linearly mixed signals
(s1,s2), and is thus able to restore the original sources.
A typical ICA model assumes that the recorded signal consists of
statistically independent and non-Gaussian sources that are linearly
mixed. Consider an observed M–dimensional random vector denoted
by x = [x1,x2,⋯,xM]T:
x = As
ð1Þ
where s = [s1,s2,⋯,sN ]T is an N-dimensional vector whose elements are
the random variables that refer to the independent sources and AM × N
is an unknown mixing matrix. Typically M ≥ N, so that A is usually of
full rank. The goal of ICA is to estimate an unmixing matrix WN × M
such that y given by
y = Wx
ð2Þ
is a good approximation to the ‘true’ sources s.
Two commonly used ICA algorithms for EEG and fMRI data derived
within these formulations are Infomax (Bell and Sejnowski, 1995) and
FastICA (Hyvärinen et al., 2001). Both Infomax and FastICA typically
work with a fixed nonlinearity or one that is selected from a small
set, e.g., two in the case of extended Infomax (Lee et al., 1999). These
algorithms work well for symmetric distributions and are less
accurate for skewed distributions and for sources close to Gaussian.
An intuitive introduction into ICA is provided by Stone (Stone, 2004),
and a detailed mathematical description by Hyvärinen (Hyvärinen
et al., 2001).
3. ICA of EEG data
The initial application of single-subject temporal ICA to EEG data
was introduced by Makeig using multichannel event related potentials (Makeig et al., 1997). Since then, ICA of EEG signals has become
popular for a wide user community, facilitated through the opensource toolbox EEGLAB (www.sccn.eeglab.edu). The use of ICA for
multi-channel EEG recordings has been reviewed (Onton et al., 2006),
and a conceptual framework for using ICA for the study of eventrelated brain dynamics has been proposed by Makeig and colleagues
(Makeig et al., 2004b).
The popularity of ICA is in large part due to two key features. First,
it is a powerful way to remove artefacts from EEG data (Jung et al.,
2000a,b), and second, it helps to disentangle otherwise mixed brain
signals (Makeig et al., 2002). One of the most common artefacts
typically identified by ICA is shown in Fig. 2. Eye blinks can often be
easily identified by their characteristic topography and time course.
Also very common in EEG recordings are ICs reflecting lateral eye
movements, and muscle activity (EMG). ICA from high-density EEG
recordings typically reveals a number of these components. Beyond
artifacts, various examples have been published demonstrating the
potential of ICA for the separation of event-related brain activity
patterns (Debener et al., 2005a,b; Delorme et al., 2007; Makeig et al.,
2002; Onton et al., 2005). These studies have combined ICA with
single-trial EEG analysis, thereby allowing exploration of brain
dynamics beyond the evoked fraction of the signal that is preserved
in the ERP. Typically, the inspection of non-artefact ICs suggests that
some can be specifically linked to stimulus processing and explain
condition-specific variance, which affords functional inferences about
event-related component activity (for a review, see Onton and
Makeig, 2006).
4. ICA of fMRI data
Following its first application to fMRI (McKeown et al., 1998), ICA
has been successfully utilized in a number of fMRI studies, especially in
those that have proven challenging to analyze with the standard
regression-type approaches (Calhoun and Adali, 2006; McKeown et al.,
2003). Spatial ICA of fMRI finds systematically non-overlapping,
temporally coherent brain regions without constraining the shape of
the temporal response. Note that ICA can be used to discover either
spatially or temporally independent components (Stone, 2004). Due to
the data structure, in which the number of observed volume elements
(voxels) by far exceeds the number of observed time points, most
applications to fMRI use the former approach and seek components
that are maximally independent in space. The aim of ICA is then to
factor a two dimensional data matrix (voxels-by-timepoints) into a
product of a set of time courses and a set of independent spatial
patterns. The choice of spatial or temporal independence has been
somewhat controversial, although these are just two possible modeling assumptions. McKeown et al. (1998) argued that the sparse
distributed nature of the spatial pattern for typical cognitive activation
paradigms would work well with spatial ICA (sICA). Furthermore,
since the proto-typical image artifacts are also sparse and localized,
e.g., vascular pulsation, CSF flow signals in the ventricles, or breathing
induced motion (signal localized to strong tissue contrast near
discontinuities), the Infomax algorithm with a sparse prior is very
well suited for spatial analysis, and has also been used for temporal ICA
as have decorrelation-based algorithms (Calhoun et al., 2001; Petersen
et al., 2000). Stone et al. (1999) proposed a method which attempts to
maximize both spatial and temporal independence. An interesting
Fig. 1. Scatterplot of two independent, mixed signals illustrates the need for higher order statistics as an alternative to orthogonal projection as a means to faithfully un-mix the data.
T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
55
Fig. 2. Illustration of artefact removal from EEG data by means of ICA. A Section of selected channels from a multi-channel EEG recording is shown, with ongoing EEG oscillations in
the alpha range evident at occipital electrodes and two eye blinks at fronto-polar channels. B Un-mixing of the EEG data into a set of independent components. Each component can
be described on the basis of a spatial pattern (map) and a time course (activation). C Back-projection of all but components 1 and 2 reveals artefact-corrected EEG data.
combination of spatial and temporal ICA was presented by Seifritz et al.
(2002). They used an initial sICA to reduce the spatial dimensionality
of the data by locating a region of interest in which they then subsequently performed temporal ICA to study in more detail the structure of the response in the human auditory cortex.
5. Group ICA models for EEG and fMRI
Unlike univariate methods such as the general linear model (GLM),
ICA does not naturally generalize to a method suitable for drawing
inferences about observations from multiple subjects. For example,
when using the GLM, the investigator specifies a fixed set of
regressors, and so drawing inferences about group data comes
naturally, since all individuals in the group share the same regressors.
In ICA, by contrast, different individuals in the group will have
different time courses and component maps. Even if components are
very similar across subjects they require a subsequent clustering effort
to be grouped together. Accordingly, it is not immediately clear how to
draw inferences about group data by using single-subject ICA.
