Language-related brain activity revealed by independent
component analysis
Carlo Salustria,*, Eugene Kronbergb
aInstitute of Cognitive Science and Technology (CNR) – Unita MEG, Ospedale San Giovanni Calibita Fatebenefratelli – Isola Tiberina, 00186 Rome, ItalybCenter for Neuromagnetism, New York University School of Medicine, New York, NY, USA
Accepted 17 September 2003
Abstract
Objective: When an individual engages in a cognitive task, a multitude of diverse processes are activated in his/her brain and it is
reasonable to assume that multiple brain sources are simultaneously active at any one time. Magnetoencephalographic (MEG) data recorded
in such circumstances provide a picture of spatial distribution and time course of the sum of the magnetic fields generated by all these
sources. Thus, the experimenter faces the challenge of separating the multiple contributions to the total recorded signal before attempting a
localization of their sources and studying their functional roles.
Methods: We describe in this paper how independent component analysis of MEG data collected in a word/pseudo-word reading
experiment elegantly solves this problem.
Results: Using a few statistical assumptions, independent component analysis resolved simultaneously active brain sources in the right-
frontal, left-parietal and left-frontal areas, all showing well defined dipolar field distributions.
Discussion: We describe the characteristics of these contributions and discuss the language-related functional roles that appear to be
associated to some of the independent sources. We report in particular on one source, localized near Broca’s area, which showed to be
affected by reading words but not pseudo-words.
q 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Keywords: Magnetoencephalography; Independent component analysis; Language
1. Introduction
Understanding the mechanisms underlying the pro-
duction and the use of language represents a target of
utmost importance both per se and for the wide number of
pathologies in which language is involved. In this context
localization of language-related areas and identification of
their functional roles are obviously major targets of ongoing
research. Despite the fact that this issue has been and is
widely studied by means of different techniques as
electroencephalography (EEG), magnetoencephalography
(MEG), functional magnetic resonance imaging, positron
emission tomography (PET) and more (for a review, see
Pulvermuller, 1999, 2001 and references therein), no unified
picture has been achieved. This is not surprising since it is
reasonable to think that a multitude of diverse cognitive
processes are likely to be activated in the brain when
a human engages in any activity requiring language,
meaning that at least several, if not many brain sources
must be assumed to be simultaneously active at any one
time. Consequently, techniques like EEG and MEG face the
challenge of separating the multiple contributions to the
total recorded signal, as a first basic step towards the more
ambitious target of localizing their sources and studying
their functional roles. This task is further complicated by the
presence in the recorded signal of artifacts of different
origins, as cardiac activity, head or body movements and
muscle contractions: in experiments involving reading, for
example, brain MEG signals are often dominated by
artifacts due to eye blinking which, lasting sometimes
hundreds of milliseconds, can easily bury most of the
cognitive response. A number of robust mathematical
techniques, as for example orthogonal signal projection
(Samonas et al., 1997), adaptive noise cancelling (Widrow
et al., 1975), singular value decomposition (Kanjilal et al.,
1997), are commonly used for artifact rejection and for
extracting essential features from the data but they do not
1388-2457/$30.00 q 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
doi:10.1016/j.clinph.2003.09.015
Clinical Neurophysiology 115 (2004) 385–395
www.elsevier.com/locate/clinph
* Corresponding author. Tel.: þ39-6-6837-382.
E-mail address: [email protected] (C. Salustri).
provide detailed spatio-temporal information specific to the
multiple brain sources that simultaneously contribute to the
total signal.
In this paper we describe the application of a relatively
novel technique, independent component analysis (ICA), to
brain data collected in a word/pseudo-word reading
experiment by means of a whole-head MEG system. Our
aim is to show how this technique is capable of resolving
and localizing multiple simultaneously active brain sources,
providing the full time course of their individual activations.
ICA finds linear projections of the data that maximize their
statistical independence (Hyvaerinen and Oja, 2000; Brown
et al., 2001; Tzyy-Ping et al., 2001) and our results suggest
the possibility of interpreting these projections as a direct
description of the underlying neuronal sources as well as of
the overlying artifacts. The clearcut separation of multiple
sources provides insight into the functional roles that far-
apart brain areas appear to have in processing language.
