Does Event Related Desynchronization reveal anticipatory attention in the somatosensory modality?

DOES EVENT RELATED DESYNCHRONIZATION
REVEAL ANTICIPATORY ATTENTION
IN THE SOMATOSENSORY MODALITY?
O. ROMIJN

Does Event Related Desynchronization reveal anticipatory attention in the somatosensory modality?

Does Event Related Desynchronization Reveal Anticipatory Attention
in the Somatosensory modality?

2


Table of contents
Abstract

5

Introduction

6

Anticipatory attention and motor preparation

6

What is attention?

7

A taxonomy of attention

8

The phenomenon of anticipatory attention
A neurophysiological model for anticipatory processes

11
12

The Reticular Nucleus

12

Rhythmic activity and the thalamocortical network

16

Synchronization and desynchronization

18

On the existence of different rhythms

19

Physiological measures in attention research

24

EEG
Preparatory processes: slow potentials and ERD

25
28

Motor preparation

28

Anticipatory attention

30

Aims of the present study

Methods

32

33

Subjects

33

Experimental design & Procedure

33

Experimental design

33

Procedure

35

Apparatus and KR stimuli

36

Apparatus

36

Stimuli

37

Electrophysiological recordings

37

EEG recordings

37

EOG recordings

38

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Data reduction and statistical analysis

38

Artifacts

38

ERD computation

39

Statistical analysis

40

Results

45

Behavioral data

45

Physiological data

45

Premovement data

45

Prestimulus data

48

Discussion

55

Conclusions

61

Recommendations

62

References

64

Acknowledgements

82

Appendix A

83

Appendix B

95

Appendix C

97

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5

Abstract

Attention that is directed at an upcoming stimulus is termed anticipatory attention. The extended
thalamocortical gating model (Brunia, 1999) addresses the processes underlying anticipatory
attention. According to this model, both the thalamic relay (TCR) nuclei and the reticular nucleus
(RN) are involved in the selection (i.e. gating) of the relevant sensory modality. The TCR nuclei
can fire in two modes. The tonic mode is associated with the transmission of afferent and
subcortical input to the cortex and leads to desynchronization of 10 Hz rhythmic activity in the
cortical projection area of the TCR nucleus. The burst mode is associated with a disruption in this
transmission and results in the occurrence of 10 Hz rhythmic activity at the cortical projection
area. This implies that event-related changes in 10 Hz activity in the scalp recorded EEG may
give insight into the firing mode of the TCR nuclei and thus into the process of anticipatory
attention. Event Related Desynchronization (ERD, Pfurtscheller & Aranibar, 1977) can quantify
such changes. The extended thalamocortical model states that anticipatory attention is manifest as
a prestimulus activation of the sensory cortex corresponding to the modality of the anticipated
stimulus. Anticipatory attention to somatosensory stimuli would therefore be manifest as a 10 Hz
ERD over the postcentral cortex, whereas anticipatory attention to visual stimuli would be
manifest as a 10 Hz ERD over the occipital cortex. To test this hypothesis 9 subjects performed a
time-estimation task. They received a Knowledge of Results (KR) stimulus 2 seconds after their
manual response. ERD was recorded in the 10 Hz and 20 Hz frequency bands. An occipital ERD
was present preceding visual KR stimuli, whereas no significant postcentral ERD was present
prior to somatosensory KR stimuli. Nonetheless, the statistical analyses indicated that these
differences between conditions were not significant. Therefore, these results do not support the
extended thalamocortical gating model. It can be hypothesized that the postcentral ERD
preceding somatosensory stimuli is masked by a postmovement Event Related Synchronization
(ERS).


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Introduction
Anticipatory attention and motor preparation
How convenient yet unchallenging life would be if man always knew what was going to happen
next, completely destitute of the element of surprise. This is not the case, however, and the
unforeseen allows us to gain new experiences throughout life. In general, knowing what is going
to take place and when this is about to occur does have certain advantages. Imagine an athlete
taking his starting position for the 100 m dash finals. “Now is the time for the months of
preparation to pay off”, he whispers, while his feet meet the starting blocks. One last check. Is
everything in place? A glance at his running shoes makes him feel somewhat uneasy. As he is
still in doubt whether he should tie the laces more firmly, the starting pistol is fired. The starting
shot takes him completely by surprise and when he raises his head he realizes that he must drag
himself to his inevitable defeat.
This is not a desirable situation for an athlete. How then can this athlete improve his
performance? Attention seems so be the answer. According to Laberge (1995) attention can
increase the accuracy (and speed) of perceptual judgments by selecting information flow on the
input side of cognitive processing, and increase the accuracy (and speed) of actions on the output
side of cognitive processing by selecting information flow in the organizing and planning of both
internal and external actions. Therefore, the athlete would benefit from paying attention to the
starting gun (input side) and from preparing for the action that must be initiated by its firing
(output side).
Preparing for events, that will take place in the near future and call for action, is often referred to
as anticipatory behavior. As the example of the athlete indicates, anticipatory behavior
encompasses at least two factors (see figure 1): anticipatory attention and motor preparation. By
enhancing perceptual processes anticipatory attention may result in a better performance: the
sooner the athlete becomes aware of the gunshot, the better he is able to commence running at the
right moment. For the actual running to take place in a coordinated and appropriate manner,
however, another process comes into play: the process of motor preparation. Motor preparation
centers round planning and organizing internal and external actions.


7

Anticipatory behavior


Figure 1.

Motor preparation

Two components of anticipatory behavior.

Although motor preparation will be touched upon, this paper focuses on anticipatory attention
related to visual and somatosensory events. For a more detailed description of the purpose of this
investigation, the reader is referred to the section “Aims of the present study”.
The ensuing section provides an introduction to the concept of attention followed by a section on
the classification of attentional processes in order to elucidate the work domain of this thesis. For
a more thorough understanding of the concept of attention, the interested reader is referred to
Appendix A, which provides a concise history of the concept of attention.

What is attention?
“Everyone knows what attention is. It is the taking possession by the mind in clear and vivid form
of one out of what seem several simultaneous objects or trains of thought ... It implies
withdrawal from some things in order to deal effectively with others…”(William James, 1890, p.
403-404).
Perhaps the opening sentence of this chapter should have been “Nobody knows what attention is”.
Being familiar with the manifestations of attention does not define attention itself. Describing
attention somewhat resembles explaining the meaning of the word love to an alien. Experiencing
love is one thing, putting your finger on it is another. The main difficulty with defining attention
is that attention is not a single concept, but a term for a wide variety of psychological phenomena
(e.g. Styles, 1994, p. 1).


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It seems that the goals of attention are less disputed than its definitions. Laberge (1995) states that
being able to pay attention has three major benefits for an individual: accuracy, speed and
maintenance of mental processing. Accuracy in making a perceptual judgment is ordinarily not a
problem for the individual when the object is the only item in the perceptual field. Difficulties
sharply increase when other objects are in the vicinity, because information arising from
distractors can confuse one’s judgment of the target object. By selecting information flow on both
the input side and the output side (behavior) of attention one can respectively increase accuracy in
perception and accuracy of (future) actions. Attention also increases the speed with which
perceptual judgments and the planning and performance of actions take place.
In other words, attention enables us to focus on certain relevant features of the environment or
inner-self and to attenuate or exclude other features. Since only relevant features have to be taken
into account selection plays a crucial role in attention processes. How selection plays a role in
attention has been the subject of discussion for several decades. Until now the mechanism is not
fully understood.
Unfortunately, attention research is characterized by a great amount of different points of view,
research paradigms and theories. This, combined with the inconsistent terminology –the
terminology used to describe the subject of attention varies from one researcher to another- turns
the field of research into a maze of seemingly disorganized facts. In order to outline the work
domain of the present investigation, it is useful to categorize similar points of view and theories.
In the next section a concise taxonomy of distinguishable categories of attention will be
described.

A taxonomy of attention
As early as 1890 James noted that “Attention may be either … passive, reflex, nonvoluntary,
effortless: or … active and voluntary” (James, 1890, p. 416). This distinction still holds and, in
essence, boils down to the issue of exogenous and endogenous processes, respectively. Passive
attention and its derivatives are thought to be elicited by the changing environment whereas
active attention is often referred to as being voluntary, thus stemming from within the individual
(e.g. Posner, 1980). Both passive and active attention can be subdivided into two subcategories,
as illustrated by figure 2.


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Attention

Passive

Active

Intensive

Selective

Intensive

Selective

Orienting response

Automatic
attention reaction

Divided attention

Focused attention

Anticipatory
attention

Figure 2.

A taxonomy of attention. See text for details. Adapted from Kok & Boelhouwer
(1997, p. 2)

Passive attention as well as active attention can be either intensive or selective in nature. The term
“intensive” in the context of attention refers to the amount of energy allocated to the stimulus or
task as a whole (e.g. Kahneman, 1973), whereas “selective” refers to man’s ability to focus
attention on a specific part of the environment. Four types of attention can thus be distinguished
(see figure 2): passive intensive attention, passive selective attention, active intensive attention
and active selective attention.
James (1890, p. 416-418) noted that attention can be drawn automatically to stimuli by virtue of
either their “immediate” characteristics, such as suddenness or high intensity, or their “derived”
characteristics, which are acquired through experience. Thus, for a non-immediate stimulus to
attract attention, the stimulus should somehow be of importance to an individual. One way of
attributing importance to a stimulus is through training (Schneider, 1985). Thus, these stimuli are
not intrinsic attention-attractors but have acquired this ability by persistent training. The reactions
to derived stimuli are termed automatic attention reactions. Since these attention reactions are
automatic and selective, they are passive (i.e. nonvoluntary) selective attention processes.
The other subcategory, in which attention is attracted by the physical properties of the stimuli, is
termed passive intensive attention. Aspects of passive intensive attention can be observed during
the phenomenon termed “the Orienting Reflex” (Sokolov, 1963). Sokolov (1963) describes an


