ORIGINAL PAPER

EEG Correlates of Action Observation in Humans

Elisa Mira Holz Michael Doppelmayr Wolfgang Klimesch Paul Sauseng

Accepted: 26 August 2008 / Published online: 9 September 2008

Springer Science+Business Media, LLC 2008

Abstract To investigate electrophysiological correlates

of action observation electroencephalogram (EEG) was

recorded while participants observed repetitive biological

(human) or non-biological movements (at a rate of 2 Hz).

Steady-state evoked potentials were analyzed and their

neural sources were investigated using low resolution

electromagnetic tomography analysis (LORETA). Results

revealed significantly higher activation in the primary

motor and premotor cortex, supplementary motor area as

well as the posterior parietal cortices during observation of

biological movements, supporting mirror properties of

cortical motor neurons. In addition, interregional commu-

nication was analyzed. Increased coherence for distributed

networks at delta (0.54 Hz) and lower alpha (810 Hz)

frequencies were obtained suggesting integration and

functional coupling between the activated cortical regions

during human action observation.

Keywords Alpha Coherence Delta LORETA Mirror neuron system Oscillations

Introduction

To facilitate understanding and imitation of other persons

behaviour a system matching observed actions with ones

own motor representations is implemented in the brain.

Brain imaging studies postulate that this action observa-

tionexecution matching system is associated with a fronto-

parietal network, in particular the inferior frontal gyrus, the

inferior parietal lobe and the premotor cortex (Gallese et al.

2004; Rizzolatti and Craighero 2004; Iacoboni et al. 2005).

This system, also known as Mirror Neuron System (MNS),

enables us to understand what others are doing by inte-

grating the observed movement into an internal simulation

of this action (Oberman et al. 2005; Rizzolatti and Craig-

hero 2004). This kind of mental imitation of the observed

action seems to be necessary for imitating others behaviour

(Gallese and Goldman 1998; Agnew et al. 2007). Interest-

ingly reaction times (RT) during imitation are shorter when

finger movements have to be imitated than when non-bio-

logical ones are presented (Brass et al. 2000; Kessler et al.

2006). Buccino et al. (2004) showed that actions that

belong to the observers own motor system or are similar to

them but from other species are mapped on the observers

own motor system. But actions that do not belong to it are

predominantly analyzed on their visual properties. It seems

as if cortical motor neurons or neurons with motor functions

belong to a MNS which preferentially processes human

actions whereas non-biological motions or motions of

objects are rather processed by the visual system.

Motor neuron activity is characterized by oscillatory

changes in the 812 Hz range (for a review see Pineda

2005). 812 Hz activity over sensorimotor areas is sup-

pressed during execution (Manganotti et al. 1998) and

imagination of human actions (Pfurtscheller and Neuper

1997; Pfurtscheller et al. 1999; Neuper et al. 2005) but also

during observation of human movements (Hari et al. 1998;

Cochin et al. 1999; Rossi et al. 2002; Muthukumaraswamy

and Johnson 2004). This latter phenomenon was consid-

ered to reflect the activity of mirror neurons. EEG studies

E. M. Holz M. Doppelmayr W. Klimesch P. Sauseng (&)Department of Psychology, University of Salzburg,

Hellbrunnerstr. 34, 5020 Salzburg, Austria

e-mail: paul.sauseng@sbg.ac.at

P. Sauseng

Department of Neurology, University Hospital Eppendorf,

University of Hamburg, Martinistr. 52, 20246 Hamburg,

Germany

123

Brain Topogr (2008) 21:9399

DOI 10.1007/s10548-008-0066-1

comparing brain activity during observation of biological

and non-biological motion showed that activity around

10 Hz over central (sensorimotor) areas is significantly

more suppressed during the observation of biological

(human) movements than observation of scrambled motion

(Ulloa and Pineda 2007) or motion of objects (Oberman

et al. 2005). Despite many findings supporting the exis-

tence of a MNS tuned for human movements the results

remain discordant. For instance also visuomotor priming

for robotic movements (Press et al. 2005) and activation in

MNS caused by robotic movements was found (Gazzola

et al. 2007; Oberman et al. 2007).

For visual stimulation most of the above cited EEG

studies used continuous motion videos without a clear

stimulus onset. These ongoing tasks make it hard to ana-

lyse event-related potentials. Therefore, these EEG studies

only relied on reactivity of certain EEG frequency bands as

indicator for mirror neuron activity. Here, steady-state fin-

ger or object movements at a rate of 2 Hz were presented.