To overcome this problem, several multi-subject ICA approaches
have been proposed (Beckmann and Smith, 2005; Calhoun et al.,
2001; Esposito et al., 2005; Guo and Pagnoni, 2008; Lukic et al., 2002;
Schmithorst and Holland, 2004; Svensen et al., 2002). The various
approaches differ in terms of how the data are preprocessed and
organized prior to the ICA analysis and what types of outputs are
generated. One possibility is to perform single-subject ICA and then
attempt to combine the output into a group post hoc by using a
clustering metric or simple correlation of the components (Calhoun
et al., 2001; Esposito et al., 2005). This has the advantage of allowing
for unique spatial and temporal features, but has the disadvantage
that the components are not necessarily unmixed in the same way for
each subject.
Other approaches involve ICA computed on condensed group data.
The advantage of these approaches is that they perform one single ICA
on the group data, in which subject-specific and condition-specific
contributions can be identified. Note that a temporal concatenation
approach allows for unique time courses for each subject, but assumes
common group maps. A spatial concatenation approach on the other
hand allows for unique maps but assumes common time courses. It
appears that temporal concatenation works better for fMRI data (Guo
and Pagnoni, 2008; Schmithorst and Holland, 2004). Temporal
concatenation is implemented in MELODIC (http://www.fmrib.ox.ac.
uk/fsl/) and in GIFT (http://icatb.sourceforge.net/). GIFT additionally
implements a back-projection step which returns subject specific maps
and timecourses for further analysis (Calhoun et al., 2001; Eichele et al.,
2008a). A detailed comparison of several group ICA approaches
including temporal concatenation and tensor ICA is provided in recent
papers (Guo and Pagnoni, 2008; Schmithorst and Holland, 2004).
While group spatial ICA is more prominent in fMRI, it has been more
typical in EEG research to employ single-subject temporal ICA and
cluster individual component sets along features of interest for
inferences about groups of subjects (Debener et al., 2005a,b; Onton
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T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
et al., 2005, 2006). In the case of group temporal ICA on EEG time domain
data, the spatial concatenation, aggregation and data reduction with PCA
precedes component estimation, enabling the identification of components that contribute to event-related potentials (Eichele et al., 2008a),
and is implemented in EEGIFT (http://icatb.sourceforge.net/). A similar
group ICA model in combination with partial least squares was proposed
recently by Kovacevic (Kovacevic and McIntosh, 2007). The drawback
however is that processes which are not well time-locked across
subjects, such as ongoing EEG rhythms, cannot be satisfyingly
reconstructed in this model. The accuracy of component detection and
back-reconstruction with such a group ICA model depends on the degree
of intra- and inter-individual latency jitter of event related EEG processes
(see also Moosmann et al., 2008).
We focus on the group ICA approach implemented in the GIFT
software, which is freely available and has been designed for analysis of
EEG and fMRI data. GIFT uses multiple data reduction steps following
data concatenation, and enables a statistical comparison of individual
maps and time courses. GIFT essentially estimates a mixing matrix
which has partitions that are unique to each subject. Once the mixing
matrix is estimated, the component maps for each subject can be
computed by projecting the single subject data onto the inverse of the
partition of the unmixing matrix that corresponds to that subject.
1
Mathematically, if we let Xi = F−
Yi be the L × V reduced data matrix
i
from subject i, where Yi is the K × V data matrix containing the
1
preprocessed fMRI or EEG data, F−
is the L × K reducing matrix
i
determined by the PCA decomposition, V is the number of fMRI voxels
or EEG timepoints, K is the number of fMRI time points or EEG channels
and L is the size of the dimension following reduction. The reduced
data from all subjects is concatenated into a matrix and reduced using
PCA to N dimensions the number of components to be estimated. The
N × V reduced, concatenated matrix for the M subjects is
X=G
2
− 14
3
F−1
1 Y1
5:
v
F−1
Y
M
M
ð3Þ
where G− 1 is an N × M reducing matrix (also determined by a PCA
decomposition) and is multiplied on the right by the LM × V
concatenated data matrix for the M subjects. Following ICA estimation, we can write X =  Ŝ, where  is the N × N mixing matrix and Ŝ is
the N × V component fMRI map or EEG timecourse. Substituting this
expression for X into Eq. (3) and multiplying both sides by G, we have
2
GÂ Ŝ = 4
3
F−1
1 Y1
5:
v
−1
FM YM
ð4Þ
Partitioning the matrix G by subject provides the following
expression
3
2 −1
3
G1
F1 Y1
4 v 5Â Ŝ = 4
5:
v
GM
F−1
Y
M
M
2
ð5Þ
We then write the equation for subject i by working only with the
elements in partition i of the above matrices such that
−1
Gi  Ŝi = Fi Yi :
ð6Þ
The matrix Ŝi in Eq. (6) contains the single subject maps for subject
i and is calculated from the following equation
−1
−1
Fi Yi :
Ŝi = Gi Â
ð7Þ
We now multiply both sides of Eq. (6) by Fi and write
Yi ≈Fi Gi  Ŝi ;
ð8Þ
which provides the ICA decomposition of the data from subject i,
contained in the matrix Yi. The N × V matrix Sî contains the N source
maps in fMRI and timecourses in the EEG modality and the K × N
matrix FiGi  is the single subject mixing matrix and contains the fMRI
component time course or the EEG component topography for each of
the N components Fig. 3.