2. Methods
The problem of separating simultaneously active sources
belongs to the family of blind source separation, the term
‘blind’ indicating that signals which cannot be measured
directly and about which we have little or no information are
recovered from measurements of mixtures of them. It is also
known as the ‘cocktail party’ problem (Brown et al., 2001)
since its nature is well described by the analogy of a number
of people talking in a room while a number of microphones
record the room sound. The sound traces recorded by each
microphone represent a weighted mixture of all the people’s
voices, the weights depending on the microphone location
in the room, and the problem is to extract each single
person’s voice from the total sound the microphones have
recorded. Much in the same way, what is available in an
MEG experiment is the head surface distribution of the total
magnetic field as recorded by N sensors distributed over the
subjects head. If we assume that these N sensor recordings
x1(t)……xN(t) are each a weighted mixtures of N statisti-
cally independent sources s1(t)……sN(t), our recordings can
be written as
x1ðtÞ ¼ a11s1ðtÞ þ a12s2ðtÞ þ · · ·· · · þ a1NsNðtÞ
x2ðtÞ ¼ a21s1ðtÞ þ a22s2ðtÞ þ · · ·· · · þ a2NsNðtÞ
·
·
xNðtÞ ¼ aN1s1ðtÞ þ aN2s2ðtÞ þ · · ·· · · þ aNNsNðtÞ
or in matrix notation
xðtÞ ¼ AsðtÞ
The matrix A, which is unknown to us, is called the ‘mixing’
matrix since it mixes up the independent sources s which, in
turn, are called ‘latent’ variables, since they cannot be
measured directly. ICA ignores any time structure of the
data and estimates the matrix A that best delivers statistical
independence of the sources s. From the mixing matrix A
we directly extract information on the source spatial
locations and, by computing its inverse B ¼ A21, we obtain
the time course of the sources’ activations:
sðtÞ ¼ BxðtÞ
The application of ICA to MEG (as well as EEG) data is
based on 3 fundamental assumptions: (1) the existence of
signal sources statistically independent from each other; (2)
that the mixing of their contributions is instantaneous and
linear; and (3) that both the source signals and the mixing
process are spatially stationary.
Independence, which implies uncorrelatedness, may
seem difficult to assume with simultaneously active brain
sources: nevertheless it must be emphasized that the concept
of independence does not refer to the physiology of the
neural sources but to the statistics of their amplitude
distributions. Moreover, some ICA algorithms search for
linear transformation of the data that maximize their non-
Gaussianity and both evoked fields and artifacts have a non-
Gaussian distribution (Vigario and Oja, 2000; Hyvaerinen
et al., 2001).
The mixing process can also be reasonably assumed
instantaneous, since MEG activity is well below 1 kHz and
the quasi-static approximation of the Maxwell equations
holds (Hamalainen et al., 1993): ICA then considers each
time point of the MEG traces separately.
Brain sources are normally described with current
dipoles and within this model, the concept of spatial
stationarity of the source signals and of their mixing process
corresponds to the existence of sources with fixed locations
and orientations but time varying amplitudes (Scherg and
von Cramon, 1995; Mosher et al., 1992).
A detailed discussion of ICA assumptions and require-
ments can be found in almost any publication regarding the
mathematics of ICA.
2.1. Experiment
Verbal stimuli consisted of a visually delivered random
series of words and pseudo-words. The word set was made
of 168 function words (like ‘from’, ‘after’, ‘beyond’, etc.)
and 168 content words (like ‘chair’, ‘mountain’, ‘car’, etc.),
jointly matched for frequency (log frequency from 0.47 to
3.1) and length (3–10 characters) (Kucera and Francis,
1967). The pseudo-word set was made of 168 function and
168 content non-words constructed by changing one to two
letters in real words, maintaining them orthographically
legal, pronounceable, and 3–10 characters in length. The
mean diagram frequency of both words and pseudo-words
was calculated by summing the frequencies of each
consecutive letter pair in the string and dividing by the
number of pairs. Pseudo-words and content words resulted
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395386
quite similar on this measure, indicating that the pseudo-
words were word-like.
