The functional significance of mu rhythms: Translating ‘‘seeing’’
and ‘‘hearing’’ into ‘‘doing’’
Jaime A. Pineda*
Department of Cognitive Science and Neuroscience, University of California, San Diego, La Jolla, CA 92037-0515, USA
Accepted 21 April 2005
Available online 31 May 2005
Existing evidence indicates that mu and other alpha-like rhythms are independent phenomena because of differences in source generation,
sensitivity to sensory events, bilateral coherence, frequency, and power. Although mu suppression and enhancement echo sensorimotor
processing in frontoparietal networks, they are also sensitive to cognitive and affective influences and likely reflect more than an idling brain
state. Mu rhythms are present at early stages of human development and in other mammalian species. They exhibit adaptive and dynamically
changing properties, including frequency acceleration and posterior-to-anterior shifts in focus. Furthermore, individuals can learn to control
mu rhythms volitionally in a very short period of time. This raises questions about the mu rhythm’s open neural architecture and ability to
respond to cognitive, affective, and motor imagery, implying an even greater developmental and functional role than has previously been
ascribed to it. Recent studies have suggested that mu rhythms reflect downstream modulation of motor cortex by prefrontal mirror neurons,
i.e., cells that may play a critical role in imitation learning and the ability to understand the actions of others. It is proposed that mu rhythms
represent an important information processing function that links perception and action — specifically, the transformation of ‘‘seeing’’ and
‘‘hearing’’ into ‘‘doing.’’ In a broader context, this transformation function results from an entrainment/gating mechanism in which multiple
alpha networks (visual-, auditory-, and somatosensory-centered domains), typically producing rhythmic oscillations in a locally independent
manner, become coupled and entrained. A global or Fdiffuse and distributed alpha system_ comes into existence when these independent
sources of alpha become coherently engaged in transforming perception to action.
D 2005 Elsevier B.V. All rights reserved.
Theme: Neural basis of behavior
Topic: Biological rhythms
Keywords: EEG; Rolandic; Synchronization; Desynchronization; ERD; ERS; Mirror neuron; Alpha entrainment; Coupling; Gating
1.Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1. Are mu rhythms epiphenomena without significance?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.Goals and organization of this review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Phenomenology of the mu rhythm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. More than an idling state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.Mu synchronization and desynchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.Unique rhythms with independent sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.Topography of mu rhythms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0165-0173/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
* Fax: +1 858 534 1128.
E-mail address: email@example.com.
Brain Research Reviews 50 (2005) 57 – 68
3. Other supporting evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1. Developmental and clinical studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2. Animal studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.Brain computer interface: volitional control of mu rhythms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4. Mu rhythms and mirror neurons: a link between perception and action . . . . . . . . . . . . . . . . . . . . . . . . .
Mu rhythms and perception-to-action processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.Translating ‘‘seeing’’ and/or ‘‘hearing’’ into ‘‘doing’’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1. Are mu rhythms epiphenomena without significance?
Ever since their discovery by Hans Berger , electro-
physiological rhythms have held the promise of being key to
the understanding of the brain–mind relationship .
Scalp-recorded electroencephalographic (EEG) and magne-
toencephalographic (MEG) activity, as well as more recent
neuroimaging technologies, have been important for under-
standing the functional significance of these rhythms and the
way they are generated by the underlying neural machinery.
Indeed, recent work has began to shed light on the macro-,
micro-, and functional architecture of EEG rhythms, from
slow theta  to fast gamma frequencies [81,100].
The mu rhythm (also known as the central, Rolandic,
sensorimotor, wicket, or arceau rhythm), first reported in the
1950s [20,43,44], has not received the kind of attention that
other EEG oscillations have, most likely because until
recently  it was thought to occur infrequently and only
in a small percentage of the population . However, new
and more sophisticated techniques, such as independent
component analysis (ICA), have shown that mu rhythms are
found with scalp EEG in most, if not all, healthy adults .
A significant amount of work on the mu rhythm has been
conducted since the 1950s. Unfortunately, most of this work
has occurred in academic fields that, until recently, have not
overlapped significantly, resulting in slow progress towards
the delineation of the antecedent conditions and the neural
basis of this rhythm.
Are mu rhythms merely epiphenomena without functional–
behavioral significance? This question has been asked
repeatedly over the years [16,82,94]. A number of different
hypotheses have been put forth in response, and although
most of them link mu rhythms to sensorimotor processes, a
comprehensive and satisfactory explanation is still not
available. To arrive at such an explanation, it is critically
important to understand the neural basis of this rhythm, if
only to constrain functional speculations. Hence, one goal of
mu research must be to demonstrate that any proposed
functional role is affected by the manipulation of the
neuronal populations assumed to generate it. Approximately
50 years since it was first reported, we have a variety of
insights regarding the mu rhythm but little understanding on
how those individual pieces fit together into a rational whole.
Conceptual gaps remain in our understanding of the
relationship between mu rhythms, neuronal activity, and
information processing, and there have only been a few
systematic efforts to integrate the body of results into a
coherent theoretical framework [7,16,94,102].
1.2. Goals and organization of this review
This review aims to address some of these conceptual
gaps and integrate existing knowledge regarding the
phenomenology and neural basis of the mu rhythm in order
to elucidate its functional role. More specifically,it presents a
synopsis of the evidence suggesting that mu rhythms: (1) are
more than an idling state of sensorimotor cortex and instead
reflect dynamic and integrative sensory, motor, and mne-
monic processes; (2) reflect activity in sensori-, audio-, and
visuomotor mirror neurons in frontal and parietal networks;
and (3) represent an important information processing
function, which is to link perception and action, or more
specifically the transformation of ‘‘seeing’’ and ‘‘hearing’’
into ‘‘doing.’’ These proposed functions are consistent with
previous empirical and theoretical work on mu rhythms in
animals, infants, clinical populations, normal adults, and
even the developing brain computer interface field.
The organization of the review will proceed according to
the following format: in Section 2, the phenomenology of the
mu rhythm, a summary of what is known about its
antecedent conditions, and a review of work addressing the
neural mechanisms involved in mu generation are described.
In Section 3, some supporting evidence from different
domains that bear on the functional significance of mu
rhythms are reviewed. This section also includes a review of
the mirror neuron system, some of its basic properties, and its
relationship to mu rhythms. Finally, in Section 4, the concept
of the mu rhythm as a perception-to-action process is
2. Phenomenology of the mu rhythm
2.1. More than an idling state
The mu rhythm is an EEG oscillation with dominant
frequencies in the 8–13 Hz and 15–25 Hz bands .
These oscillations are limited to brief periods of 0.5 to 2 s
duration  and can be recorded over human sensorimotor
cortex in the absence of movement. Because it appears to
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
occur in the absence of processing sensory information or
motor output, i.e., ‘‘an area that has nothing to do’’ , the
mu rhythm was originally conceived to reflect a cortical
‘‘idling’’ or ‘‘nil-work’’ state, similar to the classical alpha
rhythm [18,89,109]. Indeed, Pfurtscheller and colleagues
 have argued that ‘‘one example of such an idling
cortical area is the enhancement of mu rhythms in the
primary hand area during visual processing or during foot
movement. In both circumstances, the neurons in the hand
area are not needed for visual processing or preparation for
foot movement. As a result of this, an enhanced hand area
mu rhythm can be observed. Another characterization was
given earlier by Kuhlman  who attempted to isolate
variables mediating mu rhythm enhancement. Because no
single variable that produced consistent enhancements was
found, it was concluded that ‘‘...mu rhythm had no striking
psychological correlates and was present as part of the
normal waking state’’.
Idling states of the brain, as well as simple explanations
concerning their functional significance, are giving way to
more complex views reflective of diverse brain functions
that comprise sensory, motor, cognitive, mnemonic, and
integrative processes [7,9,101]. Today, there is increased
interest in the association between EEG rhythms and
cognitive processes [8,61,63,69,71], with many studies
reporting short- and long-lasting suppressions of ongoing
alpha-like rhythms with cognitive function [11,62,65,
71,104,105,125]. It has been shown, for example, that
finger movements in elderly subjects are accompanied by a
more widespread alpha-like suppression over motor and
premotor areas compared with young subjects , pre-
sumably because older subjects put more effort into this
simple task. Paradoxically, increased attentional demands,
such as attentive expectation of relevant stimulus omission,
working memory activation, and episodic short-term mem-
ory tasks, also produce enhancements of alpha-like rhythms
[6,67,74,90,116,129]. Recently, a causal connection be-
tween alpha-like rhythms and mental rotation has been
suggested based on studies using repetitive transcranial
magnetic stimulation (rTMS) in which performance on the
task was enhanced following alpha-like frequency stimula-
tion . While these reports argue for a relationship
between brain rhythms and higher cognitive functions, the
precise relationship is not yet clearly established.
