22
Brain Imaging Applications
With the advent of new anatomical and functional brain imaging methods, it is now
possible to collect vast amounts of data from the living human brain. It has thus
become very important to extract the essential features from the data to allow an
easier representation or interpretation of their properties. This is a very promising
area of application for independent component analysis (ICA). Not only is this an
area of rapid growth and great importance; some kinds of brain imaging data also
seem to be quite well described by the ICA model. This is especially the case
with electroencephalograms (EEG) and magnetoencephalograms (MEG), which are
recordings of electric and magnetic fields of signals emerging from neural currents
within the brain. In this chapter, we review some of these brain imaging applications,
concentrating on EEG and MEG.
22.1 ELECTRO- AND MAGNETOENCEPHALOGRAPHY
22.1.1 Classes of brain imaging techniques
Several anatomical and functional imaging methods have been developed to study the
living human brain noninvasively, that is, without any surgical procedures. One class
of methods gives anatomical (structural) images of the brain with a high spatial res-
olution, and include computerized X-ray tomography (CT) and magnetic resonance
imaging (MRI). Another class of methods gives functional information on which
parts of the brain are activated at a given time. Such brain imaging methods can help
in answering the question: What parts of the brain are needed for a given task?
407
Independent Component Analysis. Aapo Hyv
¨
arinen, Juha Karhunen, Erkki Oja
Copyright

2001 John Wiley & Sons, Inc.
ISBNs: 0-471-40540-X (Hardback); 0-471-22131-7 (Electronic)
408

at any given time tend to be clustered in the brain. Thus, the total electric current
produced in such a cluster may be large enough to be detected. This can be done by
measuring the potential distribution on the scalp by placing electrodes on it, which
is the method used in EEG. A more sophisticated method is to measure the magnetic
fields associated with the current, as is done in MEG.
EEG and MEG
The total electric current in an activated region is often modeled
as a dipole. It can be assumed that in many situations, the electric activity of the brain
at any given point of time can be modeled by only a very small number of dipoles.
These dipoles produce an electric potential as well as a magnetic field distribution
that can be measured outside the head. The magnetic field is more local, as it does
not suffer from the smearing caused by the different electric conductivities of the
several layers between the brain and the measuring devices seen in EEG. This is one
of the main advantages of MEG, as it leads to a much higher spatial resolution.
EEG is used extensively for monitoring the electrical activity within the human
brain, both for research and clinical purposes. It is in fact one of the most widespread
brain mapping techniques to date. EEG is used both for the measurement of sponta-
neous activity and for the study of evoked potentials. Evoked potentials are activity
triggered by a particular stimulus that may be, for example, auditory or somatosen-
sory. Typical clinical EEG systems use around 20 electrodes, evenly distributed
ELECTRO- AND MAGNETOENCEPHALOGRAPHY
409
over the head. State-of-the-art EEGs may consist of a couple hundred sensors. The
signal-to noise ratio is typically quite low: the background potential distribution is
of the order of 100 microvolts, whereas the evoked potentials may be two orders of
magnitude weaker.
MEG measurements give basically very similar information to EEG, but with a
higher spatial resolution. MEG is mainly used for basic cognitive brain research.
To measure the weak magnetic fields of the brain, superconducting quantum inter-
ference devices (SQUIDs) are needed. The measurements are carried out inside a

considering the underlying source signals as stochastic processes, the requirement
of stationarity is in theory necessary to guarantee the existence of a representative
distribution of the ICs. Yet, in the implementation of batch ICA algorithms, the data
are considered as random variables, and their distributions are estimated from the
whole data set. Thus, the nonstationarity of the signals is not really a violation of the
assumptions of the model. On the other hand, the stationarity of the mixing matrix A
is crucial. Fortunately, this assumption agrees with widely accepted neuronal source
models [394, 309].
410
BRAIN IMAGING APPLICATIONS
22.2 ARTIFACT IDENTIFICATION FROM EEG AND MEG
As a first application of ICA on EEG and MEG signals, we consider separation of
artifacts. Artifacts mean signals not generated by brain activity, but by some external
disturbances, such as muscle activity. A typical example is ocular artifacts, generated
by eye muscle activity.
A review on artifact identification and removal,with special emphasis on the ocular
ones, can be found in [56, 445]. The simplest, and most widely used method consists
of discarding the portions of the recordings containing attributes (e.g., amplitude
peak, frequency contents, variance and slope) that are typical of artifacts and exceed
a determined threshold. This may lead to significant loss of data, and to complete
inability of studying interesting brain activity occuring near or during strong eye
activity, such as in visual tracking experiments.
Other methods include the subtraction of a regression portion of one or more
additional inputs (e.g., from electrooculograms, electrocardiograms, or electromyo-
grams) from the measured signals. This technique is more likely to be used in EEG
recordings, but may, in some situations, be applied to MEG. It should be noted that
this technique may lead to the insertion of undesirable new artifacts into the brain
recordings [221]. Further methods include the signal-space projection [190], and
subtracting the contributions of modeled dipoles accounting for the artifact [45]. In
both of these latter methods we need either a good model of the artifactual source

3
3
4
4
5
5
6
6
MEG
saccades blinking biting
Fig. 22.1
A subset of 12 spontaneous MEG signals from the frontal, temporal and occipital
areas. The data contains several types of artifacts, including ocular and muscle activity, the
cardiac cycle, and environmental magnetic disturbances. (Adapted from [446].)
sensor. ICs 6 and 7 may be breathing artifacts, or alternatively artificial bumps
caused by overlearning (Section 13.2.2). For each component the left, back and right
views of the field patterns are shown. These field patterns can be computed from the
columns of the mixing matrix.
22.3 ANALYSIS OF EVOKED MAGNETIC FIELDS
Evoked magnetic fields, i.e., the magnetic fields triggered by an external stimulus, are
one of the fundamental research methods in cognitive brain research. State-of-the-art
approaches for processing magnetic evoked fields are often based on a careful expert
scrutiny of the complete data, which can be either in raw format or averaged over
several responses to repeating stimuli. At each time instance, one or several neural
sources are modeled, often as dipoles, so as to produce as good a fit to the data as
possible [238]. The choice of the time instances where this fitting should be made,
as well as the type of source models employed, are therefore crucial. Using ICA, we
can again obtain a blind decomposition without imposing any a priori structure on
the measurements.
The application of ICA in event related studies was first introduced in the blind