Both words and pseudo-words were produced as letter
strings by a standard personal computer and delivered one at
the time to the subject lying in a magnetically shielded
room, via a fiber optic system (Silent Vision, AVOTEC)
engineered to avoid electromagnetic noise.
Each presentation epoch was organized in the following
way (see Fig. 1): a 200 ms long attention mark appeared on
the screen 1 sec prior the appearance of the verbal stimulus:
then, the verbal stimulus appeared on the screen for 150 ms
and was followed 1200 ms later by a 150 ms long question
mark; 2 s of blank screen separated one epoch from the next
one. The subject’s task was simply to judge whether the
presented stimulus was a word or a pseudo-word, wait for
the appearance of the question mark and then press one of
two buttons accordingly. It is important to notice that the
subject’s task was not overtly to discriminate between
function and content words. The delayed question mark was
introduced in order to well separate cognitive processes and
motor activation.
Stimuli were presented in blocks, each block containing
56 stimuli (28 words and 28 pseudo-words): after each block
subjects were allowed to rest their eyes for a few minutes
without moving their heads with respect to the recording
apparatus. The number of stimuli delivered changed for
each subject according to the level of fatigue he/she would
report: the minimum was 6 blocks (336 epochs), the
maximum was 13 blocks (728 epochs). Each recording
session lasted then between 45 and 90 min, including resting
times, depending on subject endurance.
Ten subjects participated in the experiment after signing
an informed consent. Brain magnetic fields were recorded
with a 148-channel MEG system (Magnes 2500WH, 4D
Neuroimaging) at 678 Hz sampling rate.
2.2. Data analysis and results
First of all we performed a spectral analysis of the data.
MEG recordings have in general a rather large dynamic
range and in principle one can expect the simultaneous
sources to have rather diverse frequency contents; on the
other hand, one cannot rule out the possibility that they may
also show frequency peaks very close to or overlapping each
other. Standard FFT techniques did not seem to us the best
choice for spectral analysis since they are known to suffer
from ‘leakage’, i.e. the transfer of information originating
from one frequency into another frequency (this phenom-
enon is also commonly called ‘bias’). In general leakage is a
concern for processes whose spectra cover a large dynamic
range and most importantly when data have a limited
temporal span: in fact, it can be shown from statistical
theory that the standard periodogram is approximately an
unbiased estimator of the true spectral density function only
when sample sizes are very large. For finite sample sizes,
this approximation can be quite poor due to the above
mentioned leakage. Moreover, the periodogram variance
does not decrease with increasing sample size. For these
reasons, we decided to use a more modern technique called
multi-taper (Thomson, 1982; Percival and Walden, 1993;
Mitra and Pesaran, 1999), which resolves the problems of
bias and variance by averaging over a set of orthogonal basis
functions, the so-called Slepian sequences (Slepian and
Pollak, 1961). These sequences wk(t) of orthogonal func-
tions, defined in the time interval t ¼ 1, 2, …, T, are
parameterized by a bandwidth parameter W, such that there
are K ¼ 2WT 2 1 basis functions whose spectra are
confined to a frequency band [ f 2 W, f þ W ] around the
frequency of interest f. For a given data sequence x(t), the
tapered Fourier transform gk( f) ¼P
t x(t) wk(t) exp(22pft)
is computed and a direct estimate of the spectrum is given
by SMT( f) < (1/K)P
k j gk( f) j 2. The average across tapers
reduces the variance by a factor of 1/K and the effects of
leakage are drastically reduced. The multi-taper technique
has been recently applied to the analysis of neurobiological
time series (Mitra and Pesaran, 1999, Llinas et al., 1999).
Each single 3.5 s long epoch of the recorded MEG
(Fig. 1) was passed through a multi-taper algorithm with a
bandwidth parameter W ¼ 0.57 Hz, leading to K ¼ 3. The
extracted single-epoch frequency spectra were then aver-
aged separately for each subject. Fig. 2 shows the resulting
Fig. 2. Averaged power spectral density for 3 representative subjects.
Fig. 1. Timing of the stimulus presentation.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395 387
power spectral density (PSD) for 3 subjects both in the case
of words and pseudo-words.