6. Application of ICA to concurrent EEG-fMRI recordings
It has become popular to collect multiple types of imaging and
other (e.g. genetic) data from the same participants, often in settings
where relatively large groups are sampled. Each imaging method
informs on a limited domain and typically provides both common and
unique information about the problem in question. Approaches for
combining or fusing data in brain imaging can be conceptualized as
having a place on an analytic spectrum with meta-analysis (highly
distilled data) to examine convergent evidence at one end and highly
detailed large-scale computational modeling at the other end (Husain
et al., 2002). In between are methods that attempt to perform a direct
data fusion (Horwitz and Poeppel, 2002). One promising data fusion
approach is to first process each image type and extract features from
different modalities. These features are then examined for relationships among the data types at the group level. This approach allows us
to take advantage of the ‘cross’-information among data types and
when performing multimodal fusion provides a natural link among
different data types (Ardnt, 1996; Savopol and Armenakis, 2002). Here
we focus selectively on ‘integration by prediction’, where typically
some feature from the EEG (e.g. alpha power, P300 amplitude) is
convolved with a canonical hemodynamic response function and used
as a predictor of hemodynamic activity in a GLM. Integration-byprediction is based on the assumption that the hemodynamic
response is linearly related to local changes in neuronal activity, in
particular local field potentials (Heeger and Ress, 2002; Lauritzen and
Gold, 2003; Logothetis et al., 2001). Since large-scale synchronous
field potentials are the basis for the scalp EEG (Nunez, 1995), such
integration can be achieved by investigating correlations between
BOLD and scalp EEG. This can be done either continuously over time,
as in the study of background rhythms and epileptic discharges in the
EEG (for a review see Laufs et al., 2008), or in the context of inducing
variation in some cognitive operation (Debener et al., 2006, 2005;
Eichele et al., 2005). ICA has been used in this context on many levels:
for artifact reduction during preprocessing, for feature extraction from
the EEG, decomposition of fMRI, parallel decomposition of EEG and
fMRI, and joint ICA of multimodal data.
6.1. EEG artifact reduction
Besides using ICA for feature extraction and making inferences, it
has been successfully employed during pre-processing for denoising
of EEG data acquired in the scanner. Temporal ICA is very useful for
artifact reduction in EEG data (Jung et al., 2000a,b), and it is similarly
useful in pre-processing of simultaneously acquired EEG–fMRI data.
Principally, the same artifacts affect the in-scanner EEG recording as
outside the scanner. In addition, in-scanner EEG recordings suffer
from further artifacts, most prominently the pulse artifact. A number
of authors have used ICA for removing the pulse artifact, given that
template-based rejection algorithms (Allen et al., 2000, 1998) do not
always sufficiently control for this type of artifact. ICA has been shown
to be helpful in reducing the pulse artifact only at lower field strengths
(Debener et al., 2008, 2007; Mantini et al., 2007a,b). Since this artifact
seems to violate the spatial stationarity assumption inherent in
temporal ICA, other existing tools such as optimal basis sets (OBS)
T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
57
Fig. 3. Prototypical independent components from fMRI data. The figure shows the activation maps of nine independent components from an event related fMRI study (Eichele et al.,
2008b) rendered onto the MNI template at representative transverse slices. The maps are shown in neurological convention (left hemisphere is on the left). Activations are plotted in
red, deactivations in blue. To the left of each map, the hemodynamic response functions within the respective ICs as estimated via deconvolution from 1–20 s after stimulus onset, in
arbitrary (range-scaled) amplitude units are displayed. The group average from the 13 participants is plotted as a solid line, error bars indicate ±1 S.E.M., dotted lines represent all
individual estimates. The empirical HRFs were used to estimate single trial amplitudes in the fMRI data. (For interpretation of the references to colour in this figure legend, the reader
is referred to the web version of this article.)
might be more advantageous (Niazy et al., 2005). It is our experience
that ICA returns better unmixing results when used subsequent to
pulse artifact correction with OBS or template-based rejection
algorithms (Debener et al., 2007).
6.2. EEG feature extraction
Another powerful option is to use ICA to identify and select a brain
signal of interest, such as the power modulation of the posterior alpha
rhythm (Feige et al., 2005), frontal theta (Scheeringa et al., 2008), or
single trial variability in the error-related negativity (ERN, Debener
et al., 2005). ICA here provides an unmixed and denoised source
waveform, and the assumption is that this improves the correlation
between EEG and fMRI signals. This approach assumes that unmixed
data allows a better matching of specific EEG timecourses to their
underlying spatial sources in the fMRI. An intuitive example is activity
in the 8–12 Hz range, and a common ICA observation often ignored in
the literature is that this frequency range is occupied by at least
posterior alpha and central mu sources. These rhythms are regionally
and functionally separable, and it is easily conceivable that sampling
alpha activity from single or grouped posterior electrodes in which all
these activities mix provides a less clear measure of any of these subprocesses than decomposition and selection of one feature (Feige
et al., 2005). Similar considerations apply to other rhythms, and to
event related activity. For example, the isolation of an ERN-like
topography and time course separates the variability associated with
response-locked error-related processing from stimulus-locked and
background activity (Debener et al., 2005).
In other circumstances where it is difficult to define a feature of
interest it seems more appropriate to employ ICA for artifact reduction
only and choose the back-projected, mixed EEG rather than the sources
for correlation analysis with the fMRI (Eichele et al., 2005). The results
from both method choices may provide comparable results (Bagshaw
and Warbrick, 2007). However, one caveat is that temporal independence in the EEG does not necessarily equate with different regional
generators of the activity in the fMRI, such that a variety of scenarios
are conceivable in which unmixing of the EEG could reduce sensitivity
of the EEG–fMRI correlation. Also, although the mixing problem is
acknowledged and addressed in the EEG, this is not extended in these
publications to the treatment of fMRI data, where it exists as well.
6.3. fMRI decomposition
While the work cited above used ICA to decompose EEG data for a
better integration with the fMRI BOLD signal, more recently
alternative approaches have been used. Here, ICA is applied to address
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T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
the spatio-temporal mixing problem that is inherent in fMRI data as
well, while the EEG signals were not subjected to ICA. For instance,
Mantini et al. (2009, 2007b) decomposed resting state and event
related fMRI data with fastICA and subsequent component clustering
across subjects and extracted distributed regional networks. In the
resting state data (Mantini et al., 2007b), the BOLD signals of these
networks were correlated with power fluctuations in the delta, theta,
alpha, beta, and gamma bands from concurrently recorded EEG. Each
network was characterized by a signature that involved variable
contributions from the different bands. The interesting idea of this
work is the perspective that wide-band EEG activity principally
describes large-scale brain networks more comprehensively than
narrow-band activity. In the event related data (Mantini et al., 2009),
the amplitude fluctuation of the P300 revealed correlations with
attentional networks. The approach illustrates a means of how to
meaningfully reduce the multiple testing problems in fMRI without
defining regions of interest a priori. However, again, only one modality
was unmixed. In this case the EEG remained mixed.
can use the convolved trial-by-trial modulation in EEG components to
predict the fMRI, or (vice versa) employ deconvolution and single trial
estimation on the fMRI component timecourses, and use these as
predictors for the trial-by-trial amplitude modulation in the EEG.