10

increase in arousal after the presentation of an unexpected auditory stimulus or an auditory
stimulus of a high intensity. Divided attention can be classified as active intensive attention,
whereas active selective attention involves focused attention.
A description of the way active and passive attention enable an individual to locate an object in
space provides a nice example of how both subcategories differ. To go short, there are two main
ways of cuing an object’s location: an exogenous cue initiates orienting from the onset of a
stimulus away from the current alignment of attention and an endogenous cue induces higherorder processes of the subject to initiate orienting. While the top-down control of orienting
induced by an endogenous cue typically involves voluntary processing, the bottom-up control of
orienting initiated by an exogenous cue is typically similar to a reflex. In general, the time taken
to orient following an endogenous cue is more than the time taken to orient following an
exogenous cue. This exogenous cue can cause attention to switch because of either the cue’s
physical or derived characteristics.
In the experimental setting, eliciting active or passive processes requires different designs
(Verbaten, 1997). If one wants to elicit active attention, the experiment is such that the subject has
to respond in a way that can be evaluated in a qualifiable manner, for instance correct or
incorrect. The subject is well informed on the upcoming task and is able to change the outcome of
the task by means of his own behavioral output. Hence, the subject has a precise mental
representation of the stimulus and his own actions in memory. In passive attention tasks, the
subject is unaware of the task. As a matter of fact, the design does not even contain a task. In
contrast with “active attention experiments”, “passive attention experiments” are not evaluated in
a qualifiable manner, for a desired behavioral performance is simply lacking. The key research
goal here is determining how new, unfamiliar and unexpected stimuli are processed by the
nervous system.
On the basis of the provided taxonomy, one could infer that the current investigation into the
realm of anticipatory attention is related to active selective attention. Hence, the subject
voluntarily focuses on a specific part of his environment in order to improve perception and
thereby improve task performance.


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Note that the athlete, who was mentioned in the introduction, had to focus attention on the sound
of the starting gun in order to make a good start. This anticipatory process implicitly encompasses
a selective aspect for it is the starting gun that has to be marked and not the encouragements of
the crowd nor the cameras in the athlete’s proximity.

The phenomenon of anticipatory attention
Although this taxonomy may fit the majority of types and subtypes of attention, this does not
imply that the taxonomy is suitable for every type of attention. These attention processes may be
the result of an accumulation of several of the four subcategories.
Anticipatory attention differs from other types of attention in at least one way. In general,
anticipatory attention is measured on a different timescale. Selective attention, for example, can
operate at snapshot durations while preparatory attention may operate on a scale of seconds
(Laberge, 1995).
Moreover, anticipatory attention differs from the defined subcategories in the sense that it
requires an expectation: it occurs before some expected perception or action, whereas, for
instance, selective attention can occur after as well as before the onset of an event, whether this
event was expected or not (Laberge, 1995).
The benefits of anticipatory processes in the case of the athlete, i.e. an increase in both speed and
accuracy in processing perceptual stimuli and actions, are presumably achieved by pre-activating
certain brain structures (Brunia, 1999). Brunia (1999) points out that anticipation is a selective
process and that this selective process can operate in two ways: by inhibiting irrelevant structures
or by increasing the excitation of the relevant brain structures (see also Laberge, 1995).
In order to gain insight into the process of anticipatory attention, it is necessary to isolate this
process (see the section on experimental design). For instance, it is not clear whether the faster
response time after hearing the starting gun is due to anticipation of the stimulus, anticipation of
the response, or both. Laberge et al. (1967) conducted a series of experiments of which the results
suggest that response time can be reduced by anticipating the stimulus without varying
anticipation of the response. Furthermore, the results of a subsequent experiment (Laberge et


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al.,1969) indicate that response time can be reduced by anticipating the response without varying
anticipation of the stimulus. Thus, it seems that anticipation for a stimulus or a response can be
varied independently and that they can occur simultaneously.

A neurophysiological model for anticipatory processes
Since adapting to the environment seems crucial for surviving, man must keep notion of the
changing environment surrounding him by relying on his sense organs. The sensory sytems of the
three main (i.e. visual, auditory and somatosensory) modalities have a common denominator with
respect to the processing of sensory information. Before the afferent input stemming from the
sense organs reaches its corresponding primary projection area in the posterior cortex cerebri, the
stream passes the thalamus. Here, each of the sensory systems has a private first order thalamic
‘relay’ nucleus (e.g. Guillery et al., 1998).
Visual information is transmitted via the lateral geniculate body to the primary visual cortex (V1),
whereas auditory information is transmitted via the pars ventralis (e.g. Jones, 1985) of the medial
geniculate body to the temporal cortex (A1). For the somatosensensory system it is the
ventrobasal nucleus (e.g. Jones, 1985) that transmits information to the cortex (S1). Besides first
order nuclei, higher order (association) nuclei are also present in the thalamus. The pulvinar can
be seen as a higher order nucleus for the visual system (Laberge & Buchsbaum, 1990) although it
is far from exclusively involved in the processing of visual information: somatosensory and
auditory information is also transmitted to this nucleus (e.g. Brunia, 1999). Parts of the dorsal
division of the medial geniculate nucleus (MGm) can be regarded as a higher-order relay to
secondary auditory areas (e.g. Conley & Diamond, 1990). For the somatosensory system, the
medial division of the posterior group (POm) can be seen as a higher-order (S2) relay (e.g.
Crabtree, 1996) connecting to the second somatosensory area.

The Reticular Nucleus
All the thalamocortical relay (TCR) nuclei send afferents to the cortex. One distinct nucleus
however, does not. This specific nucleus, which is termed the reticular nucleus (RN), caps the
entire lateral aspect of the thalamus and lies like a shield between thalamus and cortex. All fibers


13

passing either way between thalamus and cortex must go through this nucleus (Guillery et al.,
1998). Therefore, this nucleus is believed to play a pivotal role in information processing.
Although Laberge (1995) pointed to the fact that there are still uncertainties regarding the exact
wiring of the RN, it has been demonstrated that the RN exerts a local inhibitory influence upon
the underlying TCR nuclei. (e.g. Schlag & Waszak, 1970; Steriade, 1990). This inhibitory
influence constitutes the core of Skinner and Yingling’s gating model of attention (Skinner &
Yingling, 1977; Yingling & Skinner, 1977). As they put it: “the possibility that the RN may
function as a topographically specific inhibitory feedback circuit makes it a prime candidate for
selective regulation of thalamocortical activities” (Skinner & Yingling, 1977). Their model is an
attempt to describe inter-modal selective attention and the way it is brought about in the central
nervous system. Skinner and Yingling’s model was based on a large number of experiments, in
which rhythmic brain activity, slow potential shifts and evoked potentials were studied under
different conditions in the cat. According to their theory, inhibition of what is irrelevant underlies
selective attention. In other words, the signal-to-noise ratio is ameliorated by suppressing the
noise. This inhibition is believed to be brought about by the RN. Skinner and Yingling (1977)
suggested that neurons in the RN are under a dual control from both the frontal cortex and the
reticular formation (RF). These two major sources of input exert different influences on the RN.
The input from the (pre)frontal cortex appears to be selectively aimed at specific sectors only,
whereas the input from the RF provides a more diffuse innervation.
The RN was long regarded as a diffusely organized nucleus, having global rather than localized
actions on thalamocortical pathways (e.g. Scheibel & Scheibel, 1966). However, Skinner and
Yingling (1977) demonstrated that activating a part of the RN overlying a certain TCR nucleus
results in an inhibition of that TCR nucleus. In 1985 Jones pointed out that the RN is divided into
several distinct sectors, each related to a particular group of thalamocortical pathways. Now,
evidence points to the notion that there is a topographically ordered representation of relevant
cortical areas and thalamic nuclei in each of the sectors (e.g. Crabtree et al.,1989, 1992) and that
the RN is indeed organized in a modality-specific manner (Guillery et al., 1998; Mitrofanis &
Guillery, 1993).
The input from the (pre)frontal cortex and the input from the RF differ in yet another aspect,
besides the aforementioned difference in innervation pattern (i.e. selective versus diffuse
innervation). Whereas the influence of the (pre)frontal cortex on the RN is excitatory, the


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influence of the RF is inhibitory in nature. Excitation of RN –thereby facilitating its inhibitory
function – by the (pre)frontal cortex causes a relative closing or blocking of a thalamocortical
channel. According to Skinner and Yingling (1977), this is what happens during periods of
selective attention. Irrelevant channels are inhibited whereas the relevant channel is not. This
process enables information in the relevant channel to pass to the cortex while information in the
irrelevant channel does not. The inhibitory influence of the RF on the RN causes a global
disinhibition of the underlying thalamic relay nuclei that results in a relative deblocking of all
channels. Skinner and Yingling (1977) hypothesized that the two main sources of input, i.e.
(pre)frontal cortex and RF, underlie two distinct functions: selective attention and arousal,
respectively. They assigned the term selective attention to the selective innervation from the
(pre)frontal cortex and the term arousal to the diffuse innervation from the RF. Selection between
one of the main sensory modalities can be realized by a selective lack of activation from the
prefrontal cortex of a sector of the RN that overlies the TCR nucleus corresponding to the
attended channel (Skinner & Yingling, 1977).
Note that, at the level of the TCR nuclei, there is a balance between the ascending activation from
the RF and the descending inhibition from the (pre)frontal cortex.
Skinner and Yingling’s model is often referred to as a gating model for the selective blocking of a
channel can be regarded as a closing of the respective gate.
Brunia (1993) suggested that the anatomic interrelations of the RN and the thalamic relay nuclei
may have more consequences than Skinner and Yingling noted (1977). Brunia pointed to the fact
that the RN does not only cover the thalamic nuclei involved in sensory information processing
but that the RN covers the thalamic motor nuclei as well. The mechanisms involved in attention
in the motor domain are therefore thought to be similar to those involved in perceptual selective
attention. As Brunia (1997) puts it: “Since the thalamic motor nuclei are equally under control of
the RN, it seems plausible that the RN, comparable to what we have seen in perception, is also in
a pivotal position to influence motoric processes.” Furthermore, Brunia (1993) notes that this
model, holds for anticipatory attention and motorpreparation as well as for selective attention
and motor execution. Brunia (1999) points out that the aim of anticipatory processes is to pre-set
relevant brain structures in order to ameliorate the processing of information. As was stated in the
introduction, anticipatory attention and motor preparation are similar processes. According to
Brunia (e.g. 1993), this resemblance can be traced down to the roots: the RN.