EEG activity phase-locked to the observation of rhythmical

down-movements of the finger or an object was analyzed.

The steady-state evoked potentials elicited by this kind of

stimulation in the present study were then used to localize

mirror neuron activity in 3-D source space. Oscillatory

brain activity (amplitude estimates as well as interregional

synchronization) has turned out to play a major role during

various motor functions (Pfurtscheller and Neuper 1997;

Manganotti et al. 1998; Neuper et al. 2005; Pineda 2005).

However there is a lack of research on these brain param-

eters regarding the observation of biological and non-

biological movements. Therefore, a further aim was to

examine what kind of information about mirror neuron

activity can arise from task related coherence between brain

regionspredominantly in the frequency range around

10 Hzwith regard to the MNS. Subjects viewed kine-

matically matched rhythmical non-biological and biological

movements. With regard to above cited findings we

expected higher activity in terms of steady-state evoked

potential amplitude in fronto-central and parietal regions for

biological motion. We also hypothesized higher interre-

gional synchronization between the activated brain regions

in particular at the EEG alpha frequency band for obser-

vation of biological actions (Manganotti et al. 1998).

Methods

Forty-five subjects participated voluntarily in this study.

Four subjects were excluded from analysis because of

muscle or eye-blink artifacts. The sample of 41 subjects (15

men and 26 women) were at the average age of 23.41 years

(SD = 2.91). All participants were right-handed, had nor-

mal or corrected-to-normal vision and had no history of

neurological disorders. They all gave informed consent

according to the Declaration of Helsinki and were naive

with respect to the purpose of the experiment.

EEG was recorded in an auditory shielded room during

3 conditions. In the moving finger condition (biological

movements) the subjects observed movies of fast repetitive

(human) finger movements (mouse clicks with the index

finger of the right hand onto the left button of a computer

mouse) at a rate of 2 Hz (visually paced). In the moving

object condition (non-biological movements) subjects

viewed the same movements generated by a metal bar

instead of a finger. To ensure that attention was paid to the

videos participants had to absolve a kind of continuous

performance task. Within the blocks of either finger or bar

motions there were button presses to the right instead of the

left mouse button, three to five times for each condition.

These events had to be detected and silently counted (later

reports suggest a 100% correct performance in all sub-

jects). The paradigm was organized as block design with

2 min finger movements and 2 min bar movements

(resulting in 240 movements for each condition). Half of

the subjects first viewed the moving finger condition and

then the moving object condition and half of the subjects

viewed the conditions in the inverse order.

To compare the data from the moving finger and moving

object condition also to a neutral condition, EEG record-

ings during a 2 min baseline measure (resting condition)

were used as an additional control condition. In this con-

dition subjects were required only to keep their eyes open

and fixate the middle of a computer screen without any

additional task or stimulation.

EEG was recorded using a BrainAmp amplifier (Brain

Products, Germany) with 32 channels and a sampling fre-

quency of 500 Hz. Twenty-seven AgAgCl electrodes were

placed according to the extended 1020 system at the

positions Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4,

FT8, T3, C3, Cz, C4, T4, Cp3, CPz, Cp4, T5, P3, Pz, P4, T6,

O1, O2 and were recorded against a common reference at

the nose. In addition, the vertical electrooculogram (EOG)

was recorded. To control for minimal finger movements we

recorded electromyogram (EMG) at the finger flexors of the

right hand. Impedance was kept below 15 kX.Data analysis was done using Vision Analyzer (Brain

Products, Germany). First EEG-signals were offline re-ref-

erenced to digitally linked earlobes. Ocular correction as

suggested by Gratton et al. (1983) was applied. Remaining

ocular and muscular artefacts were manually eliminated. A

low-cutoff filter of 0.5 Hz and a high-cutoff filter of 50 Hz

with a notch filter at 50 Hz were used (EMG was recorded

between 10 and 250 Hz with a notch filter at 50 Hz). To

control for the possibility that differences at EEG activity

over sensorimotor regions between conditions are elicited

by minimal finger movements, EMG was compared between

94 Brain Topogr (2008) 21:9399

123

the two types of tasks. Therefore the EMG was rectified and

the average activity of each block was statistically compared

between conditions using paired sample t-test. The t-test was

not significant, t (40) = .45, P = .66. This shows that EEG

differences between conditions cannot be explained by

artificial finger movements. This does not exclude the pos-

sibility that subjects performed small finger movements

during the experiment; however the non-significance of the

statistical comparison between conditions demonstrates that

subjects did not move fingers differently during the moving

finger and the moving object condition.