2.2. Mu synchronization and desynchronization
It is generally accepted that EEG desynchronization
resulting from thalamocortical stimulation is a reliable
correlate of excited neural networks or activated cortical
areas [49,133], while EEG synchronization is a correlate of
deactivated cortex . Both the occipital alpha blocking
seen with eyes open as well as frontal mu blocking seen
during movement preparation and execution are examples of
desynchronized activity. The degree of mu blocking,
reactivity, or suppression occurring during movement can
be expressed as a percentage of the peak power value at rest
and typically shows an average decrease of about 61% (SD =
According to Nunez and Silberstein , frequency-
specific electrical scalp activity and hemodynamic activity
can change in opposite directions. This inverse relationship
is believed to depend upon both the spatial scale and the
frequency band of cortical synchrony. During the type of
synchrony that is reflected in alpha-like rhythms, activity in
a small fraction of neurons within a cortical column may be
sufficient to give rise to a strong EEG signal, while the
inactive majority maintains overall metabolism low and thus
the blood oxygen level dependent (BOLD) effect small .
This suggests that widespread thalamocortical synchroniza-
tion is associated with decreased brain metabolism .
Recent functional magnetic resonance imaging (fMRI)
studies are also consistent with this negative correlation,
showing that, whenever alpha-like oscillations increase,
presumably due to increased synchronous activity, inferior
frontal, superior temporal, parietal, occipital, and cingulate
cortex show a decrease in BOLD signal . In contrast, a
positive correlation between alpha rhythm and BOLD signal
occurs in some areas, including the thalamus and insula
. In PET studies, similar negative correlations between
alpha-like power and glucose metabolism have been
reported , with positive correlations occurring in the
pons, midbrain, thalamus, hypothalamus, amygdala, basal
prefrontal cortex, insula, and right dorsal premotor cortex
Changes in EEG rhythmic activity not only happen
spontaneously but can also be induced by external events.
These event-related oscillations bridge the gap between
single neuron activity and activity in neuronal assemblies. In
particular, stimulus-dependent 10 Hz oscillations in the
scalp EEG and at the cellular level (evoked by auditory and
visual stimuli) share common features. These can be
quantified in time and space relative to a predefined
reference and hence can be related to specific tasks.
Experimental data support the idea that 10 Hz event-related
desynchronizations (ERDs) are a correlate of activated
cortical networks related to information processing
[29,30,63,66,65,124], selective attention [28,137,143], and
motor preparation [24,78,110,111,139].
Recordings in normal human adults show that mu ERD
starts 2 s before movement onset and that the size and
magnitude of the ERD effect reflect the size of the neural
network involved in the performance of the task. This is
supported by the observation that increasing task complex-
ity increases ERD [11,28,30,143] presumably because a
larger group of cells is involved. In general, the two main
factors that have been reported to affect the occurrence or
maintenance of mu rhythms are motor inactivity and the
level and quality of attention. The induction of actual
movement is a reliable blocking mechanism of pre-existing
mu rhythms, but attention alone (most likely on the motor
imagery itself) is also able to provoke the same mu
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
suppression response . Interestingly, it has been shown
that hand dominance, handedness, and type of movement
influence the proportion of pre-movement mu rhythm
desynchronization in both the left and right peri-Rolandic
Since ERD reflects activation of cortical tissue, event-
related synchronization (ERS) has been assumed to reflect
deactivation, inhibition, or at least the natural ‘‘idling’’ state
of the network [93,109]. Koshino and Niedermeyer 
found synchronized mu rhythms during pattern vision, and
Pfurtscheller and Klimesch  reported larger and more
synchronized mu rhythms during reading. The interpretation
has been that, since mu rhythms reflect sensorimotor
processing and are not directly involved in visual proces-
sing, the network becomes ‘‘idle’’ or ‘‘nil-working’’ and
synchronized during such tasks. However, the existence of
visuomotor Fmirror_ neurons in prefrontal cortex (see
Section 4), which may provide downstream regulation of
sensorimotor neurons, argues for a different interpretation.
That is, it is theoretically possible that ERS results from
functionally active but increasingly synchronized sensori-
2.3. Unique rhythms with independent sources
Alpha-like oscillations, like the mu rhythm, are recorded
in nervous systems of different complexities, from the
human brain to isolated ganglia of invertebrates. The
amplitude of these oscillations is generally inversely
proportional to the frequency (higher amplitudes for slower
oscillations) and to the number of synchronously active
neural elements . These rhythms are believed to arise
from the oscillation of postsynaptic potentials in the cortex
[10,97] and they are seen in surface EEG if neurons are
synchronously active across several centimeters of cortex
Mu rhythms are typically identified as an 8–13 Hz
oscillation (although see ), maximal over sensorimotor
cortex when the individual is at rest. It is attenuated by
voluntary movement or somatosensory stimulation, but is
minimally affected by visual stimulation. Because mu
rhythm frequencies overlap those of the occipital or classical
alpha rhythm (¨10 Hz), it is sometimes difficult to separate
them and more often than not the two have been confused
and misidentified. However, mu rhythms show a more
anterior focus compared to the more posterior one for the
classical alpha oscillations. In general, mu rhythms reflect
sensorimotor processing in frontoparietal networks, while
classical alpha reflects primarily visual processing in
occipital networks. Furthermore, classical alpha is desyn-
chronized and attenuated when eyes are open. This is also
true of a recently reported ‘‘third rhythm,’’ an alpha-like
oscillation that is independent of classical alpha and shown
to be present in approximately 20% of subjects . By
contrast, mu rhythms are not affected by opening or closing
of the eyes, but rather are desynchronized and attenuated by
actual movement or the motor imagery of movement
Other marked differences between mu and classical alpha
rhythms have been reported in terms of hemispheric source
localization and responsiveness to stimulus events. The
frequency of mu rhythms has been reported to be
significantly higher (mean = 10.5 Hz, SD = 0.9) than that
of the classical alpha rhythm (mean = 9.6, SD = 0.7) .
This has led some researchers to refer to them as high and
low alpha, respectively. However, other reports show
opposite numbers , suggesting that differentiating these
rhythms on the basis of mean frequencies is misleading and
may actually require differentiation by spatial or functional
criteria. Nonetheless, some differences appear real. Mu
rhythms, for example, seem to be asymmetrical in frequency
and power and, unlike classical alpha rhythms, do not
exhibit bilateral coherence . It is also the case that
amplitude or power in the classical alpha rhythm tends to be
larger than in mu rhythms .
Using high-density magnetoencephalography (MEG),
source localization techniques, and independent component
analysis (ICA), it has been possible to demonstrate distinct
cortical sources (equivalent dipoles) of alpha-like activity
[83,84,147]. Furthermore, clinical studies showing asym-
metric reactivity of alpha-like activity to eye opening also
suggest independent hemispheric generators . Indeed,
a number of recent studies [54,94] have identified at least
three distinct physiological alpha-like rhythms with inde-
pendent sources in the two hemispheres: (a) the classical
occipital alpha with parietooccipital and calcarine sulci
sources; (b) the frontal mu rhythm with sources in the
Rolandic fissure; and (c) a midtemporal ‘‘third rhythm’’ that
is typically not recorded on the scalp.
Investigators have reported that the human mu and the
occipital alpha rhythm both contribute to scalp-recorded
EEG in central regions of the brain, with the occipital alpha
thought to be volume-conducted into those areas. However,
in a study using closely spaced Laplacian derivations, it was
possible to rule out volume-conduction effects, providing
evidence that both rhythms are, in fact, independently
generated by the underlying neocortical circuitry .
Whole-head MEG recordings assessing the distribution of
the estimated sources of alpha and mu rhythms produced
similar conclusions. The results indicate that alpha and mu
rhythms have distinct spatial distributions of their equivalent
dipolar sources .
In a study to determine whether different subtypes of mu
rhythms exist, ERD was quantified in the 8–10 and 10–
12 Hz bands during self-paced, voluntary finger, and foot
movements . The data indicate that the lower
frequency component reflects a widespread movement-type
non-specific ERD, whereas the upper frequency component
shows a more focused and movement-type specific pattern,
clearly different with finger and foot movement. The
different patterns provide evidence for the existence of at
least two different subtypes of mu rhythms, a somatotopi-
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
cally non-specific lower frequency mu and a somatotopi-
cally specific mu characteristically found in the upper alpha
frequency band .
The relationship between the 8–13 Hz mu rhythm and
other EEG rhythms, such as central beta (20–24 Hz) or the
15–25 Hz rhythm reported by Hari and colleagues , is
also unclear. On the one hand, the most reactive beta-like
components (¨20 Hz) may be a first harmonic of the mu
rhythm. But, the source for these two oscillations seems to
be different [108,123], suggesting distinct component
frequencies. During a finger movement task, 20-Hz ERD
had a focus slightly more anterior than the 10-Hz ERD
[108,123]. Furthermore, the time course of recovery
following desynchronization differed for these two rhythms,
with faster ones recovering more quickly . These data
suggest that mu rhythm is not a unitary phenomenon but an
aggregation of different processes involved in the transfor-
mation of ‘‘seeing’’ and ‘‘hearing’’ into ‘‘doing,’’ with
distinct aspects of that process reflected in these different
frequencies (i.e., 8–10, 10–12, 15–25 Hz).