15

The model of Skinner and Yingling (1977) described only two major input sources of the RN (i.e.
the frontal cortex and reticular formation), there is, however, a third source of input that deserves
attention. Many of the fibers that go through the RN, passing either way between thalamus and
cortex, give off excitatory collaterals to the cells of the RN (e.g. Jones, 1985), thereby forming a
thalamocortical loop.
To exemplify the thalamocortical interrelations figure 3 shows the major connections between
thalamic relay cells, cells of the RN and the cerebral cortex in the visual system. Note that the
thalamic nuclei of the visual system and their associated cortical and reticular connections can be
categorized as being either first order or higher order nuclei.

Figure 3.

The thalamocortical network in the visual system. Adapted from Guillery et al.,
1998.


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As was described in this section, the selective mechanism of attention seems to be related to the
thalamocortical network. The following section will describe the thalamocortical network in more
detail. In addition, an outline of the neural basis of activity in the thalamocortical network will be
given. For a more thorough understanding of the neural basis the reader is referred to Lopes da
Silva (1991) and Steriade et al. (1990).

Rhythmic activity and the thalamocortical network
The prime candidate for the generation of rhythmic activity in the alpha band seems to be the
thalamocortical network (Steriade et al., 1990). In the ensuing sections the components of this
network and their characteristics will be described.

Intrinsic electrophysiological oscillatory properties of neurons in the
thalamocortical network
Steriade (1990) points out that there is ample evidence, that under imposed experimental
conditions, isolated neurons can display oscillations, usually within a frequency band of 1-20 Hz.
In the intact brain, these single cells are subject to influences from other sources that unite single
elements into ensembles. Three types of cells will be discussed.

Thalamocortical relay neurons
Jahnsen and Llinás (1984 a,b) demonstrated that the TCR neurons display oscillatory behavior at
either 6 or 10 Hz, depending on their level of polarization. The type of repetitive activity
described above (i.e. relatively low frequency repetitive activity) occurs when the membrane
potential is negative to –70 mV and has been termed burst-like activity (Deschenes et al., 1984;
Jahnsen & Llinás 1984 a,b). However, in addition to this oscillatory activity the cells are capable
of tonic repetitive activity and may serve as relay elements, when the cell membrane depolarizes
to a level of –60 mV or more. This activity may correspond to the transmission of afferent
activity to the cortex (Deschenes et al., 1984; Jahnsen & Llinás, 1984 a,b; Steriade & Llinás,
1988).


17

Thus, TCR neurons operate in two distinct modes:
1. burst mode, which is characterized by oscillations with a relatively low frequency
(around 10 Hz);
2. tonic mode, which is characterized by repetitive activity with a relatively high frequency.

Corticothalamic neurons
Cortical neurons are also capable of repetitive firing, as are TCR neurons. They are able to
respond very specifically to given thalamic input at given frequencies (Steriade et al., 1990).

Reticular thalamic neurons
Steriade et al. (1986) pointed out that the reticular thalamic neurons oscillate easily, in fact, more
readily than the other thalamic neurons due to their conductance properties (Llinás & GeijoBarrientos, 1988).

Oscillatory Properties of the thalamocortical network
For several types of brain oscillations, the pacemaker is thought to be located within the RN
(Steriade et al., 1990). One line of evidence comes from a study performed by Steriade et al.
(1985) who demonstrated that after disconnection of cortically projecting thalamic nuclei from
their RN inputs, oscillatory activity is abolished in TCR neurons.
The thalamocortical network includes two main feedback loops:

•

The hyperpolarization of the TCR cells caused by IPSPs from the RN neurons (Steriade
& Deschenes, 1984; Steriade et al., 1985) leads to the generation of burst mode action
potentials in the TCR neurons. The dendrites of the RN neurons have synaptic contacts
with the axons of the TCR cells. This produces rebound excitation that returns to the RN
neurons which results in an increased inhibitory influence on the TCR neurons (Steriade


18

& Llinás, 1988). This leads to a hyperpolarization of the TCR cells thereby establishing
the oscillatory state (Lopes da Silva, 1991).

•

In addition, corticothalamic neurons give collaterals to the reticular thalamic nucleus on
their way back to the thalamus thereby forming another feedback loop (e.g. Guillery et
al., 1998). Several studies (e.g. Steriade et al., 1993; Contreras & Steriade, 1996) suggest
that the corticothalamic projections may contribute to the regulation of the synchrony in
large portions of the thalamus. A slow cortical oscillation of 1 Hz (Steriade et al., 1993)
has been brought into relation with this regulatory function.

Synchronization and desynchronization
Steriade et al. (1990) pointed out that the various wave patterns of the EEG can be referred to as
synchronized or desynchronized patterns. While the first term implies the occurrence of highamplitude oscillations with relatively slow frequencies, the second term indicates a replacement
of synchronized rhythms by lower-amplitude and faster waves. Lopes da Silva et al. (1973)
demonstrated that there are coherences between alpha rhythms simultaneously recorded in
thalamus and cortex. Thus, scalp recorded activity may reflect the state of certain thalamic relay
nuclei. Synchronized EEG activity would thus correspond to the firing of thalamic relay nuclei in
burst mode whereas desynchronized activity would reflect the firing of thalamic relay nuclei in
tonic mode. As Steriade et al. (1990) and Lopes da Silva (1991) pointed out, the transfer to the
cortex is disengaged in burst mode. Thus, the cortex is deprived of relevant input, and will not be
engaged in active processing. Pfurtscheller (1992) suggested that the corresponding synchronized
oscillations that can be recorded at the scalp reflect a state of cortical inactivity, which he terms
cortical idling.
However, this distinction between burst mode (inactivity) and tonic mode (activity) may be
somewhat oversimplified, as Guillery et al. (1998) point out. Guillery et al. point to observations
that show that, even in burstmode, TCR cells can respond to sensory stimuli (Guido et al., 1995)
and that afferent activity is transmitted to the cortex. Strikingly, the signal-to-noise-ratio of the
afferent activity appears to be higher in the burst mode than in the tonic mode (Guillery et al.,
1998). Guillery et al. (1998) concluded that the thalamic cells, when in burst mode, are capable of
responding to novel activity patterns and then change to tonic mode so that new stimuli can be


19

accurately transferred to the cortex. “In burst mode the system is primed to react to changes in
input activity rather than to transfer this activity reliably to the cortex for analysis. For the latter,
the system needs to switch to the tonic mode” (Guillery, 1998).

Detecting oscillatory activity at the scalp
Scalp potentials are determined by the electrical equivalent dipoles of cortical activity. The
amplitude of the electrical equivalent dipoles depends on the total area of activated cortex and the
degree of synchrony between cortical neurons (Lopes da Silva & Pfurtscheller, 1999; Misulis,
1997). Furthermore, the detection of cortical activity depends on the topology of the cortical area
displaying synchrony and on the relation between this area and the electrodes at the scalp (Lopes
da Silva & Pfurtscheller, 1999). Computer simulations (Nunez, 1995) led to the general
assumption that, in order to present a frequency spectrum with a clear peak, a high degree of
synchronicity over a relatively large cortical area (about 100 mm2) is required (Lopes da Silva &
Pfurtscheller, 1999). Nunez (1995) estimated that the contribution to the local EEG of a number
of synchronously active generators (M) relative to the number of asynchronous active generators
(N) can be expressed in the equation: M/square root N. This formula indicates that an equal
number of synchronous and asynchronous generators results in a preponderant contribution of the
synchronous generators to the local EEG. The relative contribution of synchronously active
generators depends on the total number of active generators and thus on the extent of the cortical
area displaying oscillatory activity: a large cortical area requires a lesser degree of synchrony than
a relatively small one to maintain the relative preponderant contribution of synchronous
generators to local EEG. Hence, the larger the cortical area displaying oscillatory activity, the
greater the chance that it will be detected at the scalp, even though the degree of synchrony over
that particular area may be fairly low. Small cortical areas on the other hand require a high degree
of synchrony over the specific cortical area to be recorded at the scalp.

On the existence of different rhythms
Traditionally, cortical rhythms have been classified, solely on the basis of frequency bands. The
four frequency bands: 0.5-4 Hz, 4-7 Hz, 8-13 Hz and 13-25 Hz have been termed the delta, theta,
alpha and beta rhythm (Bickford, 1987; Misulis, 1997). Nowadays, it is not uncommon to view


20

rhythms with a frequency >30 Hz (e.g. Pfurtscheller, 1993) as a distinct rhythm (i.e. gamma
rhythm). However, describing cortical rhythms in terms of frequency bands alone, without taking
the alleged functionality into consideration may lead to false conclusions regarding the number of
distinct cortical rhythms. Nunez (1995) argued that next to a general cortical rhythm of
approximately 10 Hz, there are several local cortical rhythms that can be functionally
distinguished from each other. This distinction can be made clear by taking the differences in
scalp distribution and reactivity into account.
The notion of distinguishable rhythms in the same frequency band is not restricted to alpha band,
as studies several studies (e.g. Papakostoupolos et al., 1980; Pfurtscheller et al., 1997) indicate.
Papakostoupolos et al. (1980) observed central beta activity during self-paced movements with an
inconsistent blocking pattern. They demonstrated that whereas certain beta rhythms were blocked
during movement others were not affected and some even became more prominent.
Bastiaansen et al. (1999) enunciated that each of the three main sensory modalities and the motor
system all have their own corresponding rhythm. The rhythms of the visual, somatosensory/motor
and auditory system will be discussed.

The alpha rhythm
As Nunez (1995) pointed out, rhythms with a frequency of approximately 10 Hz can be measured
over large portions of the neocortex. These rhythms react differently to experimental
manipulations, indicating that these rhythms are distinct. However, since activity in the 10 Hz
frequency range is termed alpha (-like) activity, one could argue that these distinct rhythms are all
alpha, in essence. In order to avoid confusion, the term alpha rhythm, in this thesis, is restricted to
10 Hz rhythmic activity that reacts to manipulations in the visual modality and are characterized
by an occipital maximum.
The alpha rhythm has been recorded as early as 1929 by Berger. The alpha rhythm does not seem
to be a unitary phenomenon. Walter (1969) describes alpha rhythms in adjacent cortical areas,
that are believed to be involved in visual processing, which show clear differences in the
reactivity to experimental manipulations (Walter, 1969). This notion implies that there are distinct
rhythms in the visual system.