EEG data were segmented into 500 ms epochs, each

representing observation of one complete movement (at a

rate of 2 Hz). For the resting condition, data were also

segmented into epochs of 500 ms.

Segments were then averaged separately for the moving

finger and the moving object condition. On average the

number of artefact free trials was 208.37 and 205.85 for the

two conditions, respectively. Averaging of trials resulted in

steady-state evoked potentials (SSEPs; Gerloff et al. 1997)

which are shown in Fig. 1a. For the resting condition no

SSEPs could be calculated as there was no rhythmical

visual stimulation in this condition.

Positive and negative maxima in the SSEP were deter-

mined using peak detection. Then the difference between

the positive and negative peak amplitude was calculated.

This was done separately for the moving finger and the

moving object condition. Then these values were entered

into non-parametrical comparisons (Wilcoxon tests)

between the two conditions separately for each recording

site.

SSEPs were used for source analysis. Therefore, low

resolution electromagnetic tomography analysis (LORETA;

Pascual-Marqui et al. 1994) was performed. The current

source density (CSD) was calculated for each time frame of

the 500 ms analysis interval of the SSEPs for 2,394 cortical

voxels, under the assumption that adjacent voxels have

similar activity (Pascual-Marqui et al. 1994). Then all time

5 V

-250 ms- 5 V

250 ms0 ms

0

A

RL

P

A P

RL

F3 F4

C3

-

Cz C4

P3 Pz P4

[ms]

Fz

FC3-

FCz FC4

CP3 CPz CP4

O1 O2

a b

Fig. 1 Steady-state evoked potentials elicited by action observationat scalp and source level. (a) Steady-state evoked potentials for themoving finger (black) and the moving object condition (red line) forselected electrodes. Brain potentials are phase-locked to the down

movements (at 0 ms) of the observed finger or bar motion. (b) Lowresolution electromagnetic tomography analysis (LORETA) was used

to localize the neural sources underlying the difference between the

two conditions on the electrode level. Red colour indicates higher

current source density (CSD) in the moving finger compared to themoving object condition (P \ .01; A = anterior, P = posterior,L = left, R = right). Note that there is higher activity in bilateral

primary motor cortices (M1), premotor cortices, supplementary motor

areas, right prefrontal cortex and superior and posterior parietal

cortices for the moving finger compared to the moving object

condition

Brain Topogr (2008) 21:9399 95

123

frames were averaged for each condition separately. CSD for

every voxel was compared between both conditions running

voxel-wise non-parametrical statistics as implemented

in LORETA on the 1% significance level (corrected for

multiple comparisons; non-parametric bootstrapping was

used for significance testing, for details see Nichols and

Holmes 2002).

To compute EEG coherence we applied Fast Fourier

Transformation (FFT) to the 500 ms epochs (for the moving

finger, moving object and resting condition separately) and

calculated coherences of all combinations of channels

(n = 351) for the frequency bands: delta (0.54 Hz), theta

(48 Hz), lower alpha (810 Hz), upper alpha (1012 Hz)

and beta 1 (1220 Hz), beta 2 (2030 Hz) and gamma (30

50 Hz). Previous research suggests these frequency bands

to play an important role in cognitive and motoric processes

(Pfurtscheller and Neuper 1997; Gerloff et al. 1998; Neuper

et al. 2005; Pineda 2005; Calmels et al. 2006). Coherence is

a measure of signal similarity between different electrode

sites. Values can range between 0 and 1where 0 indicates

no similarity and 1 stands for absolute similarity. Usually

coherence values are not normally distributed, thus values

were Fisher-z-transformed. Wilcoxons signed rank tests

comparing values between conditions on the 1% signifi-

cance level were run for each electrode pair (Rappelsberger

and Petsche 1988). To examine whether there was a

coherent pattern of either increase or decrease of coherence

in the moving finger condition compared to the moving

object condition or between these conditions and the resting

condition the number of electrode pairs with a significant

increase of coherence were compared with the number of

electrode pairs with significant decrease of coherence using

chi-square tests. Therefore, if there was neither a clear

pattern of increase nor a clear pattern of decrease of

coherence over electrode pairs, i.e. when there was an equal

distribution between electrode pairs showing stronger

coherence in the moving finger condition and electrode

pairs with higher coherence in the moving object or resting

condition, respectively, this was indicated by non-signifi-

cance of the chi-square test. The chi-square testing was

done separately for each of the 7 frequency bands and each

comparison (moving finger vs. moving object, moving finger

vs. resting condition and moving object vs. resting condi-

tion). To correct for this multiple testing Bonferroni

correction was applied to the results.