Furthermore, all these differences in source localization,
bilateral coherence, frequency, power, and functional
responsiveness suggest, not only independent sources for
mu and classical alpha rhythms, but also independent mu
sources in the two hemispheres .
2.4. Topography of mu rhythms
Studies have also shown that a correspondence exists
between the body area movement which blocks the mu at a
given site and the body region that is affected by stimulation
at the same site . This suggests that sensorimotor cortex
displays a variety of mu rhythms with specific topographic
and functional properties rather than one uniform rhythm.
Indeed, areas involved in hand, foot, and face movements,
which have larger cortical representations in the human
brain and an increased number of internal connections but
few connections between areas  appear to produce
distinct mu rhythms [106,110].
Other studies have shown that early ERD, presumably
indicative of motor preparation, is located over the
contralateral central region covering primary motor cortex.
It is then followed by a bilateral suppression occurring over
ipsilateral and contralateral central regions during the actual
performance of the movement. These results indicate that
programming of voluntary movement induces early activa-
tion in contralateral sensorimotor areas, while performance
of the movement induces bilateral activation in sensorimotor
While the major focus of studies examining the neural
basis of mu rhythms has been in terms of their role in motor
preparation and execution, some studies point to a role
beyond simple motor control. For example, all the
characteristics of the waking mu rhythm, including being
blocked by contralateral body movement or contralateral
somatosensory stimuli, are present during REM sleep—a
time when motor movement is essentially inhibited .
This suggests a larger functional role for mu rhythms in
states of wakefulness and REM sleep.
In summary, the extant evidence is compelling that mu
rhythms are only superficially similar to other alpha-like
rhythms in cortex. Differences in source generation,
sensitivity to sensory events, bilateral coherence, frequency,
and power suggest that mu and classical alpha rhythms are
independent phenomena. Mu rhythms are phasic electro-
physiological signals spontaneously induced by internal
events or evoked by external stimuli. Functionally, they
primarily reflect sensorimotor processing in frontoparietal
networks, while classical alpha reflects mainly visual
processing in occipital networks. All these argue that mu
rhythms are more than an idling state of the brain. Its
suppressions and enhancements seem to largely reflect
motor processing, but the system is very sensitive to
cognitive and affective influences.
3. Other supporting evidence
3.1. Developmental and clinical studies
Stroganova et al.  showed that the alpha-like rhythm
is blocked by visual stimulation even in 7- to 12-month-old
infants, suggesting a functional similarity between infant
and adult rhythms. They found a pronounced increase in 6–
8 Hz rhythmic activity at parietooccipital sites under a
condition of total darkness, compared with a condition of
quiet attention. Several other studies [51,52,94] have
reported on the appearance of a 4–7 Hz rhythm at central
sites at around 4 months of age. Hagne labeled this the
Fcentral alpha_ and noted that the mean frequency of this
rhythm remained around 7 Hz over the first year of life,
increasing to 8 Hz by 18 months then to 9 Hz by 4 years,
and stabilized at around 10 Hz in mid-adolescence. Most
researchers examining the developmental data speculate that
the progression of this central rhythm in infants is associated
with the development of motor and locomotor skills.
In more recent work, it has been argued that the central
rhythm of infants and young children and the sensorimotor
mu rhythm in adults have a functional relationship
[39,40,85,136]. Observations of 20-month-old infants show
a similar slow equivalent of the mu rhythm in the 6–7 Hz
range . As individuals get older, alpha-like responses
increase in frequency and show stronger phase locking and
an increasing locus over frontal brain regions. These results
demonstrate a trend towards frequency acceleration and a
posterior-to-anterior shift in focus for both the spontaneous
and evoked alpha-like activity with increasing age .
Clinical studies, in contrast, report a deceleration or
slowing of alpha-like rhythms with pathology . Schizo-
phrenic patients, whose EEG is characterized by diminished
alpha-like power, exhibit more negative symptoms than
those patients who do not show diminished power in the
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
alpha band . Other clinical studies have reported
distinct populations of subjects with characteristically
different mu rhythms. In one study, over 5000 patients
who received an EEG epileptic assessment were divided
into two groups: Group 1 showing typical mu rhythms, i.e.,
recorded during wakefulness and not affected by visual
stimulation but blocked by voluntary movements or tactile
stimuli; and Group 2, subjects showing atypical mu
rhythms, i.e., accentuated or activated by drowsiness, photic
stimuli, or hyperventilation. No difference between the two
groups was found with regard to frequency, amplitude, or
origin of the mu rhythm. However, although both groups
showed a high incidence of epilepsy, Group 2 showed
higher incidence of intractable epilepsy, as well as of severe
intracranial trauma and of organic brain disease .
Clearly, mu and alpha-like rhythms are present at the
early stages of human development, suggesting an important
functional role. While most current speculation centers on
the relationship with developing motor and locomotor skills,
our hypothesis would argue for a role in translating
‘‘seeing’’ and ‘‘hearing’’ into ‘‘doing,’’ a necessary compo-
nent for imitation learning. Hence, it is assumed that mu
rhythms play an even greater developmental role than has
previously been ascribed. Furthermore, the adaptive and
dynamically changing properties of alpha-like rhythms,
including frequency acceleration and posterior-to-anterior
shifts in focus, underscore a plasticity that is not necessarily
lost in adulthood and that may explain the dynamical
changes brought about by pathological states.
3.2. Animal studies
Animal experiments in awake cats and monkeys have
reported sensorimotor rhythms (SMR) in the 12–16 Hz
band. These rhythms are blocked, suppressed, or desyn-
chronized with body movement [18,56,134] when the
animal is immobile and/or receiving a reinforcement. Like
the human mu rhythm, the SMR develops when the animal
is motionless and attentive, i.e., in a state of Fquiet waking_
. SMR is distinct from other sensorimotor rhythms,
with different cortical distributions and different frequencies
and appearing under different behavioral and environmental
conditions. For example, although similar SMR rhythms are
recorded from the primary visual and somatosensory cortex
of the cat in the same overall behavioral situations (quiet
waking and/or expectancy of an event to occur), these
rhythms are independent . The cortical mu focus in the
cat is centered within a restricted part of area SI, in the
forepaw and wrist projection field. The related thalamic
focus is localized in a small zone of the VP nucleus that also
corresponds to the hand and wrist projection area .
Based on the reported topographical, morphological, and
functional similarities between feline SMR and the human
mu rhythm, the feline SMR has been described as the first
demonstrable model of a spontaneous human EEG rhythm
Although few in number, animal studies of mu and
alpha-like rhythms show a remarkable similarity to the
analogous human rhythms. From an evolutionary perspec-
tive, this gives weight to the growing importance of such a
mechanism in more than simply cortical ‘‘idling.’’ Animal
models provide a unique opportunity to understand the
functional significance of brain rhythms, and there is need
for more of this work, especially using non-human primates.
In a review of the animal (primarily cat) literature examining
the functional role of thalamocortical oscillations, Buser and
Rougeul-Buser  concluded that SMR-type oscillations
have at least two distinct and opposite roles: to lower the
level of awareness, such as in slow wave sleep, when the
oscillations are globally distributed throughout the cortex; or
to contribute to perceptual processing, when these rhythms
are of more local origin and possibly of a higher frequency.
This local versus global role is an important contextual
backdrop for the perception-to-action role that is being
ascribed to the human mu rhythms.
3.3. Brain computer interface: volitional control of mu
Studies show that humans can learn to volitionally
control the amplitude of the 8–13 Hz mu rhythm
[75,128], as well as central beta (20–24 Hz). Whether this
control reflects direct access to the neural mechanisms
generating these rhythms or indirect modulation of some
general state of the brain is not clear. In brain computer
interface (BCI) studies, performance feedback is typically
provided by the visual consequences of controlling the mu
rhythm, thus creating a closed-loop neurofeedback system.
Such learning has been mapped to the control of a variety of
applications, from moving a cursor, spelling words, answer-
ing questions, selecting among choices on a computer
screen, to activating a device [87,88,113,148–150]. This
control does not depend on muscle activity per se  and
thus provides an alternative channel of communication and
control for individuals with severe neuromuscular disorders
who are unable to use the standard interfaces, i.e., keyboard
Some studies use feedback involving 2-dimensional
computer representations of mu power in the form of a
box or a cursor moving in an upward or downward motion,
reflecting high or low levels, respectively [86,149]. The
changes in amplitude that provide one- and two-dimensional
control involve desynchronization (mu suppression) or
synchronization (mu enhancement) of the mu rhythm.