21

The central mu rhythms

The rolandic cortex of a relaxed human subject exhibits rhythmic oscillations that have become
known as (central) mu rhythms. These rhythms, previously known as “rolandic rhythm”,
“sensorimotor rhythm”, “precentral alpha rhythm” or “wicket rhythm” (Pfurtscheller, 1986), can
be detected both invasively (e.g. Toro et al., 1994) and noninvasively with
electroencephalography (e.g. Pfurtscheller et al., 1999) and magnetoencepahalography (e.g.
Salmelin et al., 1995).
The (central) mu rhythm is considered by many authors to be a normal resting rhythm of the
sensorimotor cortex area (e.g. Kuhlman, 1978; Salmelin & Hari, 1994, 1995; Tiihonen et al.,
1989). However, Niedermeyer (1993) points out that when the EEG is visually scored, the central
mu rhythm is observed in no more than 15% of the clinical EEG records. Pfurtscheller and
Neuper (1994) point to the notion that the prevalence of the (central) mu rhythm has been
reported to fall into the range of 50 to 100% when appropriate EEG derivations and computer
methods are used. Furthermore, Salmelin and Hari (1994, 1995) argue that the (central) mu
rhythm can be detected in practically all subjects in MEG records.
The (central) mu rhythm is neither affected by opening/closing of the eyes nor by auditory stimuli
as is the case with occipital alpha rhythm (Walter, 1969) and tau/third rhythm (Niedermeyer,
1990, 1991), respectively. Furthermore, unlike tau and occipital alpha rhythms, the (central) mu
rhythm is suppressed by tactile stimulation, motor preparation, execution of movements and even
imagination of movement (e.g. Chatrian et al., 1974). These findings suggest that the (central) mu
rhythm is related to the sensorimotor system.
Gastaut (1952), who described the (central) mu rhythm in detail referred to the mu rhythm as the
“rhythme en arceau” because of its arch-like shape. This shape suggests that it is built up of
distinct components. Storm van der Leeuwen et al. (1978) and Pfurtscheller (1981) argue that a
harmonically related frequency results from the arched waveform if spectral analysis is used.
This harmonic relation was confirmed by various studies (e.g. Niedermeyer & Lopes da Silva,
1982). Two spectral peaks are found when the classical Fourier method is used for estimation of


22

the power spectrum of the arch-like mu rhythm: one in the alpha band representing the basic
frequency of the mu rhythm and another in the beta band at the first harmonic frequency. (e.g.
Pfurtscheller, 1997). The dominant component rhythms are considered to peak at approximately
10 and 20 Hz (e.g. Tiihonen et al., 1989; Hari & Salenius, 1999). Salmelin and Hari (1994) argue
that these frequencies may display independent activity besides the coherent oscillations present
in the central mu rhythm. For this reason, “the configuration of the mu rhythm signal can deviate
strongly from the classical wicket shape; this might be one reason for the poor detection of mu in
the scalp EEG records” (Salmelin & Hari, 1994).
The notion that both components may, to some extent, occur independently persuaded many
authors to make a distinction between 10 Hz mu and 20 Hz beta activity despite the original
definition of Gastaut (1952) who considered the whole arch-liked rhythm (10 Hz plus 20 Hz) to
be mu.
Recent experiments seem to justify the distinction between 10 Hz and 20 Hz components of the
mu rhythm for they suggest that both components differ in their generation sites, timing and
reactivity (Salmelin & Hari, 1994, Salmelin et al., 1995; Tiihonen et al., 1989) and thus can be
functionally segregated (Salmelin et al., 1995).
A number of electrocorticographic studies have provided evidence for the notion that the central
mu rhythm is generated by neuronal structures in the pre and postcentral gyri (e.g. Gastaut, 1952;
Jasper & Andrews, 1938; Jasper & Penfield, 1949; Kuhlman, 1978; Kruger & Henry, 1957;
Papakostopoulos et al., 1980). The precise locations of the generator sites of the distinct
component rhythms, however, was not decisively revealed by this array of studies.
In his pioneering work Berger (1929) demonstrated that electrocortical 20 Hz activity could be
recorded in the precentral cortex. The first observation of the attenuation of precentral beta
rhythms stems from Jasper and Andrew (1938). They reported on a depression of beta rhythms
(with an average frequency of about 25 Hz) by tactile stimuli from the contralateral part of the
body. The observation that precentral beta over the motor hand area was blocked by fist clenching
led Jasper and Penfield (1949) to interpret the precentral beta rhythm as the idling activity (see
the section on synchronization and desynchronization) of the resting motor cortex.


23

On the basis of their intra-operative recordings, Jasper and Penfield (1949) considered the central
fissure to be an important borderline, separating 20 Hz rhythms (precentral) and 10 Hz rhythms
(postcentral). Several studies, however, do not seem to favor a strict application of this borderline
model. Papakostopoulos et al. (1980), for example, demonstrated that the central sulcus does not
constitute a definite borderline between 10 and 20 Hz rhythms, for he recorded beta activity (1632 Hz) in both pre- and postcentral areas in man. Furthermore, Rougeul et al. (1979) found 20 Hz
rhythms, which were blocked by the smallest body movement, over the SI hand area and posterior
parietal cortex in monkeys.
Yet, recent studies indicate that the two rhythms which constitute the classical mu rhythm may
indeed differ in their generation sites (Salmelin & Hari, 1994; Salmelin et al., 1995; Tiihonen et
al., 1989) and that these differences center round the borderline as suggested by Jasper and
Penfield in 1949. Furthermore, the two main component rhythms display differences in timing
and reactivity. Taken together, these differences may point to differences in functionality as well.
Source analysis of the 10 Hz and 20 Hz components of the classical mu rhythm (Salmelin and
Hari, 1994; Salmelin et al., 1995) demonstrated that the generators of the 10 Hz rhythm were
confined to the postcentral gyrus (and extended to the parietal lobe; Salenius et al., 1997) while
generators of the 20 Hz components were predominantly located in the precentral gyrus. Note that
they found some 20 Hz generators in the post-central gyrus as well. Several EEG studies
displayed similar topographic differences consisting of a clear attenuation of 20 Hz activity
localized slightly anterior to the desynchronization of 10 Hz activity (e.g. Pfurtscheller & Neuper,
1994).
Salmelin et al. (1995) found that the sites of maximal suppression and subsequent rebound of the
20 Hz rhythm followed the somatotopic (or motorotopic, as they termed it) representation of
fingers, toes and tongue over the motor cortex. Furthermore, they found that the reactivity of the
10 Hz rhythm did not vary with the type of movement. However, Toro et al. (1994) reported that
subdural recordings revealed that the 10 Hz reactivity also follows a somatotopic organization.
This somatotopic organization was not present in simultaneously recorded scalp EEG.
Their neuromagnetic recordings brought Salmelin and Hari (1995) to hypothesize that the 10 Hz
signal is a true somatosensory rhythm whereas the 20 Hz activity is essentially somatomotor in
nature.


24

The distinction between mu and beta rhythms is a rather crude one, however, and several studies
indicate that there may be a wide variety of distinct cortical rhythms, originating in the
somatomotor cortex, each of them displaying different intrinsic characteristics. (e.g. Hari &
Salenius, 1999; Papakostoupolos et al. 1980; Pfurtscheller, 1999; Pfurtscheller and Neuper,
1997).

The tau rhythm
The tau rhythm presumably does not form part of the present investigation. Therefore, the
discussion of this rhythm will focus on familiarizing the reader with this rhythm rather than
providing a thorough description.
In 1990 Niedermeyer recorded rhythmical activity in the alpha frequency band over the temporal
lobe by means of epidural and intracortical recordings. Niedermeyer (1990, 1991), demonstrated
that “the third rhythm” could be functionally distinguished from the (occipital) alpha and
(sensorimotor) mu and beta rhythms. Tiihonen et al. (1991) observed a magnetoencephalographic
rhythm with comparable characteristics. Hari (1993) reported on similar findings and termed this
rhythm the “tau rhythm”. Source analysis and the notion of a clear attenuation of the tau rhythm
following auditory stimuli strongly suggest that the tau rhythm is an intrinsic rhythm of the
auditory cortex (Hari et al. 1997).

Physiological measures in attention research

As the historical overview pointed out, attentional phenomena have received a great deal of
interest during the second part of the 20th century. Nowadays researchers in the field of attention
have a wide variety of measurement techniques at their disposal. The most commonly used brain
imaging techniques include: EEG (scalp recordings, intraoperative/subdural recordings of
neuronal populations, intraoperative single cell recordings), MEG, MRI, fMRI, event-related
fMri, CAT, PET, and rCBF. It is remarkable that EEG, which is the oldest measurement
technique of human brain activity – Hans Berger discovered the human EEG in 1929- still in use
today, is probably the most frequently applied technique in attention research. Three main


25

characteristics of EEG seem to account for this situation to evolve. Firstly, the costs of EEG
equipment are modest compared to, for instance, MEG and fMRI. Secondly, EEG measurements
are characterized by a high temporal resolution, which is not matched by certain imaging
techniques as rCBF, PET and MRI, which have a higher spatial resolution. It must be noted that
the temporal resolution of certain imaging techniques (e.g. Event Related fMRI) is increasing due
to further modifications, but is still on the scale of seconds. Thirdly, EEG (and MEG) data,
provided that their acquisition meets certain requirements, can be analyzed in distinct ways. One
possible analysis is the transition into power values by means of Event Related
Desynchronization (Pfurtscheller & Aranibar, 1977). EEG (and MEG) thus enables the
investigator to derive distinct and complementary measures from one and the same dataset.