Besides an expected strong presence of low frequencies,
the spectra showed two rather sharp peaks slightly before
and after 10 Hz: the first peak varied across subjects from as
low as 6 Hz to about 9 Hz, whereas the second peak varied
only between 11 and 12.5 Hz. All subjects’ spectra also
showed a wide PSD increase centered between 20 and 25 Hz
but its relative amplitude and frequency spread varied across
subjects. Both words and pseudo-words produced the same
frequency peaks although the PDS appeared slightly (never
exceeded 10%) but consistently lower for pseudo-words
than for words. It is worth noticing that this double peak
pattern was invisible in the majority of the subjects when
data were analyzed with standard FFT instead of a multi-
taper algorithm.
The recorded brain magnetic fields were dominated by
high-amplitude waves that lasted some hundreds of
milliseconds after the stimuli and showed highly bi-
hemispheric patterns. The averaged brain response of
subject 1 of Fig. 2, filtered between 1 and 55 Hz, is shown
in Fig. 3 (lower portion) together with the channel
distribution of the averaged magnetic fields at 4 representa-
tive time points (upper portion).
The channel distribution pattern was shared by all
subjects and would well represent two dipolar sources
located on both eyes’ sides and oriented in the same
direction, suggesting a strong artifact caused by eye
blinking. Applying a high-pass filter did not rid the data
of these bi-hemispheric contributions and no reliable dipolar
patterns could be identified in the time span of hundreds of
milliseconds after the stimulus with the exception of
visual evoked fields in the occipital areas. Data sug-
gested a scenario in which the cognitive response to the
word/pseudo-word presentation, if it existed, was buried
under high-amplitude bi-hemispherical waves generated by
a variety of non-language related sources.
We separated the recorded MEG data in two groups
according to whether the corresponding stimulus was a
word or a pseudo-word, filtered them between 1 and 55 Hz
and separately passed them through an ICA algorithm
(available for download at Dr Scott Makeig’s web site
http://www.sccn.ucsd.edu/~scott/ica.html). The ICA algor-
ithm assumed the existence of as many components as the
number of sensors, i.e. extracted 148 independent com-
ponents and ordered them according to the variance their
projections accounted for, the first component accounting
for the highest variance.
The spatio-temporal characteristics of the highest-rank-
ing components extracted by ICA led us to interpret them as
related to eye blinks and heartbeat. The left column of Fig. 4
shows the head distribution in one subject of a component
that we interpreted as associated to eye blinks: this
component was extracted by ICA in all subjects, it showed
no difference between word and pseudo-word stimulation
and its distribution strikingly resembles the one obtained by
averaging the data shown in Fig. 3.
This type of artifact can be due either to eye muscle
contraction or to a change of the retinal dipoles’ directions
consequent to the ocular movements, or both: in the
assumption that the activation of an independent component
represents the time course of the contribution of
Fig. 3. Lower part: averaged magnetic fields recorded from subject 1 of Fig. 2. Data were filtered between 1 and 55 Hz. Upper part: channel distribution at 4
representative time points.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395388
the underlying neuronal source to the total variance, we
defined time epochs corresponding exactly to the epochs in
the original data and averaged the activations across epochs:
the right side of Fig. 4 shows the averaged activation time
course of the component whose head distribution is shown
on the left side.
ICA extracted at least one heart-related component in
all subjects and more than one in some subjects: this is
due to the fact that ICA assumes sources fixed not only
in space but also in orientation: consequently dipolar
activations like the heart QRS and T cycle are ‘seen’ by
ICA as two or more statistically independent activations.
Fig. 5 shows 3 randomly chosen epochs of activation of
the second and seventh components extracted from one
subject.
None of the remaining high-ranking components showed
a repetitive pattern of activation visible by simple eye
inspection of the single epochs: they rather appeared as
series of spindles. Nevertheless some of them showed
outstanding dipolar distributions located near areas of the
brain that are generally associated with language processing
and an interesting temporal evolution strongly affected by
the stimulus presentation. We used both simple visual
inspection of the head distributions and examination of their
frequency contents to relate components to each other in the
two word classes. Although many of the components
extracted by ICA from our data showed an activation time
course clearly related to the stimulus presentation, we
devote the rest of this paper to describing those independent
components that accounted for a high percentage of the
variance and whose projections showed a well defined
dipolar structure. In general we will proceed describing
components in descending order of projected variance.