Different levels of suppression or enhancement (i.e.,
different levels of mu amplitude or power) can then be
mapped to a variety of computer-controlled functions .
Kuhlman  used a different form of feedback in which a
small white light was illuminated for as long as the subject’s
mu power remained at the desired level. In this study, power
spectral analysis across 50 sessions of EEG feedback
training showed that mu activity could be systematically
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
enhanced, whereas a 12–14 Hz component of low voltage
fast activity could not. Results such as this indicate that
long-term modification of mu rhythms with feedback
training is possible. However, training per se cannot produce
the rhythm . Using a stimulus-rich, realistic, and
motivationally engaging environment, Pineda et al. 
showed that subjects gain control of mu suppression more
rapidly than they do mu enhancement. In that study, changes
in mu power were reflective of hemispheric coupling
(suppression) or uncoupling (enhancement). Overall, sub-
jects learned to control mu rhythms within 3–10 h of
training, which required the acquisition of independent
control of mu generators in the two hemispheres. Guger et
al.  reported a similar type of rapid training.
The key to learning volitional control of mu rhythms
appears to be the gradual build-up of internal associations
with the visual feedback . Since mu rhythms are
desynchronized by self-movements, as well as by the
observation and imagination of movement [92,112], learn-
ing strategies tend to focus on motor imagery. However,
recent evidence indicates a strong cognitive and affective
influence. In a recent study, for example, it was found that
affective inputs regulate mu rhythms by showing that anger
induces changes in the spatial distribution of intracortical
connections in the alpha-like frequency range over frontal
It is remarkable that individuals learn to control mu
rhythm suppressions and enhancements volitionally in a
very short period of time. This raises questions about the
open architecture of neural systems producing mu rhythms,
their ability to respond to cognitive, emotive, and motor
imagery, and their ability to reorganize dynamically.
Whether dynamical restructuring as a consequence of
neurofeedback learning produces improved (or perhaps
decreased) performance in motor performance or more
broadly defined cognitive processes is only now beginning
to be studied.
3.4. Mu rhythms and mirror neurons: a link between
perception and action
Single unit studies in the premotor cortex of macaque
monkeys indicate that cells in area F5, which are indistin-
guishable from neighboring neurons in terms of their motor
properties, also discharge in response to observed actions
 (for a review, see . That is, when a monkey
observes an action that is in its motor repertoire, these
neurons ‘‘mirror’’ the activity and appear to represent the
motor action in their own premotor cortex, revealing a type
of observation/execution matching system. Mirror neuron
activity has also been shown to be selective for goal-
directed, meaningful actions . Recently, a population of
neurons in the ventral premotor cortex of the monkey has
been found that discharge when the animal performs a
specific action, when it sees the same action performed by
another individual, or when it hears sounds associated with
the same action [60,70]. Thus, mirror neurons respond to the
execution of a movement as well as the audiovisual
representations of the same movement — therefore provid-
ing an audiovisual-to-motor link or a transformation
between ‘‘seeing’’ and/or ‘‘hearing’’ and ‘‘doing.’’ Such an
observation/execution matching system is capable of
performing an on-line automatic execution or ‘‘mimicry’’
of the action it perceives or it may perform an off-line
internal simulation of the observed action without concom-
itant motor movements. It has been speculated that such
internal simulation or motor imagery may play a critical role
in imitation learning or the ability to understand other
individuals’ movements, an ability that may be especially
critical for the development of a theory of mind and hence
for understanding social interactions .
Although individual mirror neurons cannot be studied
directly in humans, the existence of an analogous system in
the homologous brain regions (Broca’s area or Brodmann’s
area 44) has been supported by indirect population-level
measures such as TMS  and fMRI . Fadiga and
colleagues  found that motor-evoked potentials over
motor cortex were enhanced in response to TMS when
subjects observed another individual performing an action
relative to when they detected the dimming of a light. They
concluded that this specific enhancement was a result of
activity of mirror neurons in the prefrontal cortex influenc-
ing the motor response. Iacoboni and colleagues 
measured blood flow in Brodmann’s area 44 and found
increases during the observation and performance of
actions. Other recent studies have reported activations with
similar properties in the parietal cortex [13,14], as well as
the superior temporal sulcus [58,115]. These data suggest
that the prefrontal mirror neuron system may in fact be part
of a broader network, one responsible for biological motion
The connection between mu rhythms and mirror neuron
activity was first suggested by Altschuler et al.  and
thereafter by other researchers [21,22,55]. More recent
studies echoing this line of work give further support to this
link [91,92,98]. It is a working hypothesis of this review that
mu rhythms reflect the downstream modulation of motor
neurons by cells in the premotor cortex, some of which are
mirror neurons. However, when the transformation from
Fseeing_ and/or Fhearing_ to Fdoing_ occurs, or, to put it
another way, when action observation/comprehension
occurs, then mu rhythms reflect primarily mirror neuron
modulation. Various properties of the mu rhythm link it
directly to mirror neuron activity. At rest, sensorimotor
neurons fire in synchrony , leading to large amplitude
EEG oscillations in the 8–13 Hz (mu) frequency band.
When subjects perform an action, these neurons fire
asynchronously, decreasing the power of the mu-band
oscillations [107,123]. Mu power recorded from electrodes
at scalp locations over sensorimotor cortex is reduced in
normal adults by self-initiated movement, imagined move-
ment, and observed movement [5,21,44,112]. More recent
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
studies have found that the mu rhythm is also modulated by
object-directed actions , similar to fMRI studies of the
mirror neuron system . Since the mu rhythm is
generated by activity in sensorimotor areas and mirror
neurons have been located primarily in premotor areas, it
has been hypothesized that the mu rhythm may specifically
index downstream modulation of primary sensorimotor
areas by mirror neuron activity [91,92].
In addition to its responsiveness to motor-related
imagery, the human mirror system has been implicated in
higher-level cognition. Rizzolatti et al.  have suggested
that the capacity to associate the visual representation of an
observed action with the motor representation of that action
can lead to imitative learning. Oberman and colleagues have
recently reported evidence for a dysfunctional mu rhythm
and by implication a dysfunctional mirror neuron system in
high functioning individuals with autism spectrum disorders
(ASD) . ASD are largely characterized by deficits in
imitation, pragmatic language, theory of mind, and empathy.
Rizolatti and Arbib  suggest that this observation/
execution mechanism is an ideal candidate for a hypothe-
sized evolution of language from an earlier gestural
communication system. Gallese  suggests that once
another individual’s actions are represented and understood
in terms of one’s own actions, it is possible to make
predictions about the mental state of the observed individ-
ual, leading to ‘‘theory of mind’’ capabilities. Lastly, Leslie
et al.  found that facial expressions may activate the
mirror neuron system. Therefore, empathy may critically
depend on one’s ability to understand the observed facial
expression in terms of one’s own motor representations.
In summary, the phenomenology of the mu rhythm
resembles the phenomenology of mirror neuron activity.
Both are sensitive to movement, as well as to motor and
cognitive imagery (i.e., observed meaningful actions). Their
overlapping neural sources in sensorimotor frontoparietal
networks further support the argument that they are related
and involved in linking perception to action, which may be a
critical component in the development of higher level
4. Mu rhythms and perception-to-action processes
4.1. Translating ‘‘seeing’’ and/or ‘‘hearing’’ into ‘‘doing’’
The existence of mirror systems in the human brain
involved in the transformation of perceptual representations
to executable actions, together with new conceptualizations
about alpha and mu rhythms , prompted this elabora-
tion of the mu rhythm–mirror neuron hypothesis. The
functional significance of such rhythms in the EEG was one
of the major problems in cybernetics put forward early on
by Norbert Wiener. Wiener argued that the alpha-like
rhythm reflects ‘‘the mutual entrainment of an ensemble of
non-linear alpha rhythm generators such that the frequencies
of individual members of the ensemble have a tendency to
be pulled synchronously toward a central frequency, and
serving a type of gating function’’ . This gating function
means that the regulation of incoming signals, or the
opening and closing of thalamic gates, occurs with the
rhythm of the alpha waves—an idea supported by early, as
well as more recent work [45,82]. More recently, it has been
suggested that alpha rhythms result from narrowband
filtration of a broadband process. In such a model, the
synchronicity of rhythmic components is produced by the
synchronicity of the broadband inputs to the narrowband
filters  That is, there is no need for rhythmic afferent
input into such a system [76,151].
The entrainment/gating hypothesis proposed by Wiener,
based on deterministic, non-linear oscillators, is a reason-
able explanation to account for the multiplicity of local
alpha-like rhythms in the context of coherent functions.