EEG
Potentials
The proposed neurophysiological mechanism underlying Event Related Potentials (ERPs) is the
summation of depolarizing Excitatory Post-Synaptic Potentials (EPSPs) and hyperpolarizing
Inhibitory Post-Synaptic Potentials (IPSPs) on the membrane of efferent neurons in the cortex
area underlying the electrodes (Birbaumer et al., 1990; Misulis, 1997).
EPSPs lead to a depolarization, which brings the membrane potential nearer to the firing
threshold, thereby increasing the chance that the cell will actually fire. IPSPs, on the other hand
decrease the chance that an action potential will develop (e.g. Böcker, 1994).
The spatial and temporal integration of all EPSPs and IPSPs determines the membrane potential
at the triggerzone near the axon hillock. If this potential exceeds about –50mV an efferent action
potential develops, i.e. the cell fires.

ERD
Sensory stimulation can elicit two types of changes – both poststimulus and prestimulus- in the
electrical activity of the cortex (see figure 3): evoked activity changes and induced activity
changes (Pfurtscheller & Lopes Da Silva, 1999). The first change is both time-locked and phase-


26

locked to the occurrence of an event, whereas the second change is time-locked but not phaselocked. This difference has important implications for the analysis of electrical activity: the two
changes require distinct methods of analysis. Whereas simple linear methods (e.g. averaging)
suffice to extract the evoked activity changes from the raw data set, these methods fail in
extracting induced activity. The method of averaging, common in ERP studies, cannot be applied
for it causes cancellation of the non-phase-locked manifestations present in induced activity
changes.

Figure 4.

Schema of generation of two types of changes in the electrical activity of the
cortex: induced activity and evoked activity. TCR: thalamocortical relay cells;
RE: reticular nucleus. From Lopes da Silva & Pfurtscheller (1999).

In order to be able to reveal induced activity changes, a different method of analysis should be
applied. ERD is such a method.
Note that averaging is an intrinsic characteristic of scalp recorded EEG. Firstly, the summation of
EPSPs and IPSPs at the neuronal level can be regarded as a form of averaging, since several
inputs result in one net output. Secondly, electrodes are not able to detect all of the potential


27

changes at the cortical surface. Potentials are volume-conducted through the meninges, skull, and
scalp before they are picked up by the surface electrodes. These tissues act as a spatial low pass
filter that causes the potential at the scalp to appear blurred and attenuated in comparison to
cortical activity (Van Burik et al., 1999) i.e. one does not only record activity from cortical areas
directly underlying the electrode, but also activity from adjacent cortical areas. Therefore, if an
electrode is placed on the scalp, the electrical activity from a restricted cortical area is averaged
(Pfurtscheller & Aranibar, 1980). Cooper et al. (1965) pointed out that potentials recorded with
subdural electrodes show a wide variability in form and phase over small cortical areas. Since one
scalp electrode may cover several of these small cortical areas, the variability in form and phase is
partially cancelled out by spatial averaging.

The computation of ERD
The major advantage of Event-Related Desynchronization over linear averaging techniques is its
ability to quantify induced activity in a reliable manner, i.e. without phase cancellation. Several
ways of calculating ERD and topographical mapping have been suggested since Pfurtscheller and
Aranibar reported on this technique in 1977. Each of these classical ERD derivatives seems to
bear intrinsic advantages and disadvantages. The method of choice therefore depends on the
experimental parameters (such as electrode configuration) and the (expected) characteristics of
the bioelectrical data (see Appendix B).

The classical ERD
Pfurtscheller (1999) states that several requirements should be met in order to perform ERD. One
must have at least 30 event-related EEG trials, synchronously time-locked to an internal or
external event, at one’s disposal. Furthermore, both these trials and the intervals between
consecutive events should span at least some seconds. The quantification of classical EventRelated Desynchronization encompasses 6 steps (e.g. Bastiaansen, 2000):


28

1. select the frequency bands of interest;
2. apply a (digital) band pass filter with the desired characteristics to the data, eliminating
frequencies that lie out of the desired frequency band1;
3. the amplitudes of the filtered EEG epochs are squared in order to avoid phase
cancellation, thereby obtaining power values of that particular frequency band;
4. the power values are integrated over a number of consecutive samples in order to obtain a
more reliable estimate of the power rendering power values over a certain time window
(e.g. 250 ms);
5. the power values in each time window (e.g. 250 ms) are averaged over all corresponding
epochs (e.g. 250 – 0 ms premovement);
6. the quantification of ERD is expressed as the change of power at each time window
relative to the (average) power in a reference interval. A power increase is denoted by an
Event-Related Synchronization (ERS) whereas a power decrease is denoted by and an
Event-Related Desynchronization (ERD), respectively2.
A possible drawback of the classical ERD is that it is not capable of differentiating between
induced activity and evoked activity. Furthermore, the classical ERD is characterized by a rather
poor temporal resolution. Appendix B describes a method that can account for evoked activity
and a method that has a higher temporal resolution than the classical ERD.

Preparatory processes: slow potentials and ERD
Motor preparation
Slow Potentials
In 1965 Kornhuber and Deecke reported on a negative slow potential in the EEG that preceded
self paced movements. They termed this negativity Bereitschaftspotential (BP). The BP, or
Readiness Potential (RP) in English, is characterized by a maximum amplitude over the vertex
and a preponderance of negativity over the hemisphere contralateral to the movement side, for
hand movements (e.g. Böcker, 1994). Preceding foot movements, the RP is larger over the
1

See appendix B
ERD%= ((A-R)/R)*100 with “A” denoting the power in the timewindow of interest and “R” denoting the average
power in the reference interval. Note that this equation assigns a negative value to an ERD and a positive value to an
ERS.
2


29

ipsilateral motor cortex. (Brunia, 1980; Brunia & Vingerhoets, 1981). Its scalp distribution is also
influenced by other movement parameters, such as the complexity of movement (e.g. Lang et al.,
1989) and the number of fingers involved in he movement (Kitamura et al., 1993). The
characteristics of the RP indicate that the locus of its maximum amplitude roughly corresponds to
the cortical representation of the moving body part.
The RP may develop as early as 1500 ms prior to movement onset. Note that a RP is not a
prerequisite for movement execution: in non-forewarned Reaction Time experiments, no RP is
recorded (Deecke & Kornhuber, 1977; Kutas & Donchin, 1980).

ERD
In contrast to the BP, the ERD may already start around 2 s prior to the onset of a voluntary,
selfpaced finger movement (Pfurtscheller & Berghold, 1989; Stancák & Pfurtscheller, 1996) over
the contralateral hemisphere. Shortly before movement onset the ERD appears on the ipsilateral
side as well (e.g. Pfurtscheller, 1999). During execution of movement the ERD becomes almost
symmetric on both hemispheres. After movement execution, the ERD slowly makes way for a
rebound of synchronized activity. Although there is some variability in the reported onset of
synchronization, the beta rhythm synchronization is generally reported to develop well within the
first second after movement offset whereas the mu rhythm synchronization builds up 1 to 2
seconds after movement offset (e.g. Leocani et al., 1997; Pfurtscheller et al., 1996). The
topography of the ERD depends on, at least, the handedness of the subject, response side,
analyzed frequency band, moved limb and several other kinematic parameters (e.g. Pfurtscheller,
1999; Stancák & Pfurtscheller, 1996; Toro et al. 1994).
Several studies suggest that the post-movement rebound of central rhythms represents a localized
hypersynchronization of those motor cortical areas, which have been active during motor
preparation (Pfurtscheller et al., 1996; Salmelin and Hari, 1994; Salmelin et al., 1995; Stancák &
Pfurtscheller, 1995; Toro et al., 1994).


30

Slow potentials
In order to study processes related to stimulus anticipation, the presentation of a stimulus is
required. Furthermore, the design should prevent non-stimulus related activity from interfering
with stimulus related activity. In a series of experiments Damen and Brunia (1987a, 1987b, 1994;
Brunia & Damen, 1988) solved this problem by separating motor related and stimulus related
processes in time. Preceding stimulus presentation, which took place 2 seconds after movement
onset, they recorded a negativity that differed from that of the RP. They termed this negativity
“Stimulus Preceding Negativity”. The SPN has been recorded prior to three types of stimuli:
1. Knowledge of Results stimuli (e.g. Damen & Brunia, 1987a);
2. Instruction stimuli conveying information about a future task (e.g. Gaillard & Van
Beijsterveld, 1991);
3. Probe stimuli, with which the outcome of a previous task has to be matched (e.g. Chwilla
& Brunia, 1991).
The amplitude and the distribution of the SPN appear to vary with the type of stimulus that is
anticipated. Prior to KR stimuli the SPN shows a widespread distribution. Over the parietal cortex
a steep increase in negativity is found (see Brunia, 1999) whereas over the frontal areas (frontotemporal; e.g. Bastiaansen et al., 1999) the SPN is manifest as a sustained negativity. The SPN
shows a right-hemispheric preponderance. Preceding instruction stimuli, the SPN shows a parietal
maximum but shows a bilateral symmetrical distribution. Moreover, the amplitude is smaller than
the pre KR SPN. The SPN prior to Probe stimuli shows a parietal maximum but exhibits a lefthemispheric dominance. These findings indicate that the SPN might not merely reflect a
perceptual process.
Thus, like the RP, the distribution and the amplitude of the SPN seems to depend on the
characteristics of the anticipated event. However, not all parameters influence the manifestation
of the SPN. Böcker et al. (1994) found that KR stimuli of different modalities (i.e. auditory and
visual) were preceded by a SPN with a similar scalp topography. Bastiaansen (2000) found the
same results with respect to the SPN in the visual and somatosensory modalities. Although recent
findings from our lab seem to show differences in scalp distribution of the SPN the SPN does not


31

give strong support for the extended thalamocortical model. As pointed out in the section on
EEG, this could be a result of a shortcoming of the method.