Results

SSEPs on the Scalp Level

Wilcoxon paired comparisons indicate that there were

SSEPs with larger amplitude in the moving finger than the

moving object condition at the following recording sites:

F3, Fz, F4, FC3, FCz, FC4, Cz, C4, CPz, CP4, O2 (all

Z [ 3.12, P \ .05, Bonferroni corrected).

Current Source Density of SSEPs

LORETA results indicate that there was higher bilateral

activation in the moving finger condition in the premotor

cortex, supplementary motor area (SMA), primary motor

cortex as well as the posterior parietal cortex (PPC),

whereas the latter activation was predominant in the right

hemisphere. In addition higher activation was observed in

the right prefrontal cortex (t = 3.89, P \ .01). As can beseen in Fig. 1b there was no single voxel were the moving

object condition showed higher CSD than the moving fin-

ger condition.

Interregional Coherence

Moving Finger Versus Moving Object Condition

Coherence analysis revealed stronger synchronization in

the moving finger as compared with the moving object

condition for lower alpha (v2 = 22.27, P \ .01) and delta(v2 = 16.95, P \ .01) frequency bands, as depicted inFig. 2. In the lower alpha frequency band a fronto-parietal

network including recording sites overlying the premotor

cortex and primary sensorimotor cortex of the left (FC3,

C3, CP3) and the right (FC4, C4, CP4) hemisphere and the

frontocentral cortex including the SMA (Fz, FCz, Cz)

(Homan et al. 1987) were coherently active during obser-

vation of finger movements. Delta coherence was found

between occipital and central electrode sites with pre-

dominant coupling between the right primary visual cortex

(O2) and the sensorimotor cortex and the SMA. Higher

coherence in the moving object condition was found for the

theta frequency band (v2 = 99.56, P \ .01), extendingover a wider fronto-parietooccipital network. No system-

atic differences in coherence between conditions were

found for upper alpha, beta 1, beta 2 and gamma.

Moving Finger Versus Resting Condition

Stronger interregional synchronization was found for delta

(v2 = 137, P \ .01), theta (v2 = 11. 94, P \ .01), loweralpha (v2 = 77.76, P \ .01) and gamma (v2 = 27.22,P \ .01) in the moving finger condition than during rest.Delta coupling includes a wide fronto-parietooccipital

network (see Fig. 2). In the theta frequency higher syn-

chronization in a fronto-parietal network was found for the

moving finger condition than for rest. For lower alpha

higher coherent coupling was found between electrode

sites overlying the bilateral sensorimotor and premotor

96 Brain Topogr (2008) 21:9399

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cortex, the supplementary motor area (SMA), the posterior

parietal cortex (PPC) and the right temporal cortex. Within

Gamma frequency left temporal sites are coupled with

central leads. For Beta 1 we found higher synchronization

for the resting condition within a broad distributed network

(v2 = 41.88, P \ .01). No significant effects were foundfor upper alpha and beta 2.

Moving Object Versus Resting Condition

We found a higher degree of synchronized activity in the

moving object condition for delta (v2 = 22, P \ .01) andtheta (v2 = 51.02, P \ .01). The coupling for delta is spreadover frontal and parietal regions. For theta global coupling

was found. Stronger desynchronization in the moving object

condition than for rest were found for beta 1 (v2 = 65.09,P \ .01) and beta 2 (v2 = 40.16, P \ .01), extending over awide distributed global network. There were no significant

results for lower alpha, upper alpha and gamma.