However, a stochastic (filtered noise) signal resulting from
multiple narrowband filters could generate electrical oscilla-
tions at their own resonant frequency . Both of these
mutually exclusive models can explain the functional
interdependence within and between brain areas involved
in the generation of alpha-like rhythms. According to our
elaboration of these hypotheses, EEG consists of either
multiple self-oscillators or narrowband filters producing
rhythmic oscillations. These local networks are usually
active in an independent manner. However, sensory input
causes them to become coupled and entrained (or resonant)
and thus to begin acting together coherently. Therefore, our
hypothesis assumes that a global or Fdiffuse and distributed
alpha system_ comes into existence when the independent
sources of alpha in occipital, frontal, and temporal areas are
engaged in some coherent function . In line with
arguments derived from information theory, the number of
alpha centers decreases with lower complexity of the
functional state of the subject , presumably reflecting
the synchronization of local alpha centers into a more global
alpha-like rhythm . Local–global entrainment, then,
accounts for suppression and enhancement of alpha-like
rhythms. Furthermore, transient global coherence can then
organize and instantiate top–down anticipatory processes
that facilitate subsequent sensory processing at the local
level, say, in visual cortex . Global synchronization of
alpha centers may result from neuromodulatory activity
extrinsic to cortex, such as that from the brainstem locus
coeruleus–noradrenergic or mesocortical dopamine sys-
tems, while local intrinsic modulation (e.g., from self-
oscillating inhibitory interneurons) may mediate synchrony
in small cortical populations .
The coupling or resonant state of local alpha networks is
also supported by evidence that fronto-occipital and fronto-
frontal alpha coherences decouple or become weaker from
wakefulness to drowsiness and finally to REM sleep. These
networks seem to maintain a mutual inter-dependency
during wakefulness, whereas, during the transition to human
sleep, the anterior and posterior areas appear to work quite
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
independently . This coupling–decoupling or local–
global coherence is consistent with the idea that synchro-
nized alpha-like rhythms are the result of powerful
inhibitory effects on thalamocortical information transfer
. Because corticocortical connections have indirect
pathways through the thalamus, it provides a neural basis
for instantiating the type of gating function proposed. The
selective interruption, inhibition, or gating of thalamic
activity results in sleep spindles in the range of 7–14 Hz
[109,133]. Indeed, zones of mu desynchronization (ERD)
and synchronization (ERS), like the focal ERD/surround
ERS, likely reflect lateral inhibitory processing resulting
from nucleus reticularis (nRT) feedback onto thalamic relay
The question that remains is, what is the Fcoherent
function_ that involves occipital, frontal, and temporal alpha
sources? It is proposed that, within the context of an
entrainment/gating neural mechanism and the existence of a
system that translates visual (‘‘seeing’’) and auditory
(‘‘hearing’’) representations into action-based representa-
tions (‘‘doing’’) then a vision-centered domain (classical
alpha), an audition-centered domain (third rhythm) and a
sensorimotor-centered (mu) domain are the minimal pre-
requisites for a perception-to-action translation system that
is necessary for understanding the behavior of others.
Global alpha entrainment would then reflect the spatiotem-
poral transfer of information from occipitotemporal visual/
auditory areas to frontoparietal sensorimotor areas and back
Just what causes suppression versus enhancement in
this model? These changes in rhythmic activity must be
viewed within the context of a local–global organization.
Furthermore, the evidence of functionally distinct lower
(8–10 Hz), middle (10–12 Hz), and upper (15–25 Hz)
bands that may reflect different aspects of this global
versus local processing  suggests that these bands
should be examined independently. Nevertheless, the
evidence indicates that several distinct states may be
possible: (1) local suppression due to desynchronized
activity in a restricted cortical region as a function of
asynchronous activation of the network—this produces
alpha-like suppression, overall greater cellular firing, and
stronger BOLD signals; (2) local enhancement due to
synchronized activity of a smaller population of cells in a
restricted cortical region—this produces mu enhancement
but overall less cellular firing and hence reduced BOLD
signals; (3) global enhancement due to synchronized
activity across widely distributed networks—this produces
alpha-like enhancement, more cellular activity, and stron-
ger BOLD signals; and (4) global suppression due to
synchronized activity of a smaller subset of the networks—
this produces alpha-like suppression, less cellular activity,
and reduced BOLD signals [32,33]. Finally, the optimal
performance of a goal-directed movement necessitates the
integration of movement-specific and cognitive-related
factors [46,47]. In particular, cognitive regulation allows
the subject to plan actions to concrete events and to
respond in a flexible manner to changing environments
[126,127]. Mu suppression and enhancement appear to be
exquisitely responsive to cognitive and affective regulatory
In summary, mu rhythms appear to reflect the translation
of ‘‘seeing’’ and/or ‘‘hearing’’ into ‘‘doing.’’ This function
requires the entrainment or resonance of multiple alpha
generators (visual-, auditory-, and somatosensory-centered
domains). These domains typically exhibit synchronized
and desynchronized activity in a locally independent
manner. However, they become coupled and entrained
when they become coherently and globally engaged in
translating perception into action.
 E.D. Adrian, B.H.C. Matthews, Berger rhythm: potential changes
from the occipital lobes in man, Brain 57 (1935) 355–385.
 E.L. Altschuler, A. Vankov, V. Wang, V.S. Ramachandran, J.A.
Pineda, Person see, person do: human cortical electrophysiological
correlates of monkey see monkey do cells, Abstr.-Soc. Neurosci. 23
(2) (1997) 1848.
 C. Andrew, G. Pfurtscheller, On the existence of different alpha band
 S. Arroyo, R.P. Lesser, B. Gordon, S. Uematsu, D. Jackson, R.
Webber, Functional significance of the mu rhythm of human cortex:
an electrophysiologic study with subdural electrodes, Electroence-
phalogr. Clin. Neurophysiol. 87 (1993) 76–87.
 C. Babiloni, F. Carducci, F. Cincotti, P.M. Rossini, C. Neuper, G.
Pfurtscheller, F. Babiloni, Human movement-related potentials vs.
desynchronization of EEG alpha rhythm: a high-resolution EEG
study, Neuroimage 10 (1999) 658–665.
 E. Basar, H.G. Stampfer, Important associations among EEG-
dynamics, event-related potentials, short-term memory and learning,
Int. J. Neurosci. 26 (1985) 161–180.
 E. Basar, M. Schurmann, C. Basar-Eroglu, S. Karakas, Alpha
oscillations in brain functioning: an integrative theory, Int. J.
Psychophysiol. 26 (1997) 5–29.
 E. Basar, C. Basar-Eroglu, S. Karakas, M. Schurmann, Are cognitive
processes manifested in event-related gamma, alpha, theta and delta
oscillations in the EEG? Neurosci. Lett. 259 (1999) 165–168.
 E. Basar, C. Basar-Eroglu, S. Karakas, M. Schurmann, Oscillatory
brain theory: a new trend in neuroscience, IEEE Eng. Med. Biol.
Mag. 18 (1999) 56–66.
 H. Berger, Uber das Elektrenkephalogamm des Menschen, Arch.
Psychiat. Nervenkr. 87 (1929) 527–570.
 F. Boiten, J. Sergeant, R. Geuze, Event-related desynchronization:
the effects of energetic and computational demands, Electroencepha-
logr. Clin. Neurophysiol. 82 (1992) 302–309.
 J.J. Bouyer, C. Tilquin, A. Rougeul, Thalamic rhythms in cat during
quiet wakefulness and immobility, Electroencephalogr. Clin. Neuro-
physiol. 55 (1983) 180–187.
 G. Buccino, F. Binkofski, G.R. Fink, L. Fadiga, L. Fogassi, V.
Gallese, R.J. Seitz, K. Zilles, G. Rizzolatti, H.J. Freund, Action
observation activates premotor and parietal areas in a somatotopic
manner: an fMRI study, Eur. J. Neurosci. 13 (2001) 400–404.
 G. Buccino, F. Binkofski, L. Riggio, The mirror neuron system and
action recognition, Brain Lang. 89 (2004) 370–376.
 M.S. Buchsbaum, R. Kessler, A. King, J. Johnson, J. Cappelletti,
Simultaneous cerebral glucography with positron emission tomogra-
phy and topographic electroencephalography, Prog. Brain Res. 62
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
 P. Buser, A. Rougeul-Buser, Do cortical and thalamic bioelectric
oscillations have a functional role? A brief survey and discussion,
J. Physiol. Paris 89 (1995) 249–254.
 J.L. Cantero, M. Atienza, R.M. Salas, State-modulation of cortico-
cortical connections underlying normal EEG alpha variants, Physiol.
Behav. 71 (2000) 107–115.
 M.H. Chase, R.M. Harper, Somatomotor and visceromotor correlates
of operantly conditioned 12–14 C-SEC sensorimotor cortical
activity, Electroencephalogr. Clin. Neurophysiol. 31 (1971) 85–92.
 M. Chatila, C. Milleret, P. Buser, A. Rougeul, A 10 Hz ‘‘alpha-like’’
rhythm in the visual cortex of the waking cat, Electroencephalogr.
Clin. Neurophysiol. 83 (1992) 217–222.
