Signal Processing - May 2016 - 88
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FIGure 1. The spatiotemporal domain of neuroscience and the main methods available for the study of the nervous system in 2014. Each colored region
represents the spatial and temporal resolution range for one specific method available for the study of brain function. Open regions represent measurement techniques; filled regions represent perturbation techniques. The inset shows a cartoon rendition of the methods available in 1988, which is notable
for large gaps where no available methods existed. MEG: magnetoencephalography; PET: positron emission tomography; VSD: voltage-sensitive dye;
TMS: transcranial magnetic stimulation; 2-DG: 2-deoxyglucose. (Figure used with permission from [5].)
and fMRI signals. In clinical applications such as epilepsy,
high temporal resolution is needed to capture the temporal
dynamics of epileptic activity, while high spatial resolution is
essential to determine the seizure focus for subsequent possible surgical resection. By exploiting complementary information from EEG and fMRI, unprecedented spatiotemporal
accuracy in neuroimaging can be achieved [7]-[9]. Similar
examples include, but are not limited to, data fusion studies of
fMRI and MEG [10], fMRI and PET, EEG and MEG [11], and
fMRI and genetic data [12].
The original JBSS methods were likely canonical correlation analysis (CCA) [60] and partial least squares (PLS) [21].
Both methods utilize second-order statistics (SOS), with CCA
emphasizing the role of correlations among data sets and
PLS examining covariance information. Since, in general,
real neurophysiological data do not strictly follow multivariate
Gaussian distributions (that would have higher order statistical
moments zero), it is often insufficient to consider only up to
SOS (i.e., correlation and covariance) for obtaining a unique
JBSS model. Higher-order statistics (HOS) can therefore
be employed to enhance the accuracy of estimated sources.
Another limitation with traditional implementations of CCA
and PLS is that they accommodate only bisets (here we term
88
two data sets as a biset if they are from the same modality and
as bimodal if they are from two different modalities). However,
in many applications, more than two data sets are available,
and a better understanding can be achieved from jointly analyzing multiple data sets together [22].
Over the past decade, JBSS methods have been developed to solve two major categories of neurophysiological
data analysis challenges. The first challenge is the capacity
to simultaneously handle multiple data sets from the same
type of neurophysiological data (termed multiset)-for
example, determining group inferences by combining multiple fMRI data sets from several subjects [22], [26]. The
second challenge is the ability to jointly model multiple
data sets from distinct types of multimodal neurophysiological data-for example, corticomuscular coupling analysis by integrating EEG, electromyography (EMG), and
kinematic (KIN) data [27], [36]. In this article, we provide
a taxonomy of the representative JBSS methods characterized by two features: 1) whether or not the method utilizes
SOS or HOS; and 2) whether the method is designed for
biset/bimodal or multiset/multimodal data. We comprehensively describe these JBSS methods in a comparative manner and offer illustrative numerical simulations. We then
IEEE Signal Processing Magazine
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May 2016
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Table of Contents for the Digital Edition of Signal Processing - May 2016
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