Parallel group ICA provides a means to disentangle and visualize large
scale networks both in their spatial and temporal form, given coherent
neuronal sources jointly express scalp electrophysiologic and hemodynamic features (Calhoun and Adali, 2006; Debener et al., 2006;
Makeig et al., 2004a; McKeown et al., 2003; Onton et al., 2006).
6.5. Joint ICA for multimodal data
Joint ICA (jICA) is another approach, which enables the joint
decomposition of multi-modal data that have been collected from the
same sample of subjects. Here, we primarily consider a set of extracted
features from each subject's data, and these data form the multiple
observations. Given two (or more) sets of group data, XF and XG we
concatenate the two datasets side-by-side to form XJ and write the
likelihood as
6.4. Parallel EEG-fMRI decomposition
The two approaches described above each solve a part of the
mixing problem and make way for refined spatio-temporal mapping.
However, the choice to unmix only one modality but not the other is
somewhat inconsistent with the reasoning that leads researchers to
use ICA in the first place. We now lay out how parallel unmixing could
be used for EEG-fMRI integration.
The motivation for implementing parallel EEG-fMRI decomposition is based on the data structure and the presumed physiology of
signal generation in EEG and fMRI. First, consider that a concurrent
experiment generates a multidimensional dataset consisting of about
104 to 105 volume elements by 102 to 103 timepoints sampled in the
fMRI, by 102 to 103 timepoints by 102 to 103 trials by 32–128 scalp
channels sampled in the EEG, by a number of participants that
constitute a group study, providing a rich and complex source of
information. The utility of ICA lies in the visualization and exploratory
assessment of such data. Second, we can assume that processing of
simple stimuli and tasks produces widespread event-related
responses that are spatially and temporally mixed across the brain
(Baudena et al., 1995; Halgren et al., 1995a,b; Halgren and Marinkovic,
1995; Makeig et al., 2004a; Onton et al., 2006). The scalp EEG samples
a spatially degraded version of brain activity, where the potential is a
mixture of independent timecourses from large-scale synchronous
field potentials. Similarly, the neurovascular transformation of the
electrophysiological activity into hemodynamics provides temporally
degraded and spatially mixed signals across the fMRI volume
(Calhoun and Adali, 2006; McKeown et al., 2003).
The spatial and temporal mixing in both modalities creates
situations in which prediction of fMRI activity by EEG features in a
mass–univariate voxel-by-voxel framework may be difficult since
neither the predictor, nor the response variables are any likely to
represent single and coupled sources of variability. Thus, one
improvement to achieve a more symmetric treatment of the data is
to unmix both modalities in parallel. We have developed such an
analysis framework for multisubject data that employs Infomax ICA
(Bell and Sejnowski, 1995) to recover a set of statistically independent
maps from the fMRI, and independent time-courses from the EEG
separately (Eichele et al., 2008a). We utilized the approach that is
described above, i.e. create aggregate data containing observations
from all subjects, estimate a single set of ICs and then back-reconstruct
these in the individual data (Calhoun et al., 2001; Schmithorst and
Holland, 2004). The analysis is done on the single trial level rather
than averaged data (cf. Calhoun et al., 2006), and it does not assume a
joint mixing matrix for both modalities (cf. Calhoun et al., 2006; cf.
Moosmann et al., 2008). The components are matched across
modalities by correlating their trial-to-trial modulation, where we
LðWÞ =
N
V
Y
Y
n=1 v=1
pJ;n uJ;v ;
ð9Þ
where uJ = WxJ. Here, we use the notation in terms of random variables
such that each entry in the vectors uJ and xJ correspond to a random
variable, which is replaced by the observation for each sample n = 1,…,N
as rows of matrices UJ and XJ. When posed as a maximum likelihood
problem, we estimate a joint unmixing matrix W such that the likelihood
L(W) is maximized.
Let the two datasets XF and XG have dimensionality N × V1 and
N × V2, then we have
LðWÞ =
N
Y
n=1
V1
Y
v=1
!
V2
Y
pF;n uF;v
pG;n uG;v ;
ð10Þ
v=1
Depending on the data types in question, the above formula can be
made more or less flexible. This formulation characterizes the basic
jICA approach and assumes that the sources associated with the two
data types modulate the same way across N subjects. Note that the
assumption of the same linear covariation for both modalities is fairly
strong. However, it has the advantage of providing a parsimonious
way to link multiple data types and has been demonstrated in a
variety of cases with meaningful results, for example for fusing ERPs
with fMRI contrast images (Calhoun, Adali et al., 2006). The extension
of joint ICA for concurrent single trial data is described in a simulation
study by Moosmann and colleagues (Moosmann et al., 2008).
Although robust results can be acquired with this methodology,
broader validity of the application to real single trial data would
require an iterative procedure with estimation and removal of the
hemodynamic transfer function prior to fusion (see below).
7. Challenges
The methodological and conceptual development in the field of
multimodal integration is ongoing, and ICA plays a prominent role in
this effort. With respect to EEG-fMRI integration we will highlight two
issues that are currently being addressed. First, we discuss the utility
of hemodynamic deconvolution and single trial estimation in the
fMRI, and second, the need for more flexible modeling of the EEG-fMRI
coupling.
7.1. Deconvolution
An outstanding problem that was not addressed in the work
described above is the assumptions made about the hemodynamic
T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
response function (HRF). Most previous concurrent EEG-fMRI studies
assumed a generic transfer HRF to the transient neuronal response
evoked by discrete sensory stimulation (e.g. derived from Boynton et al.,
1996) to model the hemodynamic activation, whereby the parameters of
the HRF such as shape and latency of the peak and undershoot are
implicitly assumed to be fixed across the entire brain and across subjects.