Discussion

Brain activity evaluated by the means of LORETA during

observation of biological versus non-biological movements

was assessed. The relative brain activity should show the

location of mirror neurons. The results indicate that regions

are stronger activated during observation of biological

versus non-biological movements, in particular the pre-

motor cortex, supplementary motor cortex, primary motor

cortex, and the posterior parietal cortex (PPC). This is well

in line with other studies showing that premotor, supple-

mentary and primary motor regions contain neurons which

have mirror neuron properties (Hari et al. 1998; Cochin

et al. 1999; Rossi et al. 2002; Perani et al. 2001; Muthu-

kumaraswamy and Johnson 2004; Oberman et al. 2005;

Ulloa and Pineda 2007).

There is agreement that the PPC is responsible for vi-

suomotoric integration (Buneo and Andersen 2006;

Iacoboni 2006). However the right posterior parietal acti-

vation seems also to be related to self-recognition during

imitation whereas the left posterior parietal activity is

related to tool use (Iacoboni 2006). The higher right pos-

terior parietal activation during observation of biological

movements as found in this experiment might reflect mirror

neuron activity evoked by the recognition of the human

hand. Thus, the posterior parietal cortex seems to be an

important link between visuomotoric integration and the

emergence of an internal representation of an action.

Although there are a lot of findings describing the

localization of the MNS, little is known about the inter-

action within this network and with other regions. We

found interregional coupling predominately at lower alpha

and delta frequency during the observation of finger

Fig. 2 Coherence at delta, thetaand lower alpha frequency

bands. Red connections denote

higher coherence during

observation of finger movements(P \ .01) compared to movingobject (a) or resting condition(b) and for moving objectcompared to a resting condition

(c). Blue connections indicatehigher coherences during the

moving object or restingcondition (a and b, respectively)than during observation of the

moving finger condition, or in(c) higher coherence in theresting condition compared tothe moving object condition indelta, theta and lower alpha

frequency bands

Brain Topogr (2008) 21:9399 97

123

movements. Coherence within the EEG lower alpha fre-

quency range (810 Hz) showed a similar distribution

compared with coherence during execution of repetitive

finger movements in other studies, predominantly over

fronto-central brain regions (Manganotti et al. 1998).

However, it should be noted that in the present experiment

subjects only observed movements but did not perform the

observed actions. Therefore, it cannot derive from the

present data whether during the imitation of actions there

would be identical neural activity as during their

observation.

Several studies have shown that alpha is the prevailing

frequency of coupling during repetitive finger movements

(Manganotti et al. 1998; Pollok et al. 2005; Toma et al.

2002). These results are in line with findings of Calmels

et al. (2006). They found alpha synchronization over

fronto-central brain regions during both action execution

and observation. The comparison of the oscillatory brain

activity during observation of biological and non-biologi-

cal motion makes obvious that this specialized 810 Hz

network underlies predominately the observation of finger

movements. This is further evidence for mirror neurons,

which seem to be tuned for observation of biological

actions. Motor-relevant regions were in addition coupled to

the PPC at lower alpha frequency in the present study. This

confirms the assumption that the PPC is functionally linked

to the MNS. These findings are also underpinned by the

fact that there was no similar pattern for the non-biological

movement condition (compared with a resting condition).

Additionally, we found global synchronization within

delta (0.54 Hz) frequency during observation of finger

movements whereas there was less coupling for the

observation of movements of an object. Coupling within

delta seems to be a mechanism to integrate the rhythmic

visuo-motoric information in terms of synchronization

between parietal and central regions. Findings confirm that

lower frequencies are related to global binding which

means synchronization of a large neuronal network (von

Stein and Sarntheim 2000). This effect might be reinforced

by the 2 Hz rhythmic stimulation of the experimental

design (which is indicated by the fact that there is an even

stronger effect when the moving finger condition was

compared to the resting condition without rhythmical

stimulation). As described above mirror neuron activity in

the PPC and coupling between PPC and motor cortices

may affect the visuo-motoric integration of biological

movements. Thus we suggest that these processes are

crucial for encoding of the properties (i.e. dynamics) of

action and therefore enable imitation. With other words,

these processes seem to facilitate imitation to biological

action cues. Indeed findings indicate that during imitation

reaction times and the degree of synchronization between

PPC and the premotor cortices are correlated in an early

time window after presentation of a cue (Kessler et al.

2006). This was interpreted as a behavioural advantage for

biological cues. Thus, besides the understanding of the

intentions of others the understanding of the dynamics of

movements can be seen as necessary for imitation of an

action (Wolfensteller et al. 2007).