 G.E. Chatrian, M.C. Petersen, J.A. Lazarte, The blocking of the
rolandic wicket rhythm and some central changes related to
movement, Electroencephalogr. Clin. Neurophysiol. Suppl. 11
 S. Cochin, C. Barthelemy, B. Lejeune, S. Roux, J. Martineau,
Perception of motion and qEEG activity in human adults, Electro-
encephalogr. Clin. Neurophysiol. 107 (1998) 287–295.
 S. Cochin, C. Barthelemy, S. Roux, J. Martineau, Observation and
execution of movement: similarities demonstrated by quantified
electroencephalography, Eur. J. Neurosci. 11 (1999) 1839–1842.
 A. Covello, M. de Barros-Ferreira, G.C. Lairy, A telemetric study of
central rhythms in children, Electroencephalogr. Clin. Neurophysiol.
38 (1975) 307–319.
 P. Derambure, L. Defebvre, K. Dujardin, J.L. Bourriez, J.M.
Jacquesson, A. Destee, J.D. Guieu, Effect of aging on the spatio-
temporal pattern of event-related desynchronization during a volun-
tary movement, Electroencephalogr. Clin. Neurophysiol. 89 (1993)
 P. Derambure, L. Defebvre, J.L. Bourriez, F. Cassim, J.D. Guieu,
Event-related desynchronization and synchronization. Reactivity of
electrocortical rhythms in relation to the planning and execution of
voluntary movement, Neurophysiol. Clin. 29 (1999) 53–70.
 G. di Pellegrino, L. Fadiga, L. Fogassi, V. Gallese, G. Rizzolatti,
Understanding motor events: a neurophysiological study, Exp. Brain
Res. 91 (1992) 176–180.
 J.P. Donoghue, J.N. Sanes, Motor areas of the cerebral cortex, J. Clin.
Neurophysiol. 11 (1994) 382–396.
 K. Dujardin, P. Derambure, L. Defebvre, J.L. Bourriez, J.M.
Jacquesson, J.D. Guieu, Evaluation of event-related desynchroniza-
tion (ERD) during a recognition task: effect of attention, Electro-
encephalogr. Clin. Neurophysiol. 86 (1993) 353–356.
 K. Dujardin, J.L. Bourriez, J.D. Guieu, Event-related desynchroniza-
tion (ERD) patterns during verbal memory tasks: effect of age, Int. J.
Psychophysiol. 16 (1994) 17–27.
 K. Dujardin, J.L. Bourriez, J.D. Guieu, Event-related desynchroniza-
tion (ERD) patterns during memory processes: effects of aging and
task difficulty, Electroencephalogr. Clin. Neurophysiol. 96 (1995)
 S.P. Duntley, A.H. Kim, D.L. Silbergeld, J.W. Miller, Characteriza-
tion of the mu rhythm during rapid eye movement sleep, Clin.
Neurophysiol. 112 (2001) 528–531.
 R. Eckhorn, Oscillatory and non-oscillatory synchronizations in the
visual cortex and their possible roles in associations of visual
features, Prog. Brain Res. 102 (1994) 405–426.
 R. Eckhorn, A. Gail, A. Bruns, A. Gabriel, B. Al-Shaikhli, M. Saam,
Neural mechanisms of visual associative processing, Acta Neurobiol.
Exp. (Wars.) 64 (2004) 239–252.
 R. Elul, The genesis of the EEG, Int. Rev. Neurobiol. 15 (1971)
 L. Fadiga, G. Buccino, L. Craighero, L. Fogassi, V. Gallese, G.
Pavesi, Corticospinal excitability is specifically modulated by motor
imagery: a magnetic stimulation study, Neuropsychologia 37 (1999)
 A.H. Fagg, M.A. Arbib, Modeling parietal–premotor interactions in
primate control of grasping, Neural Netw. 11 (1998) 1277–1303.
 B. Feige, K. Scheffler, F. Esposito, S.F. Di, J. Hennig, E. Seifritz,
Cortical and subcortical correlates of electroencephalographic alpha
rhythm modulation, J. Neurophysiol. 93 (5) (2005) 2864–2872.
 V.A. Feshchenko, R.A. Reinsel, R.A. Veselis, Multiplicity of the
alpha rhythm in normal humans, J. Clin. Neurophysiol. 18 (2001)
 N.S. Galkina, A.I. Boravova, Specifics of the formation of the alpha
rhythm in different areas of the cerebral cortex in young children,
Vestn. Ross. Akad. Med. Nauk (1994) 36–38.
 N.S. Galkina, A.I. Boravova, Formation of electroencephalographic
mu- and alpha-rhythms in children during the second to third years of
life, Fiziol. Chel. 22 (1996) 30–36.
 V. Gallese, The roots of empathy: the shared manifold hypothesis and
the neural basis of intersubjectivity, Psychopathology 36 (2003)
 V. Gallese, L. Fadiga, L. Fogassi, G. Rizzolatti, Action recognition in
the premotor cortex, Brain 119 (Pt. 2) (1996) 593–609.
 H. Gastaut, Etude electrocorticographique de la reactivite des
rhythmes rolandiques, Rev. Neurol. 87 (1952) 176–182.
 H.J. Gastaut, J. Bert, EEG changes during cinematographic presen-
tation, Electroencephalogr. Clin. Neurophysiol. 6 (1954) 433–444.
 G.L. Gebber, S. Zhong, C. Lewis, S.M. Barman, Human brain alpha
rhythm: nonlinear oscillation or filtered noise? Brain Res. 818 (1999)
 A.P. Georgopoulos, Neural aspects of cognitive motor control, Curr.
Opin. Neurobiol. 10 (2000) 238–241.
 A.P. Georgopoulos, Cognitive motor control: spatial and temporal
aspects, Curr. Opin. Neurobiol. 12 (2002) 678–683.
 P. Gloor, Berger lecture. Is Berger’s dream coming true? Electro-
encephalogr. Clin. Neurophysiol. 90 (1994) 253–266.
 R.I. Goldman, J.M. Stern, J. Engel Jr., M.S. Cohen, Simultaneous
EEG and fMRI of the alpha rhythm, NeuroReport 13 (2002)
 C. Guger, A. Schlogl, C. Neuper, D. Walterspacher, T. Strein, G.
Pfurtscheller, Rapid prototyping of an EEG-based brain–computer
interface (BCI), IEEE Trans. Neural Syst. Rehabil. Eng. 9 (2001)
 I. Hagne, Development of the EEG in health infants during the first
year of life, illustrated by frequency analysis, Electroencephalogr.
Clin. Neurophysiol. 24 (1968) 88.
 I. Hagne, Development of the EEG in normal infants during the first
year of life. A longitudinal study, Acta Paediatr. Scand., Suppl. 232
 R. Hari, R. Salmelin, Human cortical oscillations: a neuromagnetic
view through the skull, Trends Neurosci. 20 (1997) 44–49.
 R. Hari, R. Salmelin, J.P. Makela, S. Salenius, M. Helle, Magne-
toencephalographic cortical rhythms, Int. J. Psychophysiol. 26 (1997)
 R. Hari, S. Levanen, T. Raij, Timing of human cortical functions
during cognition: role of MEG, Trends Cogn Sci. 4 (2000)
 R.C. Howe, M.B. Sterman, Cortical–subcortical EEG correlates of
suppressed motor behavior during sleep and waking in the cat,
Electroencephalogr. Clin. Neurophysiol. 32 (1972) 681–695.
 M. Iacoboni, R.P. Woods, M. Brass, H. Bekkering, J.C. Mazziotta, G.
Rizzolatti, Cortical mechanisms of human imitation, Science 286
 T. Jellema, D.I. Perrett, Cells in monkey STS responsive to
articulated body motions and consequent static posture: a case of
implied motion? Neuropsychologia 41 (2003) 1728–1737.
 B.J. Kaplan, Morphological evidence that feline SMR and human mu
are analogous rhythms, Brain Res. Bull. 4 (1979) 431–433.
 C. Keysers, E. Kohler, M.A. Umilta, L. Nanetti, L. Fogassi, V.
Gallese, Audiovisual mirror neurons and action recognition, Exp.
Brain Res. 153 (2003) 628–636.
 W. Klimesch, Memory processes, brain oscillations and EEG
synchronization, Int. J. Psychophysiol. 24 (1996) 61–100.
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
 W. Klimesch, EEG-alpha rhythms and memory processes, Int. J.
Psychophysiol. 26 (1997) 319–340.
 W. Klimesch, EEG alpha and theta oscillations reflect cognitive and
memory performance: a review and analysis, Brain Res. Brain Res.
Rev. 29 (1999) 169–195.