However, quite some variability of the HRF exists across regions and
subjects (Aguirre et al., 1998; Glover, 1999; Handwerker et al., 2004).
Inclusion of temporal and dispersion derivative terms of the HRF into the
model can alleviate such differences on the first level. However, it ignores
the potential for amplitude bias induced by model mismatches due to
variable hemodynamic delays and is typically not helpful for 2nd level
random effects analyses (Calhoun et al., 2004). Therefore, in order to
achieve a more sensitive and less biased analysis it is more desirable to
estimate subject- and region-specific HRFs. Another consideration is that
the eliciting conditions that generate a response which conforms to the
canonical HRF are not necessarily valid for intrinsic/resting state activity
and also more complex cognitive operations. An illustrative example of
estimating the HRF from EEG-data has been published by de Munck et al.
(2008, 2007). Here, HRFs were directly estimated from the alpha power
modulation in concurrent EEG–fMRI data, and indeed show systematic
latency and shape differences from the canonical HRF. The regional
activation with significant HRFs was much more widespread than what
59
was previously reported, which can be taken as an indication that the
sparser activation patterns in earlier studies (Goldman et al., 2002;
Moosmann et al., 2003) could have resulted from poor sensitivity.
Apart from estimating the HRF from the EEG, event related HRFs
can be estimated from independent component timecourses through
deconvolution (Eichele et al., 2008b). While typically noise-tolerant
models are needed for voxel-by-voxel deconvolution, ICA forms large
regions of interest with quasi-denoised time courses such that
deconvolution can be done simply by forming a convolution matrix
of the stimulus onsets with an assumed duration of the HRF. FMRI
single trial response amplitudes can then be recovered by fitting a
design matrix (X) containing separate predictors for the onset times of
each trial convolved with the estimated HRF onto the IC timecourse,
estimating the scaling coefficients (β) in the multiple linear regression
model y = β·X + ε using least squares. In this approach, the HRF is
estimated and then effectively removed from the hemodynamic data,
leaving just the amplitude modulation across trials, which has the
same resolution as the EEG trial-by-trial dynamics (see Fig. 4).
7.2. Integration
There are a variety of ways how one can conceive the coupling
between electrophysiology and hemodynamic signals. Linear regression
Fig. 4. EEG–fMRI integration with deconvolution. The spatial ICA of the fMRI data results in individual maps and timecourses. The single trial HRF amplitude modulation estimated from
the IC timecourses is used for prediction of EEG activity. In order to recover the amplitude modulation (AM), the pseudoinverse of a convolution matrix generated from the stimulus
timing and an assumed HRF length is multiplied with the IC timecourse, yielding individually and regionally specific HRFs. These HRFs are then convolved separately with each stimulus
onset, yielding a design matrix (X) with predictors for each trial 1..n. The regression of the design onto the IC timecourse (y) yields the single trial amplitude modulation for this IC (β1..
βn). In the EEG, a group temporal ICA provides independent source timecourses, whose trial-by-trial modulation are extracted and correlated with the fMRI activity.
60
T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
between fMRI and EEG is usually used in combination with ICA to
investigate links between modalities (e.g., Debener et al., 2006). In
searching for such one-to-one mappings it is assumed that a particular
EEG feature is related to a particular fMRI network. Although this is
justifiable since both the fMRI maps and EEG topographies are different
from each other and stationary, it appears oversimplified since in
principle several fMRI networks can affect several EEG signals. One
explanation might be that one component is a generator, i.e. it has an
open field configuration, is reasonably large and close to the cortex
surface to generate a scalp potential, while other components
transiently and remotely modulate this source, while being electrophysiologically silent on the scalp, since these otherwise should induce
topographic variability in the EEG. This explanation already implies that
the fMRI networks are coupled with each other, which is reflected in
their functional connectivity (Eichele et al., 2008b; Jafri et al., 2008), and
effective connectivity (Stevens et al., 2007). Another related explanation
is that the fMRI networks might be considered spatially independent
nodes in regionally distributed, functionally coherent source networks,
such that many nodes can contribute directly or indirectly to the scalp
potentials in a given paradigm/task. Here, the EEG components,
although temporally independent on the scalp, would not represent a
single source but still a weighted average of multiple spatially
independent regional sources. Both these explanations are physiologically plausible (Baudena et al., 1995; Halgren et al., 1995a,b; Halgren and
Marinkovic,1995), and although it is typically not the aim of integrationby-prediction to directly solve the inverse problem, it is helpful to
consider in which way the topographies and timecourses relate to each
other.
If we assume that many fMRI networks affect a particular EEG feature
one way of addressing this is by employing multiple regression with a
design that contains all fMRI trial-by-trial modulations for prediction of
EEG activity. The problem of collinearity between regressors can be
addressed by orthogonalization (Andrade et al., 1999), stepwise
selection, or a decomposition of the fMRI component timecourses into
set of unrelated factors by means of e.g. PCA or ICA (similar to the
procedure in Seifritz et al., 2002). However, such a treatment also means
that the interpretation of the results becomes less straightforward.
Other options include canonical correlation analysis (CCA) in order to
treat the problem. Here, however, 2nd level inference is less well
defined. Also, a joint (temporal) ICA of the sIC and tIC trial-by-trial
modulations appears feasible (Calhoun et al., 2006; Moosmann et al.,
2008), but has as yet not been explored in detail. A conceptually more
advanced approach that, however, needs detailed specifications would
be to adapt dynamic causal models that explain the EEG/ERP responses
as changes in the effective connectivity between independent sources
(Friston, 2005; Friston et al., 2003; Garrido et al., 2007).