ERD
Although Bastiaansen was the first to carry out experiments that were aimed at elucidating
preparatory processes by making use of ERD, four earlier studies touched upon the topic of
anticipatory attention and ERD. In 1991, Pfurtscheller and Klimesch, described an occipitally
localized ERD in the lower alpha band prior to the presentation of visual stimuli. Furthermore,
Pfurtscheller (1992) found an occipital ERD starting at 1 s before the presentation of visual
stimuli and simultaneously recorded an ERS at central electrodes. Klimesch et al. (1992, 1998)
suggested that the alpha band can be divided into two functionally distinct bands. The upper alpha
rhythm (approximately 10 – 12 Hz) is selectively associated with the processing of sensorysemantic information. The 8 – 10 Hz band would reflect expectancy, since the rhythmical activity
in this band clearly attenuates about 1 s before stimulus presentation. However, experiments by
Bastiaansen (1999, 2000) did not support these findings.
Bastiaansen (1999) performed an ERD computation on Böcker’s (1994) dataset and did find
differences in scalp distribution between the visual and auditory modality. This study clearly
shows that slow potentials and ERD are measures that can be used in a complementary way
because they convey different types of information. Bastiaansen (1999) reported on a significant
ERD in the 10-12 and 12-16 Hz frequency bands at occipital sites and not at temporal sites prior
to the presentation of a visual stimulus. However, such a difference was not found prior to the
presentation of an auditory stimulus: there was no significant ERD neither at temporal nor at
occipital sites in the 10-12 Hz frequency band. With MEG Bastiaansen (2001) replicated the
results of the first study with regard to the occipital ERD preceding visual KR stimuli. Moreover,
he demonstrated that two out of five subjects displayed a clear ERD at temporal sites preceding
the auditory KR stimulus in the 8-10 Hz frequency band. Although two out of five subjects may
not seem convincing, Bastiaansen demonstrated that the level of tau power in the baseline interval
was significantly higher for the two subjects. This may be the result of a well-developed tau
rhythm, or may be due to the orientation of the tau generators relative to the MEG sensors: a
tangential orientation increases the likelihood of recording the tau rhythm.


32

Aims of the present study
The main aim of this thesis is to test the thalamocortical gating model (1999).
In this model anticipatory attention in the three main modalities is mediated by one underlying
mechanism: the thalamocortical network. Several components of this network display oscillatory
properties. The manifestation of this oscillatory activity can be recorded by means of scalp
recorded EEG. However, in scalp recorded EEG this oscillatory activity may not be detected
without proper computational techniques, such as ERD. There is reason to believe that
anticipatory attention in both the visual and auditory modality is reflected by an ERD over the
respective sensory projection areas. The finding that anticipatory attention in the somatosensory
modality is reflected by an ERD over the somatosensory cortex would provide indirect evidence
for the thalamocortical gating model (Brunia, 1999).
The following hypotheses will be tested and discussed in the ensuing sections of this report:
1. Preceding somatosensory stimuli a postcentral ERD is present
2. The pattern of stimulus-related power changes, differs between conditions
3. Preceding visual stimuli an occipital ERD is present
4. The pattern of movement-related power changes does not differ between conditions


Methods
Subjects
Nine healthy right-handed subjects, 5 male and 4 female, participated in the experiment. The
subjects, with ages ranging from 18 to 22 years (M=20) were all undergraduate students. They
received either study credits or were paid fl. 7.50 (about 3.3 US $) an hour for their voluntary
cooperation. All subjects met the following requirements:

•

no psychiatric history

•

no history of psychotropic drug treatment

•

no history of clinical brain examinations

•

normal eyesight (after correction)

•

normal hearing

Experimental design & procedure
Experimental design
The experiment consisted of three conditions:

•

Voluntary Movement (VM);

•

Time estimation task with a visual feedback stimulus (VIS);

•

Time estimation task with a somatosensory feedback stimulus (SS).

The Time estimation with a Knowledge of Results (KR) - paradigm, presumably allows for a
separation of response-related and stimulus-related processes in the time domain. Thus, motor
preparation and anticipatory attention are dissociated on a time basis.

33


34

KR

WS

Figure 5.

response
The time estimation paradigm with KR stimulus. See text for details.

The experiment was made up of 6 blocks in total: each condition encompassed two blocks, one
for each response side (i.e. left hand or right hand). During a block subjects produced unilateral
responses of one hand only, which were measured by a force transducer.
In the Voluntary Movement condition subjects were instructed to produce self-paced rapid
unilateral flexions of index-finger and thumb about once every 10 seconds with a minimum interresponse time of 7.5 s.
The voluntary movement condition always preceded the two time estimation conditions because
unwanted differential carry-over effects were expected to occur. The carry-over effects from the
voluntary movement task to the two time estimation tasks were expected to be less disturbing
than vice versa. The carry-over effects were expected to occur due to the time constraints
involved in the time estimation conditions.
Each trial in the time estimation condition started with an auditory warning signal (910 Hz, 53
dB, 200 ms). The subjects had to estimate a four second interval following the auditory warning
signal by pressing the force transducer. They had to produce the same kind of rapid unilateral
response as in the voluntary movement condition. Two seconds after the response, they were
informed about the correctness of the estimated time interval by either a visual or somatosensory
KR stimulus, depending on the condition at issue. Note that the subjects were not informed about


35

the actual length (i.e. 4 seconds) of the target-interval. Subjects had to determine the target
interval by relying solely on the provided KR stimulus to guide their future estimations.
The order of the four KR blocks was randomized over subjects as was the order of the two
Voluntary Movement blocks preceding the time estimation blocks.
The intertrial interval between KR and the next WS varied randomly in steps of 1 second between
7 and 11 seconds.
A block consisted of 80 behaviorally valid trials. In order for a time estimation trial to be
behaviorally valid, the estimated time interval should fall into the range of 3500 ms to 4500 ms.
The inter-trial interval in the Voluntary Movement condition should measure at least 7.5 seconds
in order to be behaviorally valid. The completion of the experiment thus rendered 6 * 80
behaviorally valid trials.

Procedure
The experiment took place in an electrically shielded, sound attenuating and dimly lit cabin. The
cabin consisted of two separate compartments.
Subjects were seated on a comfortable, slightly reclining chair (height bottom of chair
approximately 50 cm) while their feet rested on a pedestal mounted on the chair and placed in a
position most comfortable to the subject.
Preceding the experiment, subjects were instructed to sit still and to minimize the number of
blinks and eye-movements during the intervals of interest to the experimenter.
In order to establish a criterion for a valid response, the maximal voluntary force that the
individual could exert on the force transducer was measured. Subjects were asked to hold the
transducer between thumb and index finger (pincer-grasp) and to flex both extremities as hard as
possible without overstraining. The criterion of a valid response was set a 20% of the maximal
voluntary force.


36

The width of the time-window considered correct was individually adjusted and obtained in a
training session preceding the two time estimations. In total 4 training blocks were carried out.
The width of the time window was set in such a manner that approximately 60% correct trials
would be obtained. The time window used in the subsequent blocks was calculated by averaging
the time windows in the training blocks.
Subjects were instructed to refrain from counting or any other rhythmic behavior during the timeestimation interval. Furthermore, they were stripped of all devices that could enhance the
performance on the time estimation task.
Every block was preceded by an amplifier calibration trial followed by an EOG calibration trial.
During this EOG calibration trial subjects had to track dots appearing at different locations on the
screen by means of eye-movements only. After every experimental block a second amplifier
calibration trial took place.

Apparatus and KR Stimuli
Apparatus
Experimental control and stimulus timing was accomplished using a PC and logical circuitry.
Two 8 mm non-polarizing Beckman electrodes (interelectrode distance from center to center
measured 3 cm) were placed on the subjects’ right calf muscle (m. gastrocnemius medialis). The
electrodes were positioned in a rostro-caudal manner, thereby forming an imaginary vertical axis
on the calf.
The electrical stimulations were administered using a Grass S88 stimulator, a Grass SIU 5
Stimulus Isolation Unit and a Grass Constant Current Unit, mounted in series.
Response manipulanda (static isometric force transducer, 5.5 cm) were placed at the end of each
arm support. They were positioned in a fashion most comfortable to the subject. The subject held
the response manipulandum between thumb and index finger (pincer-grasp). A computer screen
and a loudspeaker were placed in front of the subject at eye-level. The distance between subject
and computer screen measured approximately 1.3 meters.


37

Stimuli
In both the visual condition and the somatosensory condition the KR stimulus informed the
subject about the quality of their time estimation by indicating whether their estimated time
interval was too short, correct or too long. The visual KR stimulus consisted of a white vertical
bar (length 4 cm, width 1 cm) against a black background, centrally presented on the computer
screen placed in front of the subject. The KR stimulus was coded by the number of repetitive
presentations of the visual stimulus on the screen. This number could be 1, 2 or 4, encoding an
estimated interval which was too short, correct or too long, respectively. The visual stimulus
appeared on the screen for 30 ms. The inter-stimulus interval in the case of multiple presentations
measured 120 ms. Therefore, conveying a KR stimulus indicating that the subjects’ estimated
interval was too long took 480 ms in total.
The somatosensory KR stimulus was coded by the number of electrical stimulations. During the
stimulation intervals a pulse (duration 10 ms) was generated by the stimulator and administered to
the subject either 1, 2 or 4 times with an inter-pulse interval of 140 ms. Therefore, conveying a
KR stimulus indicating that the subjects’ estimated interval was too long took 460 ms in total.

Electrophysiological recordings
EEG recordings
For the EEG-recordings 27 non-polarizing Beckman 8 mm Ag-AgCl electrodes were affixed to
the scalp. Most of them were placed according to the international 10-20 system. Standard
positions were Fp1, Fp2, F7, F3, Fz, F4, F8, T3, T4, T5, T6, P3, Pz, P4, O1, Oz, O2. Nonstandard positions were C3’, C1’, Cz’, C2’, C4’, C3’’, C1’’, Cz’’, C2’’, C4’’. C3’, Cz’ and C4’
were mounted on the scalp 1 cm anterior to C3, Cz, C4 respectively. C1’ and C2’ were placed at
one half of the distance between C3’ and Cz’ and between Cz’ and C4’ respectively. C3’’, Cz’’
and C4’’ were affixed to the scalp 2 cm posterior to C3, Cz, C4 respectively. C1’’ and C2’’ were
placed at one half of the distance between C3’’ and Cz’’ and between Cz’’ and C4’’ respectively.
The distance between the primes and their corresponding doubles thus measured 3 cm from
center to center. The electrode montage was designed to cover the scalp above the entire brain
with an increased spatial resolution over the primary motor and somatosensory cortices.