 W. Klimesch, M. Doppelmayr, H. Schimke, T. Pachinger, Alpha
frequency, reaction time, and the speed of processing information,
J. Clin. Neurophysiol. 13 (1996) 511–518.
 W. Klimesch, H. Schimke, M. Doppelmayr, B. Ripper, J. Schwaiger,
G. Pfurtscheller, Event-related desynchronization (ERD) and the Dm
effect: does alpha desynchronization during encoding predict later
recall performance? Int. J. Psychophysiol. 24 (1996) 47–60.
 W. Klimesch, M. Doppelmayr, T. Pachinger, H. Russegger, Event-
related desynchronization in the alpha band and the processing of
semantic information, Brain Res. Cogn Brain Res. 6 (1997) 83–94.
 W. Klimesch, M. Doppelmayr, J. Schwaiger, P. Auinger, T. Winkler,
FParadoxical_ alpha synchronization in a memory task, Brain Res.
Cogn Brain Res. 7 (1999) 493–501.
 W. Klimesch, P. Sauseng, C. Gerloff, Enhancing cognitive perfor-
mance with repetitive transcranial magnetic stimulation at human
individual alpha frequency, Eur. J. Neurosci. 17 (2003) 1129–1133.
 W. Klimesch, B. Schack, M. Schabus, M. Doppelmayr, W. Gruber, P.
Sauseng, Phase-locked alpha and theta oscillations generate the P1–
N1 complex and are related to memory performance, Brain Res.
Cogn Brain Res. 19 (2004) 302–316.
 E. Kohler, C. Keysers, M.A. Umilta, L. Fogassi, V. Gallese, G.
Rizzolatti, Hearing sounds, understanding actions: action represen-
tation in mirror neurons, Science 297 (2002) 846–848.
 V. Kolev, J. Yordanova, M. Schurmann, E. Basar, Increased frontal
phase-locking of event-related alpha oscillations during task proces-
sing, Int. J. Psychophysiol. 39 (2001) 159–165.
 Y. Koshino, K. Isaki, Familial occurrence of the mu rhythm, Clin.
Electroencephalogr. 17 (1986) 44–50.
 Y. Koshino, E. Niedermeyer, Enhancement of Rolandic mu-rhythm
by pattern vision, Electroencephalogr. Clin. Neurophysiol. 38 (1975)
 C.M. Krause, L. Sillanmaki, M. Koivisto, C. Saarela, A. Haggqvist,
M. Laine, H. Hamalainen, The effects of memory load on event-
related EEG desynchronization and synchronization, Clin. Neuro-
physiol. 111 (2000) 2071–2078.
 W.N. Kuhlman, EEG feedback training: enhancement of somatosen-
sory cortical activity, Electroencephalogr. Clin. Neurophysiol. 45
 C.L. Larson, R.J. Davidson, H.C. Abercrombie, R.T. Ward, S.M.
Schaefer, D.C. Jackson, J.E. Holden, S.B. Perlman, Relations
between PET-derived measures of thalamic glucose metabolism and
EEG alpha power, Psychophysiology 35 (1998) 162–169.
 H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi,
C. Preibisch, K. Krakow, EEG-correlated fMRI of human alpha
activity, Neuroimage 19 (2003) 1463–1476.
 L. Leocani, C. Toro, P. Manganotti, P. Zhuang, M. Hallett, Event-
related coherence and event-related desynchronization/synchroniza-
tion in the 10 Hz and 20 Hz EEG during self-paced movements,
Electroencephalogr. Clin. Neurophysiol. 104 (1997) 199–206.
 K.R. Leslie, S.H. Johnson-Frey, S.T. Grafton, Functional imaging of
face and hand imitation: towards a motor theory of empathy,
Neuroimage 21 (2004) 601–607.
 H. Liang, S.L. Bressler, M. Ding, W.A. Truccolo, R. Nakamura,
Synchronized activity in prefrontal cortex during anticipation of
visuomotor processing, NeuroReport 13 (2002) 2011–2015.
 R. Llinas, U. Ribary, Coherent 40-Hz oscillation characterizes
dream state in humans, Proc. Natl. Acad. Sci. U. S. A. 90 (1993)
 F.H. Lopes da Silva, Neural mechanisms underlying brain waves:
from neural membranes to networks, Electroencephalogr. Clin.
Neurophysiol. 79 (1991) 81–93.
 S. Makeig, M. Westerfield, T.P. Jung, S. Enghoff, J. Townsend, E.
Courchesne, T.J. Sejnowski, Dynamic brain sources of visual evoked
responses, Science 295 (2002) 690–694.
 I. Manshanden, J.C. De Munck, N.R. Simon, F.H. Lopes da Silva,
Source localization of MEG sleep spindles and the relation to sources
of alpha band rhythms, Clin. Neurophysiol. 113 (2002) 1937–1947.
 P.J. Marshall, Y. Bar-Haim, N.A. Fox, Development of the EEG
from 5 months to 4 years of age, Clin. Neurophysiol. 113 (2002)
 D.J. McFarland, L.M. McCane, J.R. Wolpaw, EEG-based communi-
cation and control: short-term role of feedback, IEEE Trans. Rehabil.
Eng. 6 (1998) 7–11.
 D.J. McFarland, L.A. Miner, T.M. Vaughan, J.R. Wolpaw, Mu and
beta rhythm topographies during motor imagery and actual move-
ments, Brain Topogr. 12 (2000) 177–186.
 L.A. Miner, D.J. McFarland, J.R. Wolpaw, Answering questions with
an electroencephalogram-based brain-computer interface, Arch.
Phys. Med. Rehabil. 79 (1998) 1029–1033.
 T. Mulholland, Human EEG, behavioral stillness and biofeedback,
Int. J. Psychophysiol. 19 (1995) 263–279.
 T. Mulholl, S. Runnals, Cortical activation during steady and
changing visual stimulation, Electroencephalogr. Clin. Neurophysiol.
17 (1964) 371–375.
 S.D. Muthukumaraswamy, B.W. Johnson, Changes in rolandic mu
rhythm during observation of a precision grip, Psychophysiology 41
 S.D. Muthukumaraswamy, B.W. Johnson, N.A. McNair, Mu rhythm
modulation during observation of an object-directed grasp, Brain
Res. Cogn Brain Res. 19 (2004) 195–201.
 C. Neuper, G. Pfurtscheller, Event-related dynamics of cortical
rhythms: frequency-specific features and functional correlates, Int.
J. Psychophysiol. 43 (2001) 41–58.
 E. Niedermeyer, Alpha rhythms as physiological and abnormal
phenomena, Int. J. Psychophysiol. 26 (1997) 31–49.
 E. Niedermeyer, A. Goldszmidt, D. Ryan, ‘‘Mu rhythm status’’ and
clinical correlates, Clin. EEG. Neurosci. 35 (2004) 84–87.
 P.L. Nunez, R.B. Silberstein, On the relationship of synaptic activity
to macroscopic measurements: does co-registration of EEG with
fMRI make sense? Brain Topogr. 13 (2000) 79–96.
 P.L. Nunez, B.M. Wingeier, R.B. Silberstein, Spatial–temporal
structures of human alpha rhythms: theory, microcurrent sources,
multiscale measurements, and global binding of local networks,
Hum. Brain Mapp. 13 (2001) 125–164.
 L.M. Oberman, E.M. Hubbard, J.P. McCleery, E.L. Altschuler, V.S.
Ramachandran, J.A. Pineda, EEG evidence for mirror neuron
dysfunctionin autismspectrumdisorders,Cogn. Brain Res. (in press).
 E. Oztop, M.A. Arbib, Schema design and implementation of the
grasp-related mirror neuron system, Biol. Cybern. 87 (2002)
 C. Pantev, S. Makeig, M. Hoke, R. Galambos, S. Hampson, C.
Gallen, Human auditory evoked gamma-band magnetic fields, Proc.
Natl. Acad. Sci. U. S. A. 88 (1991) 8996–9000.
 H. Petsche, S. Kaplan, S.A. von, O. Filz, The possible meaning of the
upper and lower alpha frequency ranges for cognitive and creative
tasks, Int. J. Psychophysiol. 26 (1997) 77–97.
 G. Pfurtscheller, C. Andrew, Event-related changes of band power
and coherence: methodology and interpretation, J. Clin. Neurophy-
siol. 16 (1999) 512–519.
 G. Pfurtscheller, A. Aranibar, Occipital rhythmic activity within the
alpha band during conditioned externally paced movement, Electro-
encephalogr. Clin. Neurophysiol. 45 (1978) 226–235.
 G. Pfurtscheller, W. Klimesch, Event-related desynchronization
during motor behavior and visual information processing, Electro-
encephalogr. Clin. Neurophysiol., Suppl. 42 (1991) 58–65.
 G. Pfurtscheller, C. Neuper, Simultaneous EEG 10 Hz desynchroni-
zation and 40 Hz synchronization during finger movements,
NeuroReport 3 (1992) 1057–1060.
 G. Pfurtscheller, C. Neuper, Event-related synchronization of mu
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68
rhythm in the EEG over the cortical hand area in man, Neurosci. Lett. Download full-text
174 (1994) 93–96.