8. Conclusion
ICA is a powerful data driven approach that has been successfully
used to analyze EEG, fMRI, and simultaneous EEG-fMRI data. It has
been shown repeatedly that the integrations of both modalities can be
achieved on the (statistical) source level, as provided by ICA. The
overview provided here demonstrates the utility and diversity of the
various existing ICA-based approaches for the analysis of brain
imaging data. Two key challenges in the field, the estimation of the
HRF and a more flexible way of data integration, were identified.
Multimodal integration by means of ICA is still under development.
However, we expect ICA to contribute to more refined ways of
achieving comprehensive joint mapping of electrophysiology and
hemodynamic signals.
Acknowledgment
This work was supported by a grant from the L. Meltzer University
Fund (801616) to TE, and by the National Institutes of Health, under
Grants 1 R01 EB 000840, 1 R01 EB 005846, and 1 R01 EB 006841 to
VDC.
References
Aguirre, G.K., Zarahn, E., D'Esposito, M., 1998. The variability of human, BOLD
hemodynamic responses. Neuroimage 8, 360–369.
Allen, P.J., Polizzi, G., Krakow, K., Fish, D.R., Lemieux, L., 1998. Identification of EEG events
in the MR scanner: the problem of pulse artifact and a method for its subtraction.
Neuroimage 8, 229–239.
Allen, P.J., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from
continuous EEG recorded during functional MRI. Neuroimage 12, 230–239.
Amaro Jr., E., Barker, G.J., 2006. Study design in fMRI: basic principles. Brain Cogn. 60,
220–232.
Andrade, A., Paradis, A.L., Rouquette, S., Poline, J.B., 1999. Ambiguous results in
functional neuroimaging data analysis due to covariate correlation. Neuroimage 10,
483–486.
Ardnt, C., 1996. Information Gained by Data Fusion. SPIE Proc., vol. 2784.
Bagshaw, A.P., Warbrick, T., 2007. Single trial variability of EEG and fMRI responses to
visual stimuli. Neuroimage 38, 280–292.
Bandettini, P.A., Birn, R.M., Donahue, K.M., 2000. Functional MRI. Background,
methodology, limits and implementation. In: Cacioppo, J.T., Tassinary, L.G.,
Berntson, G.G. (Eds.), Handbook of Psychophysiology. Cambridge University
Press, Cambridge, UK.
Baudena, P., Halgren, E., Heit, G., Clarke, J.M., 1995. Intracerebral potentials to rare target
and distractor auditory and visual stimuli. III. Frontal cortex. Electroencephalogr.
Clin. Neurophysiol. 94, 251–264.
Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component
analysis for multisubject FMRI analysis. Neuroimage 25, 294–311.
Bell, A.J., Sejnowski, T.J., 1995. An information-maximization approach to blind
separation and blind deconvolution. Neural Comput. 7, 1129–1159.
Boynton, G.M., Engel, S.A., Glover, G.H., Heeger, D.J., 1996. Linear systems analysis of
functional magnetic resonance imaging in human V1. J. Neurosci. 16, 4207–4221.
Cabeza, R., Nyberg, L., 2000. Imaging cognition II: An empirical review of 275 PET and
fMRI studies. J. Cogn. Neurosci. 12, 1–47.
Calhoun, V., Adali, T., 2006. Unmixing fMRI with independent component analysis. IEEE
Eng. Med. Biol. Magazine 25, 79–90.
Calhoun, V.D., Adali, T., McGinty, V., Pekar, J.J., Watson, T., Pearlson, G.D., 2001. fMRI
activation in a visual-perception task: network of areas detected using the general
linear model and independent component analysis. NeuroImage 14, 1080–1088.
Calhoun, V.D., Adali, T., Pearlson, G.D., Kiehl, K.A., 2006. Neuronal chronometry of target
detection: fusion of hemodynamic and event-related potential data. Neuroimage
30, 544–553.
Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group
inferences from functional MRI data using independent component analysis. Hum.
Brain Mapp. 14, 140–151.
Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI analysis with the
general linear model: removal of latency-induced amplitude bias by incorporation
of hemodynamic derivative terms. Neuroimage 22, 252–257.
Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M.,
Beckmann, C.F., 2006. Consistent resting-state networks across healthy subjects.
Proc. Natl. Acad. Sci. U. S. A. 103, 13848–13853.
de Munck, J.C., Goncalves, S.I., Huijboom, L., Kuijer, J.P., Pouwels, P.J., Heethaar, R.M.,
Lopes da Silva, F.H., 2007. The hemodynamic response of the alpha rhythm: an EEG/
fMRI study. Neuroimage 35, 1142–1151.
de Munck, J.C., Goncalves, S.I., Faes, T.J., Kuijer, J.P., Pouwels, P.J., Heethaar, R.M., Lopes da
Silva, F.H., 2008. A study of the brain's resting state based on alpha band power,
heart rate and fMRI. Neuroimage 42 (1), 112–121.
Debener, S., Makeig, S., Delorme, A., Engel, A.K., 2005a. What is novel in the novelty oddball
paradigm? Functional significance of the novelty P3 event-related potential as
revealed by independent component analysis. Brain Res. Cogn. Brain Res. 22, 309–321.
Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., von Cramon, D.Y., Engel, A.K., 2005b. Trialby-trial coupling of concurrent electroencephalogram and functional magnetic
resonance imaging identifies the dynamics of performance monitoring. J. Neurosci.
25, 11730–11737.
Debener, S., Ullsperger, M., Siegel, M., Engel, A.K., 2006. Single-trial EEG–fMRI reveals
the dynamics of cognitive function. Trends Cogn. Sci. 10, 558–563.
Debener, S., Strobel, A., Sorger, B., Peters, J., Kranczioch, C., Engel, A.K., Goebel, R., 2007.
Improved quality of auditory event-related potentials recorded simultaneously
with 3-T fMRI: removal of the ballistocardiogram artefact. Neuroimage 34, 587–597.
Debener, S., Mullinger, K.J., Niazy, R.K., Bowtell, R.W., 2008. Properties of the
ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7 T static
magnetic field strength. Int. J. Psychophysiol. 67, 189–199.
Delorme, A., Westerfield, M., Makeig, S., 2007. Medial prefrontal theta bursts precede rapid
motor responses during visual selective attention. J. Neurosci. 27, 11949–11959.