Three areas in particular showed strong and well
localized dipolar distribution of high-rank independent
components: right-frontal, left-parietal and left-frontal;
one component with multi-dipolar distribution was also
extracted from all subjects.
2.2.1. Right-frontal activation
A component was extracted from 100% of the subjects
showing a marked dipolar distribution in the right frontal
area (Fig. 6a for one representative subject). Using again
multi-taper techniques we calculated the frequency spec-
trum within each epoch of the activation time course of this
component and averaged them across epochs for each
subject. Fig. 6b shows the grand-average of these spectra
across subjects. Since the spectrum is marked by a sharp
12 Hz peak we investigated the relationship of the
component’s time-course with the stimuli presentation
within the 9–15 Hz narrow band. When averaging epochs
one must remember that a link to the stimulus can happen in
different ways: as an amplitude modulation or as a phase
reset or both. A standard average of the activation time-
course does not bring complete information on amplitude
variations since phase incoherence can cause the traces to
cancel each other and appear as an amplitude drop. For the
same reason, time-limited phase coherence linked to the
stimulus could appear as an amplitude increase simply
because of the drop in the rest of the trace. This is
particularly evident when data are filtered in a narrow band.
For this reason we computed the Hilbert transform of each
single epoch and averaged the absolute value of the analytic
signal across epochs: the latter results in an all-positive
envelope of the trace without any loss of phase information.
Consequently, Hilbert transforming represents the ideal
method to measure amplitude variations not due to phase
coherence. Fig. 6c shows the grand-averaged absolute value
of the analytic signal, normalized for variations of power
across subjects.
Fig. 4. Head distribution (left part) and activation time course (right part) of the highest-rank component from subject 1 of Fig. 2.
Fig. 5. Three randomly chosen epochs of the activation time course of the
second and seventh components extracted from one subject.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395 389
2.2.2. Left-parietal activation
A total of 80% of the subjects showed also a dipolar
component in the left central-posterior areas (Fig. 7a for the
head distribution of this component in one representative
subject). Proceeding as for the previous component, we
computed and grand-averaged the power spectrum, shown
in Fig. 7b, which features characteristics remarkably similar
to the right-frontal component, marked by a prominent
frequency peak around 12–13 Hz. The component’s time
course was filtered then between 9 and 15 Hz before
computing the Hilbert transform. Fig. 7c shows the grand-
average of the absolute value of its analytic signal.
2.2.3. Left-frontal activation
Most interesting, though puzzling, is the rather frontal
component that ICA extracted in the left hemisphere since
this is the one that delivered a substantial difference between
words and pseudo-words: in fact ICA extracted this
component in all subjects from the runs where the verbal
stimuli were made of words, whereas it did not uncover any
dipolar left-frontal structure in the runs where the stimuli
were made of pseudo-words. Fig. 8a shows an example of
the head distribution of this component. Contrary to the
previous two, this component showed frequency patterns
rather diverse among subjects, including twin peaks
Fig. 6. (a) Head distribution of the independent component extracted in the right frontal area shown for one representative subject. (b) Component’s frequency
spectrum (power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395390
between 8 and 17 Hz, resulting in a power spectral density
grand-average lacking outstanding features (Fig. 8b).
Nevertheless, in the 7–15 Hz frequency range the Hilbert
grand-average (words only) still shows a modest but evident
modulation after the stimulus arrival (Fig. 8c).
2.2.4. Multi-dipolar activation
All subjects featured a multi-dipolar component that
appeared activated at the time of the presentation of both
stimuli (attention mark and verbal stimulus, no difference
between words and pseudo-words): this component showed
an activation rather distributed over the head with higher
intensity in the occipital area (Fig. 9a). Although the timing
of this component was extremely consistent across subjects,
the frequency spectra showed much variability, causing
again a lack of outstanding peaks in the grand average:
besides the ubiquitous strong low frequency, subjects
showed frequency peaks, and twin peaks, from 7.5 to 13 Hz.