 G. Pfurtscheller, C. Neuper, Motor imagery activates primary
sensorimotor area in humans, Neurosci. Lett. 239 (1997) 65–68.
 G. Pfurtscheller, M. Pregenzer, C. Neuper, Visualization of sensori-
motor areas involved in preparation for hand movement based on
classification of mu and central beta rhythms in single EEG trials in
man, Neurosci. Lett. 181 (1994) 43–46.
 G. Pfurtscheller, A. Stancak Jr., C. Neuper, Event-related synchro-
nization (ERS) in the alpha band—An electrophysiological correlate
of cortical idling: a review, Int. J. Psychophysiol. 24 (1996) 39–46.
 G. Pfurtscheller, C. Neuper, C. Andrew, G. Edlinger, Foot and hand
area mu rhythms, Int. J. Psychophysiol. 26 (1997) 121–135.
 G. Pfurtscheller, C. Neuper, G. Krausz, Functional dissociation of
lower and upper frequency mu rhythms in relation to voluntary limb
movement, Clin. Neurophysiol. 111 (2000) 1873–1879.
 J.A. Pineda, B.Z. Allison, A. Vankov, The effects of self-movement,
observation, and imagination on mu rhythms and readiness potentials
(RP’s): toward a brain–computer interface (BCI), IEEE Trans.
Rehabil. Eng. 8 (2000) 219–222.
 J.A. Pineda, D.S. Silverman, A. Vankov, J. Hestenes, Learning to
control brain rhythms: making a brain–computer interface possible,
IEEE Trans. Neural Syst. Rehabil. Eng. 11 (2003) 181–184.
 W. Pitts, W.J. McCulloch, How we know universals; the perception of
auditory and visual forms, Bull. Math. Biophys. 9 (1947) 127–147.
 A. Puce, D. Perrett, Electrophysiology and brain imaging of
biological motion, Philos Trans. R. Soc. Lond., B. Biol. Sci. 358
 W.J. Ray, H.W. Cole, EEG activity during cognitive processing:
 G. Rizzolatti, M.A. Arbib, Language within our grasp, Trends
Neurosci. 21 (1998) 188–194.
 G. Rizzolatti, L. Craighero, The mirror-neuron system, Annu. Rev.
Neurosci. 27 (2004) 169–192.
 G. Rizzolatti, L. Fogassi, V. Gallese, Neurophysiological mechan-
isms underlying the understanding and imitation of action, Nat. Rev.
Neurosci. 2 (2001) 661–670.
 A. Rougeul-Buser, P. Buser, Rhythms in the alpha band in cats
and their behavioural correlates, Int. J. Psychophysiol. 26 (1997)
 M.N. Rusalova, M.B. Kostyunina, Spectral correlation studies of
emotional states in humans, Neurosci. Behav. Physiol. 34 (2004)
 N. Sadato, S. Nakamura, T. Oohashi, E. Nishina, Y. Fuwamoto, A.
Waki, Y. Yonekura, Neural networks for generation and suppression
of alpha rhythm: a PET study, NeuroReport 9 (1998) 893–897.
 R. Salmelin, R. Hari, Characterization of spontaneous MEG rhythms
in healthy adults, Electroencephalogr. Clin. Neurophysiol. 91 (1994)
 M. Schurmann, E. Basar, Alpha oscillations shed new light on
relation between EEG and single neurons, Neurosci. Res. 33 (1999)
 J. Sergeant, R. Geuze, W.W. van, Event-related desynchronization
and P300, Psychophysiology 24 (1987) 272–277.
 D.J. Serrien, P. Brown, Changes in functional coupling patterns
during bimanualtask performance,
 D.J. Serrien, A.H. Pogosyan, M.J. Cassidy, P. Brown, Anticipatory
cortico-cortical interactions: switching the task configuration be-
tween effectors, Exp. Brain Res. 154 (2004) 359–367.
 H. Sheikh, D.J. McFarland, W.A. Sarnacki, J.R. Wolpaw, Electro-
encephalographic(EEG)-based communication: EEG control versus
system performance in humans, Neurosci. Lett. 345 (2003) 89–92.
 K.M. Spencer, J. Polich, Poststimulus EEG spectral analysis and
P300: attention, task, and probability, Psychophysiology 36 (1999)
 S.R. Sponheim, B.A. Clementz, W.G. Iacono, M. Beiser, Clinical and
biological concomitants of resting state EEG power abnormalities in
schizophrenia, Biol. Psychiatry 48 (2000) 1088–1097.
 A. Stancak Jr., G. Pfurtscheller, Desynchronization and recovery of
beta rhythms during brisk and slow self-paced finger movements in
man, Neurosci. Lett. 196 (1995) 21–24.
 A. Stancak Jr., G. Pfurtscheller, The effects of handedness and type
of movement on the contralateral preponderance of mu-rhythm
desynchronisation, Electroencephalogr. Clin. Neurophysiol. 99
 M. Steriade, R.R. Llinas, The functional states of the thalamus and
the associated neuronal interplay, Physiol Rev. 68 (1988) 649–742.
 M.B. Sterman, W. Wyrwicka, R. Howe, Behavioral and neurophys-
iological studies of the sensorimotor rhythm in the cat, Electro-
encephalogr. Clin. Neurophysiol. 27 (1969) 678–679.
 W. Storm van Leeuwen, A. Arntz, P. Spoelstra, G.H. Wieneke, The
use of computer analysis for diagnosis in routine electroencepha-
lography, Rev. Electroencephalogr. Neurophysiol. Clin. 6 (1976)
 T.A. Stroganova, E.V. Orekhova, I.N. Posikera, EEG alpha rhythm in
infants, Clin. Neurophysiol. 110 (1999) 997–1012.
 P. Suffczynski, S. Kalitzin, G. Pfurtscheller, F.H. Lopes da Silva,
Computational model of thalamo-cortical networks: dynamical
control of alpha rhythms in relation to focal attention, Int. J.
Psychophysiol. 43 (2001) 25–40.
 W. Szurhaj, E. Labyt, J.L. Bourriez, F. Cassim, L. Defebvre, J.J.
Hauser, J.D. Guieu, P. Derambure, Event-related variations in the
activity of EEG-rhythms. Application to the physiology and the
pathology of movements, Epileptic. Disord. Special Issue (2001)
 C. Toro, G. Deuschl, R. Thatcher, S. Sato, C. Kufta, M. Hallett,
Event-related desynchronization and movement-related cortical
potentials on the ECoG and EEG, Electroencephalogr. Clin.
Neurophysiol. 93 (1994) 380–389.
 C.C. Turbes, G.T. Schneider, Autoregressive spectral filter studies on
the sensorimotor rhythm EEG state variable, Biomed. Sci. Instrum.
26 (1990) 17–23.
 Y. Urakami, S. Okada, T. Kato, M. Tsuji, R. Inoue, Mu rhythm:
clinical assessment of the atypical type, No To Hattatsu 24 (1992)
 W.S. van Leeuwen, G. Wieneke, P. Spoelstra, H. Versteeg, Lack of
bilateral coherence of mu rhythm, Electroencephalogr. Clin. Neuro-
physiol. 44 (1978) 140–146.
 W. van Winsum, J. Sergeant, R. Geuze, The functional significance
of event-related desynchronization of alpha rhythm in attentional and
activating tasks, Electroencephalogr. Clin. Neurophysiol. 58 (1984)
 T.M. Vaughan, L.A. Miner, D.J. McFarland, J.R. Wolpaw, EEG-
based communication: analysis of concurrent EMG activity, Electro-
encephalogr. Clin. Neurophysiol. 107 (1998) 428–433.
 W.G. Walter, The functions of electrical rhythms in the brain,
J. Ment. Sci. 96 (1950) 1–31.
 B.F. Westmorel, D.W. Klass, Defective alpha reactivity with mental
concentration, J. Clin. Neurophysiol. 15 (1998) 424–428.
 S.J. Williamson, L. Kaufman, Advances in neuromagnetic instru-
mentation and studies of spontaneous brain activity, Brain Topogr. 2
 J.R. Wolpaw, D.J. McFarland, Multichannel EEG-based brain–
computer communication, Electroencephalogr. Clin. Neurophysiol.
90 (1994) 444–449.
 J.R. Wolpaw, D.J. McFarland, G.W. Neat, C.A. Forneris, An EEG-
based brain–computer interface for cursor control, Electroencepha-
logr. Clin. Neurophysiol. 78 (1991) 252–259.
 J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M.
Vaughan, Brain–computer interfaces for communication and control,
Clin. Neurophysiol. 113 (2002) 767–791.
 M.N. Zhadin, Rhythmic processes in the cerebral cortex, J. Theor.
Biol. 108 (1984) 565–595.
J.A. Pineda / Brain Research Reviews 50 (2005) 57–68