Eichele, T., Calhoun, V.D., Moosmann, M., Specht, K., Jongsma, M.L., Quiroga, R.Q.,
Nordby, H., Hugdahl, K., 2008a. Unmixing concurrent EEG–fMRI with parallel
independent component analysis. Int. J. Psychophysiol. 67, 222–234.
Eichele, T., Debener, S., Calhoun, V.D., Specht, K., Engel, A.K., Hugdahl, K., von Cramon, D.Y.,
Ullsperger, M., 2008b. Prediction of human errors by maladaptive changes in eventrelated brain networks. Proc. Natl. Acad. Sci. U. S. A. 105, 6173–6178.
Eichele, T., Specht, K., Moosmann, M., Jongsma, M.L., Quiroga, R.Q., Nordby, H., Hugdahl, K.,
2005. Assessing the spatiotemporal evolution of neuronal activation with single-trial
event-related potentials and functional MRI. Proc. Natl. Acad. Sci. U. S. A. 102,
17798–17803.
T. Eichele et al. / International Journal of Psychophysiology 73 (2009) 53–61
Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., Tedeschi,
G., Goebel, R., Seifritz, E., Di Salle, F., 2005. Independent component analysis of fMRI
group studies by self-organizing clustering. Neuroimage 25, 193–205.
Feige, B., Scheffler, K., Esposito, F., Di Salle, F., Hennig, J., Seifritz, E., 2005. Cortical
and subcortical correlates of electroencephalographic alpha rhythm modulation.
J. Neurophysiol. 93, 2864–2872.
Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with
functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711.
Frahm, J., Bruhn, H., Merboldt, K.D., Hanicke, W., 1992. Dynamic MR imaging of human
brain oxygenation during rest and photic stimulation. J. Magn. Reson. Imaging 2,
501–505.
Friston, K.J., 2005. A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci.
360, 815–836.
Friston, K.J., Harrison, L., Penny, W., 2003. Dynamic causal modelling. Neuroimage 19,
1273–1302.
Garrido, M.I., Kilner, J.M., Kiebel, S.J., Friston, K.J., 2007. Evoked brain responses are
generated by feedback loops. Proc. Natl. Acad. Sci. U. S. A. 104, 20961–20966.
Glover, G.H., 1999. Deconvolution of impulse response in event-related BOLD fMRI.
Neuroimage 9, 416–429.
Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., 2002. Simultaneous EEG and fMRI of
the alpha rhythm. Neuroreport 13, 2487–2492.
Guo, Y., Pagnoni, G., 2008. A unified framework for group independent component
analysis for multi-subject fMRI data. Neuroimage 42, 1078–1093.
Halgren, E., Marinkovic, K., 1995. General principles for the physiology of cognition as
suggested by intracranial ERPs. In: Ogura, C., Koga, Y., Shimokochi, M. (Eds.),
Recent Advances in Event-Related Brain Potential Research. Elsevier, Amsterdam,
pp. 1072–1084.
Halgren, E., Baudena, P., Clarke, J.M., Heit, G., Liegeois, C., Chauvel, P., Musolino, A., 1995a.
Intracerebral potentials to rare target and distractor auditory and visual stimuli. I.
Superior temporal plane and parietal lobe. Electroencephalogr. Clin. Neurophysiol.
94, 191–220.
Halgren, E., Baudena, P., Clarke, J.M., Heit, G., Marinkovic, K., Devaux, B., Vignal, J.P.,
Biraben, A., 1995b. Intracerebral potentials to rare target and distractor auditory and
visual stimuli. II. Medial, lateral and posterior temporal lobe. Electroencephalogr.
Clin. Neurophysiol. 94, 229–250.
Handwerker, D.A., Ollinger, J.M., D'Esposito, M., 2004. Variation of BOLD hemodynamic
responses across subjects and brain regions and their effects on statistical analyses.
Neuroimage 21, 1639–1651.
Heeger, D.J., Ress, D., 2002. What does fMRI tell us about neuronal activity? Nat. Rev.
Neurosci. 3, 142–151.
Horwitz, B., Poeppel, D., 2002. How can EEG/MEG and fMRI/PET data be combined?
Hum. Brain Mapp. 17, 1–3.
Huettel, S.A., Song, A.W., McCarthy, G., 2004. Functional Magnetic Resonance Imaging.
Sinauer, Sunderland, MA.
Husain, F.T., Nandipati, G., Braun, A.R., Cohen, L.G., Tagamets, M.A., Horwitz, B., 2002.
Simulating transcranial magnetic stimulation during PET with a large-scale neural
network model of the prefrontal cortex and the visual system. NeuroImage 15,
58–73.
Hyvärinen, A., Karhunen, J., Oja, E., 2001. Independent Component Analysis. John Wiley
& Sons, New York.
Jafri, M.J., Pearlson, G.D., Stevens, M., Calhoun, V.D., 2008. A method for functional
network connectivity among spatially independent resting-state components in
schizophrenia. Neuroimage 39, 1666–1681.
Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M.J., Iragui, V., Sejnowski, T.J.,
2000a. Removing electroencephalographic artifacts by blind source separation.
Psychophysiology 37, 163–178.
Jung, T.P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., Sejnowski, T.J.,
2000b. Removal of eye activity artifacts from visual event-related potentials in
normal and clinical subjects. Clin. Neurophysiol. 111, 1745–1758.
Kovacevic, N., McIntosh, A.R., 2007. Groupwise independent component decomposition
of EEG data and partial least square analysis. Neuroimage 35, 1103–1112.
Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P.,
Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., et al., 1992. Dynamic magnetic
resonance imaging of human brain activity during primary sensory stimulation.
Proc. Natl. Acad. Sci. U. S. A. 89, 5675–5679.
Laufs, H., Daunizeau, J., Carmichael, D.W., Kleinschmidt, A., 2008. Recent advances in
recording electrophysiological data simultaneously with magnetic resonance
imaging. Neuroimage 40, 515–528.