3. Discussion
Drastically reducing leakage effects in extracting the
frequency spectra by using multi-taper analysis allowed us
to resolved two peaks at frequencies slightly lower and
higher than 10 Hz, respectively, observed in all subjects.
Fig. 7. (a) Head distribution of the component extracted in the left parietal area shown for one representative subject. (b) Component’s frequency spectrum
(power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395 391
A division in ‘lower and upper alpha bands’ was established
back in the seventies by Matousek and Petersen (1973) and
by Hermann et al. (1978). Lopes da Silva (1993) reported it
in a study of 243 subjects and our frequency peaks agree
very well with the results of that study. We observed this
two-frequency configuration also in a separate measurement
of spontaneous activity (unpublished) performed in our
laboratory. Although the existence of multiple and overlap-
ping ‘10 Hz’ activities is by now well established (Andrew
and Pfurtscheller, 1997; Lutzenberger, 1997; Makeig et al.,
2002, see Hari et al., 1997 for an MEG study), it remains
strangely uncommon to see double-picked alpha frequency
spectra reported in the literature.
However, we believe that the major outcome of this work
is the evidence that ICA is effective in resolving multiple
brain sources that simultaneously contribute to the total
recorded signal: this result cannot be achieved by standard
averaging techniques. Moreover, the assumption of statisti-
cal independence reduces (in theory eliminates) the need of
an accurate choice of the ranges of data filtering: a wide
spectrum of frequencies can be safely included in the
analysis allowing each source to be characterized by its full
frequency content.
The spatio-temporal features of the independent com-
ponents extracted and presented in this paper strongly
suggest that they may directly represent the characteristics
Fig. 8. (a) Head distribution of the component extracted in the left frontal area shown for one representative subject. (b) Component’s frequency spectrum
(power spectrum density grand-averaged across subjects). (c) Grand-averaged absolute value of the analytic signal in the 9–15 Hz narrow band.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395392
of underlying neuronal sources, providing meaningful
evidence of cognitive processes.
Fig. 6c shows two successive decreases of the indepen-
dent component’s amplitude in the right-frontal area: a
descend starting after the appearance of the attention mark
and a sharp drop after the presentation of the verbal
stimulus; a recovery is visible after 600–800 ms. The rather
moderate but monotone descend between the attention mark
and the stimulus arrival and the fact that the modulation in
the entire time course appears the same for both
words and pseudo-words seem to suggest that this frontal
source is sensitive to the expectation of the event of
importance, before undergoing a strong suppression after
the event arrival.
Most interesting is of course the result of the left-
frontal independent component that was affected by words
but not by pseudo-words and is localized in the vicinity of
areas 44 and 45 (Broca’s region). Imaging studies have
already shown the activation of inferior frontal areas
(together with superior temporal) when words are
perceived (Fiez et al., 1996; Mazoyer et al., 1993; Zatorre
et al., 1992). Over the inferior frontal areas of the left
hemisphere, also previous electric and magnetic studies
(Law et al., 1993; Pulvermuller, 1996) have shown that
words evoke stronger responses than matching pseudo-
words. This difference is reported to be significant only in
the 20–35 Hz frequency range: although this is in
general agreement with the evoked responses recorded
Fig. 9. (a) Head distribution of the multi-dipolar component extended all over the head shown for two representative subjects. (b) Grand-averaged absolute
value of the analytic signal in the 7–15 Hz narrow band. (c) Component’s frequency spectrum (power spectrum density grand-averaged across subjects).
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395 393
by us (see the frequency range 20–30 Hz in Fig. 2), the
left-frontal independent component extracted by our ICA
shows its well-defined dipolar structure in the 7–15 Hz
range: this suggests that this component is linked to the
mu-rhythm suppression caused by the ongoing word
processing. From a Hebbian viewpoint (Hebb, 1949),
one could reasonably say that the word presentation
ignites the activation of a relatively vast assembly of cells
and the size and connectivity of this assembly causes the
underlying mu-rhythm to be suppressed for the time of
this activation. In this view, the presentation of pseudo-
words does not trigger an equally widespread mu-rhythm
suppression simply because it does not ignite the
activation of equally well-defined cell assemblies. If this
interpretation is correct, the sensitivity of this component
to the nature of the incoming stimulus and its localization
near areas 44 and 45 seem to contradict the old
localizationist view that describes those areas as hosting
only the motor representation of verbal processes (Gesch-
wind, 1970): in fact in our experiment there was no
induced motor activity during the mental processing.
