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: salustri@iess.rm.cnr.it (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.

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