Lauritzen, M., Gold, L., 2003. Brain function and neurophysiological correlates of signals
used in functional neuroimaging. J. Neurosci. 23, 3972–3980.
Lee, T., Girolami, M., Sejnowski, T., 1999. Independent component analysis using an
extended infomax algorithm for mixed subgaussian and supergaussian sources.
Neural Comput. 11, 417–441.
Logothetis, N.K., Wandell, B.A., 2004. Interpreting the BOLD signal. Annu. Rev. Physiol.
66, 735–769.
Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., 2001. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157.
61
Lukic, A.S., Wernick, M.N., Hansen, L.K., Strother, S.C., 2002. An ICA algorithm for
analyzing multiple data sets. Int.Conf.on Image Processing (ICIP).
Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D., Sejnowski, T.J., 1997. Blind separation of
auditory event-related brain responses into independent components. Proc. Natl.
Acad. Sci. U. S. A. 94, 10979–10984.
Makeig, S., Westerfield, M., Jung, T.P., Enghoff, S., Townsend, J., Courchesne, E.,
Sejnowski, T.J., 2002. Dynamic brain sources of visual evoked responses. Science
295, 690–694.
Makeig, S., Debener, S., Onton, J., Delorme, A., 2004a. Mining event-related brain
dynamics. Trends Cogn. Sci. 8, 204–210.
Makeig, S., Debener, S., Onton, J., Delorme, A., 2004b. Mining event-related brain
dynamics. Trends Cogn. Sci. 8, 204–210.
Mantini, D., Perrucci, M.G., Cugini, S., Ferretti, A., Romani, G.L., Del Gratta, C., 2007a.
Complete artifact removal for EEG recorded during continuous fMRI using
independent component analysis. Neuroimage 34, 598–607.
Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M., 2007b.
Electrophysiological signatures of resting state networks in the human brain.
Proc. Natl. Acad. Sci. U. S. A. 104, 13170–13175.
Mantini, D., Corbetta, M., Perrucci, M.G., Romani, G.L., Del Gratta, C., 2009. Large-scale
brain networks account for sustained and transient activity during target detection.
Neuroimage 44, 265–274.
McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., Sejnowski,
T.J., 1998. Analysis of fMRI data by blind separation into independent spatial
components. Hum. Brain Mapp. 6, 160–188.
McKeown, M.J., Hansen, L.K., Sejnowski, T.J., 2003. Independent component analysis of
functional MRI: what is signal and what is noise? Curr. Opin. Neurobiol. 13, 620–629.
Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., Taskin, B., Obrig,
H., Villringer, A., 2003. Correlates of alpha rhythm in functional magnetic resonance
imaging and near infrared spectroscopy. Neuroimage 20, 145–158.
Moosmann, M., Eichele, T., Nordby, H., Hugdahl, K., Calhoun, V.D., 2008. Joint
independent component analysis for simultaneous EEG–fMRI: principle and
simulation. Int. J. Psychophysiol. 67, 212–221.
Niazy, R.K., Beckmann, C.F., Iannetti, G.D., Brady, J.M., Smith, S.M., 2005. Removal of
FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28,
720–737.
Nunez, P.L., 1995. Neocortical Dynamics and Human EEG Rhythms. Oxford University
Press, New York.
Ogawa, S., Tank, D.W., Menon, R.S., Ellermann, J.M., Kim, S.G., Merkle, H., Ugurbil, K.,
1992. Intrinsic signal changes accompanying sensory stimulation: functional brain
mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U. S. A. 89,
5951–5955.
Onton, J., Makeig, S., 2006. In: Neuper, C., Pfurtscheller, G. (Eds.), Information-based
Modeling Of Event-Related Brain Dynamics. Progress in Brain Research, vol. 159.
Elsevier, Amsterdam, pp. 99–120.
Onton, J., Delorme, A., Makeig, S., 2005. Frontal midline EEG dynamics during working
memory. Neuroimage 27, 341–356.
Onton, J., Westerfield, M., Townsend, J., Makeig, S., 2006. Imaging human EEG dynamics
using independent component analysis. Neurosci. Biobehav. Rev. 30, 808–822.
Petersen, K., Hansen, L., Kolenda, T., Rostrup, E., Strother, S., 2000. On the independent
components of functional neuroimages. Int.Conf.on ICA and BSS, pp. 615–620.
Raichle, M.E., Mintun, M.A., 2006. Brain work and brain imaging. Annu. Rev. Neurosci.
29, 449–476.
Savopol, F., Armenakis, C., 2002. Mergine of heterogeneous data for emergency
mapping: data integration or data fusion? Proc.ISPRS.
Scheeringa, R., Bastiaansen, M.C., Petersson, K.M., Oostenveld, R., Norris, D.G., Hagoort,
P., 2008. Frontal theta EEG activity correlates negatively with the default mode
network in resting state. Int. J. Psychophysiol. 67, 242–251.
Schmithorst, V.J., Holland, S.K., 2004. Comparison of three methods for generating
group statistical inferences from independent component analysis of functional
magnetic resonance imaging data. J. Magn. Reson. Imaging 19, 365–368.
Seifritz, E., Esposito, F., Hennel, F., Mustovic, H., Neuhoff, J.G., Bilecen, D., Tedeschi, G.,
Scheffler, K., Di Salle, F., 2002. Spatiotemporal pattern of neural processing in the
human auditory cortex. Science 297, 1706–1708.
Stevens, M.C., Kiehl, K.A., Pearlson, G.D., Calhoun, V.D., 2007. Functional neural
networks underlying response inhibition in adolescents and adults. Behav. Brain
Res. 181, 12–22.
Stone, J.V., 2004. Independent Component Analysis: A Tutorial Introduction. MIT press,
Cambridge, MA.
Stone, J.V., Porrill, J., Buchel, C., Friston, K., 1999. Spatial, temporal, and spatiotemporal
independent component analysis of fMRI data. Proc.Leeds Statistical Research
Workshop.
Svensen, M., Kruggel, F., Benali, H., 2002. ICA of fMRI Group Study Data. NeuroImage 16,
551–563.