38

Electrode impedance was kept below 5 kOhm. Software-linked mastoids served as a reference.
The EEG signals were amplified by home made amplifiers with a 30 s time-constant. The low
pass filter was set at 70 Hz (-42Db/octave).
Epochs of 3000 ms premovement to 3500 ms postmovement were digitized online with a
sampling frequency of 256 Hz using a 12 bit AD converter.

EOG recordings
The EOG signals were amplified by home made amplifiers with a 30 s time-constant. The low
pass filter was set at 70 Hz (-42Db/octave). The EOG was recorded using 6 non-polarizing
Beckman 2.1 mm electrodes. The horizontal EOG from the outer canthi and the vertical EOGs of
both eyes were recorded for off-line EOG correction (Van den Berg-Lenssen et al., 1989).

Data reduction and statistical analysis
Artifacts
An automatic artifact detection was performed, discarding trials of which the epochs of interest
did not meet the following criteria (note that these criteria are the most liberal ones used):

•

the epoch did not contain spikes that exceeded 100 µV

After applying a 2 Hz low pass filter to the data
•

individual sample values did not differ from each other by more than 90 µV

•

the mean amplitude in 4 subsequently sampled intervals of equal length may not differ
from baseline by more than 35 µV.

The minimum number of trials was set at 30.


39

Trials that did meet the afore described criteria were EOG-corrected using an autoregression
model based on EOG calibration trials recorded before each experimental block of trials (see Van
den Berg-Lenssen et al., 1989)

ERD computation
In order to obtain reference-free data and to eliminate ERD/ERS effects at the reference
electrodes, Perrin (1987, 1989) recommends to transform the recorded potentials into Scalp
Current Density (SCD) fields by estimating a spherical spline function. Compared to classically
used methods (e.g. the four neighbors method used in Hjorth’s (1975) source derivation), this
interpolation technique can provide better estimates at the borders of the electrode montage.
Next a FFT was performed on the entire sampling epoch, after which the data were smoothed
twice using a moving Hamming window with a 3-sample length. Subsequently, the weights for
frequencies outside the desired frequency band were set to zero. This procedure (i.e. band pass
filtering) yielded the frequency band of interest. Finally, the data were transformed back to the
time domain. The entire procedure was carried out twice resulting in two separate frequency
bands: 8-12 Hz (mu) and 17-23 Hz (beta) frequency bands.
Power values were computed by squaring the amplitudes. Intervals of 32 consecutive samples
were averaged, giving rise to 26 time intervals of 250 ms each. Although these settings are
detrimental to the temporal resolution, these parameters are necessary to avoid unreliable
estimates of the power. The 250 ms interval prevents this from happening since the sinusoidal
period of the slowest rhythms of interest measures 125 ms (8 Hz, f = 1/t). Hence, the sample
interval covers 2 periods of the slowest frequency component.
Since the data were recorded on a trial-by-trial basis, the first and the last 250 ms intervals of the
6500 ms sampling epoch were invalid because of an inherent discontinuity in the data. For each
subject, data were averaged over trials, and ERD was computed as the percentage power change
for a particular time interval in one of two selected frequency bands, relative to the reference
interval. This interval ranged from 2750 to 2000 ms pre-movement.
The resulting percentages were averaged over all subjects for display purposes.


40

Statistical analysis
Behavioral data
Before trials were rejected to rid artifacts, each behavioral data block contained 80 behaviorally
valid trials (for criteria see section on artifacts) and could contain a number of behaviorally
invalid trials as well. Behavioral measures (as described below) were not corrected for trial
rejections, unless this is explicitly stated. The time estimation values were measured from WS
onset to response onset for each trial. In order to ascertain the effects of the KR stimulus two
behavioral measures were derived from the time estimation values:

•

the percentage of estimated interval too short/OK/too long;

•

the effectiveness of the KR stimulus.

The percentage too short/OK/too long is a measure for the quality of the time estimation. These
data were analyzed by an ANOVA with Response category (Too short/OK/Too long), KR
modality (Visual, Somatosensory) and Response side (Left hand, Right hand) as repeated
measures.
In order to ascertain the effectiveness of the KR stimulus, the percentage of correctly adjusted
trials following trials with too short or too long time values was computed for each KR condition
separately. Correctly adjusted trials were defined as trials that show a change in time estimation
values in the desired direction, indicated by the KR. In practice, this means that for a correct
adjustment of a “too short time estimation value” to take place, the subsequent trial should show
an increase in time estimation value relative to the former trial. In case of a “too long time
estimation value” the subsequent trial should display a decrease in time estimation value. These
data were analyzed by and ANOVA with KR modality (Visual, Somatosensory) and Response
side (Left hand, Right hand) as repeated measures.

Physiological data
The analysis of the physiological data centered around two presumably distinct processes: motor
preparation (motor related activity) and stimulus anticipation (stimulus related activity). Due to


41

the experimental design, both processes are thought to take place at different points in time. The
analysis of the power changes (relative to the reference interval) related to either motor
preparation or stimulus anticipation are therefore dealt with separately.
Since the largest anticipatory effects are expected to become maximally manifest just before the
upcoming event -whether it is movement onset or stimulus presentation – only the last 250 ms
intervals preceding that event were statistically analyzed.

Motor related ERD
Since postmovement effects are thought to interfere with the pre stimulus activity (e.g.
Bastiaansen, 1999) masking the latter, the elimination of these effects would presumably result in
an uncontaminated display of anticipatory attention preceding the KR stimulus. Bastiaansen
(1999), reported on such a removal of movement-related activity by subtracting the ERD data of
one experimental condition, which was thought to encompass mere movement-related activity
(VM condition), from the experimental condition believed to encompass both stimulus-related
and movement-related activity (KR condition). This subtraction can only be justified if the
movement-related activity does not differ significantly between the three different conditions, as
Bastiaansen (1999) pointed out. These movement-related effects were most prominent at central
electrodes, showing an ERD pre-movement and a strong ERS postmovement, which is in line
with previous findings (e.g. Pfurtscheller et al., 1999).
Several studies (e.g. Pfurtscheller, 1981; Pfurtscheller et al., 1999) suggested that the level of
power change over a certain cortical area is the result of a summation of power changes
stemming from neurons located in the cortical area covered by the registration device. Thus, one
should realize that the power change over a certain area is not as much a unitary level of power
change present in the entire area covered by the registration device but rather an average power
change over groups of neurons in a cortical area that is covered by the registration device.
Opposite power changes in adjacent cortical areas may therefore overrule one another in favor of
the larger of the two opposing power changes, or, in the rare case of similar magnitudes of the
opposing power changes, cancel each other out. Movement-related effects may interfere with the
manifestation of stimulus-related activity, given that the two processes overlap each other in time.
Hence, the motor cortex and somatosensory cortex are in close vicinity to each other and both


42

motor related activity and anticipatory attention are presumed to be prominent at central
electrodes. For this reason, the elimination of movement-related activity would seem highly
profitable, if not necessary.
The main point of interest regarding the movement related ERD is thus to test whether motor
related activity was similar in all three conditions (VM, Vis, SS). If this is indeed the case, the
VM condition can be subtracted from the KR conditions, thereby removing its masking effect on
the manifestation of anticipatory attention.
A second point of interest was determining the effect of response side on the distribution of
powerchanges over the scalp.
Furthermore, since the 10 Hz rhythm and the 20 Hz rhythm are believed to reflect different
processes and are reported to display distinct topographic characteristics (Salmelin & Hari, 1995),
both bands are incorporated in the analysis.
An ANOVA was performed with Experimental condition (VM, SS, Vis), Response Side (Left
hand, Right hand) Band (10 Hz, 20 Hz), Electrode Position (Precentral, Postcentral) and
Hemisphere (Left hemisphere, Right hemisphere) as repeated measure factors. For this analysis,
electrodes C3’ and C4’ were used to assess pre-movement power changes at precentral sites
whereas electrodes C3’’ and C4’’ were used to assess premovement power changes at postcentral
sites. The movement related ERD as far as upper-limb movements are concerned (Pfurtscheller et
al., 1999) is reported to be most prominent at these positions.

Stimulus related ERD
Single sample t-tests were carried out to test whether the power change at Oz and Czd in the last
interval preceding stimulus presentation -or in case of the VM condition the corresponding
interval- differed from 0, indicating an ERD or ERS.
Furthermore, an ANOVA with Experimental Condition (VM, SS, Vis), Response Side (Left
hand, Right hand) Band (10 Hz, 20 Hz) and Electrode Position (Postcentral, Occipital) was
performed at the last interval preceding the presentation. This was done in order to answer the


43

main research question of the present study: does the pattern of power changes indicating
anticipatory attention differ between modalities? For this analysis, electrode Czd was used to
assess the postcentral powerchanges and Oz to assess the occipital powerchanges. In theory, these
positions should represent the sites of maximal ERD preceding stimulus presentation in the
somatosensory and visual modality, respectively.
For all ANOVA’s that were performed, degrees of freedom were corrected using the GreenhouseGeisser Epsilon (GGE, Vasey and Thayer, 1987) when necessary. Significant interactions were
clarified by breaking them down into simple effects.
In addition to the ANOVA a Signtest was carried out. Instead of addressing the question whether
the mean power change over all trials in the last interval prior to stimulus onset significantly
deviated from 0, the Signtest focused on the number of trials (within each subject) belonging to
one specific condition (for instance: right hand, SS KR, 8-12 Hz band, Czd) in which power
changes could be denoted as either being above baseline level (ERS) or below baseline level
(ERD) for every subject. The Signtest is was used at the single subject level. A possible
advantage of this approach is that deviational large power changes do not influence the outcome
of the test since only the number of trials in which a dichotomous power change occurs, is taken
into account and not the mean. The main goal of the current Signtest is to assess whether the
number trials exhibiting an ERD (or ERS) per subject is significant when compared to chance
probability (p = 0.5). The number of power changes at both electrode positions prior to the
stimulus presentation was incorporated in the signtest so that double dissociations, if any, could
be displayed.
In order to check whether the behavioral data coincided with the physiological data, bivariate
correlations between several behavioral measures and the levels of power change were computed.
Two distinct approaches with respect to trial selection have led to two sets of behavioral data. The
first method - denoted by (1)- simply incorporates all trials irrespective of the constraints imposed
on the behavioral (i.e. within the range of 3500-4500 ms) and physiological (artifacts) trial
selection. Thus, if a subject needs 100 trials to complete a block (consisting of 80 behaviorally
valid trials) of which several trials will be eliminated due to artifacts, the total number of trials in
the behavioral data set remains 100. The second method (2), on the other hand, does take the
behavioral and physiological constraints into account and thus results in a restricted and less
numerous behavioral dataset. This set thus, consists only of trials that are incorporated in the


physiological dataset. Note that the measure “correctly adjusted (2)” cannot be computed due to
the incontinuities in the resulting dataset. The following behavioral measures of interest were
encompassed in this correlational analysis:

•

the percentage of correctly adjusted trials (see the section on behavioral data)

•

the percentage of incorrect trials (1)

•

the percentage of incorrect trials (2)

•

the percentage of TE trials (1)

•

the percentage of TL trials (1)

•

the mean response time (1) (estimated interval)

•

the mean response time (2) (estimated interval)

•

the standard deviation (1)

•

the standard deviation (2)

If not reported otherwise an alpha level of .05 was used for all statistical tests.