In the same way, also the view that assigns only the
acustic representation of a verbal process to Wernicke’s
area (Geschwind, 1970) would be contradicted: in fact
stimuli in our experiment were presented only visually
and Fig. 7c shows that they still trigger a sharp drop in the
amplitude of the left-parietal independent component
which is localized in the vicinity of Wernicke’s area.
The fact that this component is not affected by the
appearance of the attention mark suggests that the
amplitude suppression is attributable to the actual
cognitive process alone. Considering the characteristics
and consistency of its frequency spectrum and that there
seems to be no substantial difference between words and
pseudo-words, the sinking of this component seems to
represent well a suppression of the mu-rhythm caused by
the processing of the verbal input: this also extends
significantly the notion of a mu-rhythm modulated by
somatosensory activity to including a dependence on
cognitive processes.
It is worth noticing at this point that, although we have
described here only high-rank components with a well-
defined dipolar distribution, lower-rank components also
showed strong dependence on the stimulus arrival and
frequency characteristics that we believe contribute to the
above described double-peaked spectra. We are in the
process of analyzing these components.
To conclude, we believe that our results suggest a
scenario in which low-frequency oscillations (7–14 Hz) are
constantly active in distributed regions of the brain and are
locally suppressed by external inputs within stimulus-
specific areas: we speculate that during this low-frequency
suppression, higher-frequency activations may occur, poss-
ibly on a more local scale. As a consequence of the smaller
neuronal population involved, these higher-frequencies do
not produce sufficient power to be effectively extracted by
current analysis methods. We are presently testing new data
pre-processing which may help extract higher frequency
sources.
Acknowledgements
The authors sincerely thank Dr Robert M. Chapman for
providing the sets of verbal stimuli, Dr Kevin Sauve’ for his
invaluable contribution in the data collection and Dr Scott
Mekaig and his co-workers for making their ICA algorithm
available to the public.
References
Andrew C, Pfurtscheller G. On the existence of different alpha band
rhythms in the hand area of man. Neurosci Lett 1997;222:103–6.
Brown GD, Yamada S, Sejnowski TJ. Independent component analysis at
the neural cocktail party. Trends Neurosci 2001;24(1):54–63.
Fiez JA, Raichle ME, Balota DA, Tallal P, Petersen SE. PET activation of
posterior temporal regions during auditory word presentation and verb
generation. Cereb Cortex 1996;6:7–10.
Geschwind N. The organization of language and the brain. Science 1970;
170:940–4.
Hamalainen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa OV.
Magnetoencephalography – theory, instrumentation and applications
to non-invasive studies of the working human brain. Rev Mod Phys
1993;65(2):413–97.
Hari R, Salmelin R, Makela JP, Salenius S, Helle M. Magnetoencephalo-
graphic cortical rhythms. Int J Psychophysiol 1997;26(1–3):51–62.
Hebb DO. The organization of behavior. A neurophysiological theory. New
York: Wiley & Sons; 1949.
Hermann WM, Fichte K, Kubicki S. Mathematische rationale fur die
klinischen EEG-frequenzbander. I. Faktorenanalyse mit EEG power-
spektralschatzungen zur definition von frequenzbandern. Electroenceph
Electromyogr 1978;9:146–54.
Hyvaerinen A, Oja E. Independent component analysis: algorithms and
applications. Neural Netw 2000;13:411–30.
Hyvaerinen A, Karhunen J, Oja E. Independent component analysis. New
York: John Wiley & Sons; 2001.
Kanjilal PP, Palit S, Saha G. Fetal ECG extraction from single-channel
maternal ECG using singular value decomposition. IEEE Trans Biomed
Eng 1997;44(1):51–9.