44


45

Results
Behavioral data

The quality of the time estimation did not differ significantly between KR modalities and
Response categories as indicated by the ANOVA on the percentage Too short/OK/Too long.
The ANOVA revealed one significant effect: subjects produced more correct (OK) time
estimation intervals (54%) than too short or too late time estimation intervals (22% and 24%
respectively; main effect of Response category: F2, 16 = 41.14, p<0.0001, GGE = 0.9711). No
statistically significant differences between response side and KR modality were found.
The effectiveness of the KR stimulus, did not differ significantly between KR modalities as
indicated by the ANOVA on the percentage correctly adjusted trials. The ANOVA revealed one
significant effect: the mean percentage of correctly adjusted trials was higher for left hand than
for right hand responses (89.1% and 86.2% respectively, main effect of Response side: F(1, 8) =
15.19, p = 0.0046).
Both ANOVAs indicate that the modality in which the KR stimulus was presented did not affect
the subjects’ performance on the time estimation task. Hence, these behavioral data imply that
both stimulus categories are equally capable of guiding future responses in a time estimation
paradigm under the described experimental conditions.

Physiological data
Premovement data
Figure 6 displays the power changes in the 10 Hz frequency band at the last interval preceding
movement. These plots show an ERD prior to movement execution at central electrode positions.
The pattern of power changes at the last interval preceding movement differs between conditions
as indicated by the ANOVA on the last interval preceding movement onset. The results of the
ANOVA on the last interval preceding movement are summarized in table 1. Note that the power


46

changes do not only seem to differ at the last interval preceding movement but also during and
following movement, as can be seen in appendix C.

Figure 6.

Power changes in the 10 Hz frequency band at the last interval preceding
movement.

The power changes at pre- and postcentral electrode positions differ between response sides and
that this difference depends on Experimental Condition as well. In both the VM and SS condition,
left hand responses are accompanied by a preponderant ERD at precentral sites whereas left hand
responses in the Visual condition are accompanied by a preponderant ERD at postcentral
electrodes. Right hand responses show no differences between pre- and post-central sites for the
VM and Vis condition, whereas a right hand response in the SS condition is accompanied by a
preponderant ERD at postcentral electrodes. Thus, condition (VM, Vis, SS) affects the interaction
between responseside and the power changes at pre- and postcentral positions. This is indicated
by the Electrode Position * Response Side * Experimental Condition interaction of the ANOVA.
Note that none of the simple effects (Electrode Position * Response Side in Experimental
Condition) are significant.


47

Condition does not only affect the power changes at pre- and postcentral sites, it also affects the
lateralization of the power changes, as indicated by the Experimental Condition * Electrode
Position * Hemisphere interaction. In both the Visual and the VM condition the ERD at
precentral sites is preponderant in the left hemisphere, whereas the ERD at precentral sites in the
SS condition is preponderant in the right hemisphere. In both the SS and VM condition the ERD
at postcentral sites is preponderant in the left hemisphere whereas the ERD at postcentral sites is
preponderant in the right hemisphere. In the VM movement condition the described interaction
between electrode position and hemisphere are statistically significant (Simple effect of Electrode
Position * Hemisphere in VM: F(1, 8) = 5.57; p = 0.046). Simple effects of Electrode Position *
Hemisphere in SS and Electrode Position * Hemisphere in Vis were not significant.
These findings indicate that subtracting the ERD of the Voluntary Movement condition from the
ERD in the time estimation conditions is unjustifiable.
In addition, the location of the ERD in the 10 Hz band seems to differ from the location of the
ERD in the 20 Hz band, as indicated by the nearly significant Band * Electrode Position
interaction. None of the simple effects of Band in Electrode Position were significant. The
location of power changes does not only seem to differ between frequency bands but this
difference is also affected by the factor Hemisphere, as indicated by the Band * Electrode
Position * Hemisphere interaction. However, note that this interaction is only marginally
significant (F(1, 8) = 4.85, p = 0.0587). In the right hemisphere, the location of preponderant
ERD is precentral for the 10 Hz band and postcentral for the 20 Hz band (simple effect of Band *
Electrode Position in Hemisphere: F(1, 8) = 6.58; p = 0.0334).

Effect

Df

Band * Electrode position

1, 8

Condition * Hemisphere

2, 16

Band * Electrode position * Hemisphere

1, 8

Condition * Electrode positon * Hemisphere

2, 16

Condition * Response side * Electrode position

2, 16

Є

Table 1

F

p

5.31

0.0502

4.40

0.0403

4.85

0.0587

0.87996

8.46

0.0048

0.89660

3.98

0.0458

0.82263

Statistically significant effects of the ANOVA on the last 250 ms interval
preceding movement. (Note that table 1 includes marginally significant effects as
well).


48

Prestimulus data
Figure 7 displays the power changes in the 10 Hz frequency band at the last interval preceding
stimulus presentation.

Figure 7.

Power changes in the 10 Hz band at the last interval preceding stimulus
presentation for the three experimental conditions.

Visual inspection of figure 7 reveals that an ERD (statistically significant, see table 2) can be
observed at electrode position Oz preceding visual KR stimuli. Furthermore, prior to
somatosensory KR stimuli, a (statistically nonsignificant) ERD can be found at electrode position
Czd. However, the Voluntary Movement condition shows a (statistically nonsignificant) ERD at
Czd following left hand movements. Following right hand movements, an (statistically
nonsignificant) ERS can be found in the Voluntary Movement condition. Note the absence of an
ERD at electrode position Oz in the somatosensory condition and the absence of an ERD at
electrode position Czd in the visual condition (right hand). Table 2 presents the results of the ttests at the interval corresponding to 250-0 ms preceding stimulus presentation for both response
sides.


49

Power change in 8-12 Hz band
Response side

Left Hand

Right Hand

Condition

VM

SS

VIS

VM

SS

VIS

Czd

-1.6

-3.5

-5.0

5.5

-9.2

14.8

Oz

5.9

9.8

-37.0

32.7

0.3

-23.3

p<0.01

Table 2.

Results of the t-test at the interval corresponding to 250-0 ms preceding stimulus
presentation for both response sides. Reported values are percentages of power
change. An ERD is denoted by a negative value.

Figure 7 shows the topographic maps of the three last intervals preceding stimulus presentation
when averaged over response side in order to gain insight in the temporal evolution of the power
changes. The statistically significant ERD (see table 3) at Oz prior to the presentation of visual
stimuli remains present.


Figure 8.

50

Power changes in the 10 Hz band at the last three intervals preceding stimulus
presentation when averaged over response side.

Power change in 8-12 Hz band
Condition

VM

SS

VIS

Czd

1.9

-6.4

4.9

Oz

19.3

5.0

-30.2
p<0.05

Table 3.

Results of the t-test at the interval corresponding to 250-0 ms preceding stimulus
presentation when averaged over response side. Reported values are percentages
of power change. An ERD is denoted by a negative value.


51

The time courses of the power changes at both electrode positions (Czd, Oz) for the three distinct
conditions are shown in figures 9-14.
8-12 Hz power changes in VM condition
40

30

20

percentage power change
-2
75
0
m
s

10

+

30
00

m

s

R
s:
K
m
20
00

VM Czd
VM Oz

+

R

-10

es
po
ns
e

0

-20

-30

-40

-50

-60

8-12 Hz power changes in VIS condition
20

10

-20

-30

-40

-50

-60

s
+

30
00

m

R
s:
K
m
20
00
+

-10

Re
sp
on
se

-2

75
0

m

s

0

VIS Czd
VIS Oz


8-12 Hz power changes in SS condition
20

10

+

30
00

m

s

R
s:
K
m
20
00

es
po
ns
e
R

-10

+


-2
75
0

m

s

0

SS Czd

-20

SS Oz

-30

-40

-50

-60

8-12 Hz power changes at Czd
30

20

10

-20

-30

-40

-50

-60

+

30
00

m

s

R
s:
K
m

es
po
ns
e

20
00
+

-10

R

-2
75
0
m
s

0

Czd SS
Czd VIS
Czd VM

52


53

8-12 Hz power changes at Oz
40

30

20

-2
75
0
m
s

10

s
m
30
00
+

m

s:
K

R

Oz SS
Oz VIS
Oz VM

+

-10

20
00

R

es
po
ns
e

0

-20

-30

-40

-50

Figures 9-14.

Time courses for the 10 Hz band in the three experimental conditions. See the
respective legends on the right of the figure.

Taken together, the single sample t-tests, the topographic maps and the time courses seem to
indicate that the distribution of power changes at both Czd and Oz differs between conditions.
However, the ANOVA at the interval corresponding to 250-0 ms preceding stimulus presentation
does not reveal any differences of the kind (see table 4).
The ANOVA revealed only one marginally significant interaction. This Band * Electrode position
interaction indicates that the ERD in the two frequency bands (10 Hz, 20 Hz) is maximal at
different electrode positions. The ERD at Czd is larger in the 10 Hz band than in the 20 Hz band
as indicated by the simple effect of Band at Czd: F(1, 8) = 17.20; p = 0.0001. The simple effect
of Band at Oz was not significant.

Does Event Related Desynchronization reveal anticipatory attention in the somatosensory modality?

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