Kucera M, Francis WN. Computational analysis of present-day American
English. Providence, RI: Brown University Press; 1967.
Law SK, Rohrbaugh JW, Adams CM, Eckhardt MJ. Improving spatial and
temporal resolution in evoked EEG responses using surface Laplacians.
Electroenceph clin Neurophysiol 1993;88:309–22.
Llinas RR, Ribary U, Jeanmonod D, Kronberg E, Mitra PP. Thalamocor-
tical dysrhythmia: a neurological and neuro–psychiatric syndrome
characterized by magnetoencephalography. Proc Natl Acad Sci 1999;
96(26):15222–7.
Lopes da Silva FH. Dynamics of EEGs as signals of neuronal populations:
models and theoretical considerations. In: Niedermeyer E, Lopes da
Silva FH, editors. Electroencephalography: basic principles, clinical
applications and related fields, 3rd ed. Baltimore, MD: Williams and
Wilkins; 1993. p. 63–77.
Lutzenberger W. EEG alpha dynamics as viewed from EEG dimension
dynamics. Int J Psychophysiol 1997;26:273–7.
Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E,
Sejnowski TJ. Dynamic brain sources of visual evoked responses.
Science 2002;295(5555):690–4.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395394
Matousek M, Petersen I. Frequency analysis of the EEG in normal children
and adolescents. In: Kellaway P, Petersen I, editors. Automation of
clinical electroencephalography. New York: Raven Press; 1973.
p. 75–102.
Mazoyer BM, Tzourio N, Frak V, Syrota A, Murayama N, Levrier O,
Salamon G, Dehaene S, Cohen L, Mehler J. The cortical representation
of speech. J Cogn Neurosci 1993;5:467–79.
Mitra P, Pesaran B. Analysis of dynamic brain imaging data. Biophys J
1999;76:691–708.
Mosher J, Lewis P, Leahy R. Multiple modelling and localization
from spatiotemporal MEG data. IEEE Trans Biomed Eng 1992;39:
541–57.
Percival DB, Walden WT. Spectral analysis for physical applications:
multitaper and conventional univariate techniques. Cambridge, UK:
Cambridge University Press; 1993.
Pulvermuller F. Hebb’s concept of cell assemblies and the psychophysiol-
ogy of word processing. Psychophysiology 1996;33:317–33.
Pulvermuller F. Words in the brain’s language. Behav Brain Sci 1999;
22(2):253–79.
Pulvermuller F. Brain reflections of words and their meaning. Trends Cogn
Sci 2001;5(12):517–24.
Samonas M, Petrou M, Ioannides AA. Identification and elimination of
cardiac contribution in single-trial magnetoencephalographic signals.
IEEE Trans Biomed Eng 1997;44(5):386–92.
Scherg M, von Cramon D. Two bilateral sources of the late AEP as
identified by a spatiotemporal dipole model. Electroenceph clin
Neurophysiol 1995;62:32–44.
Slepian D, Pollak HO. Prolate spheroidal wavefunctions Fourier analysis
and uncertainty, I. Bell Syst Techn J 1961;40:43–63.
Thomson DJ. Spectrum estimation and harmonic analysis. Proc IEEE 1982;
70:1055–96.
Tzyy-Ping J, Makeig S, McKeown MJ, Bell AJ, Te-Wong L, Sejnowski TJ.
Imaging brain dynamics using independent component analysis. Proc
IEEE 2001;89(7):1107–22.
Vigario R, Oja E. Independence: a new criterion for the analysis of the
electromagnetic fields in the global brain? Neural Networks 2000;13:
891–907.
Widrow B, Glover Jr JR, McCool JM, Kaunitz J, Williams CS, Hearn RH,
Zeidler J, Dong Jr E, Goodlin R. Adaptive noise cancelling: principles
and applications. Proc IEEE 1975;63(12):1692–711.
Zatorre RJ, Evans AC, Meyer E, Gjedde A. Lateralization of phonetic and
pitch discrimination in speech processing. Science 1992;256:846–9.
C. Salustri, E. Kronberg / Clinical Neurophysiology 115 (2004) 385–395 395
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