We found a higher number of coherent electrode pairs in

the theta band for fronto-parietal long range connections

for the moving object than in the moving finger condition

(see Fig. 2). Sauseng et al. (2007) showed that motor tasks

requiring a higher degree of executive control exhibit

stronger fronto-parietal theta coupling. As the observation

of biological movements is more familiar than non-bio-

logical motions it is supposed that the latter requires more

executive control and thus elicits stronger theta coupling

than observation of biological movements. Compared to

the resting condition both the biological and the non-bio-

logical movement condition showed a fronto-central

increase of theta coupling and fronto-posterior decrease of

theta coherence. The synchronization in the non-biological

movement condition was stronger, underpinning the

interpretation that this condition requires more executive

functions. At beta 1 there was decreased coherence in the

biological and the non-biological movement conditions

compared to the resting condition. This effect did not dif-

ferentiate between the movement observation conditions

and thus seems to be rather unspecific and related to visual

stimulation. For gamma there was stronger interregional

coupling in the moving finger condition and for beta 2

decrease of coherence compared to the resting condition,

which similarly to beta 1 did not dissociate between bio-

logical and non-biological movement conditions.

Concluding, this study replicated, on the basis of SSEPs,

the findings indicating that a distributed bilateral network

including primary motor cortex, supplementary motor area,

premotor cortex and (right) parietal cortex is selectively

active during observation of biological movements and less

during observation of non-biological motion. These results

provide mirror properties of these regions; however the

lack of an execution condition in the present study limits

evidence for an action observationexecution matching

system. We could show that these fronto-central regions

are coherently active while biological but not non-biolog-

ical movements were observed, in particular at lower alpha

frequency. As brain activity in the alpha frequency range is

related to motoric activation we conclude that this is a

further indicator for mirror neuron activity.

Acknowledgements This research was supported by the AustrianAcademy of Sciences P_145001_1. PS is recipient of an APART

fellowship by the Austrian Academy of Sciences. Thanks also to

Sieglinde Gruber and Kerstin Hoedlmoser and several undergraduate

students at the University of Salzburg who helped with data

acquisition.

98 Brain Topogr (2008) 21:9399

123

References

Agnew ZK, Bhakoo KK, Puri BK (2007) The human mirror system: a

motor resonance theory of mind-reading. Brain Res Rev 54:286

293

Brass M, Bekkering H, Wohlschlager A, Prinz W (2000) Compat-

ibility between observed and executed finger movements:

comparing symbolic, spatial, and imitative cues. Brain Cogn

44:124143

Buccino G, Fausta L, Canessa N, Ilaria P, Lagravinese G, Benzzi F,

Porro C, Rizzolatti G (2004) Neural circuits involved in the

recognition of actions performed by nonconspecifics. An fMRI

study. J Cogn Neurosci 16:114126

Buneo CA, Andersen RA (2006) The posterior parietal cortex:

sensorimotor interface for the planning and online control of

visually guided movements. Neuropsychologia 44:25942606

Calmels C, Holmes P, Jarry G, Hars M, Lopez E, Paillard A, Stam CJ

(2006) Variability of EEG synchronization prior to and during

observation and execution of a sequential finger movement. Hum

Brain Mapp 27:251266

Cochin S, Barthelemy C, Roux S, Martineau J (1999) Observation

and execution of movement: similarities demonstrated by

quantified electroencephalography. Eur J Neurosci 11:1839

1842

Gallese V, Goldman A (1998) Mirror neurons and the simulation

theory of mind-reading. Trends Cogn Sci 2:493501

Gallese V, Keysers C, Rizzolatti G (2004) A unifying view of the

basis of social cognition. Trends Cogn Sci 8:398403

Gazzola V, Rizzolatti G, Wicker B, Keysers C (2007) The anthro-

pomorphic brain: the mirror neuron system responds to human

and robotic actions. NeuroImage 35:16741684

Gerloff C, Toro C, Uenishi N, Cohen LG, Leocani L (1997) Steady-

state movement-related cortical potentials: a new approach to

assessing cortical activity associated with fast repetitive finger

movements. Electroencephalogr Clin Neurophysiol 102:106113

Gerloff C, Richard J, Hadley J, Schulman AE, Honda M, Hallett M

(1998) Functional coupling and regional activation of human

cortical motor areas during simple, internally paced and

externally paced finger movements. Brain 121:15131531

Gratton G, Coles MGH, Donchin E (1983) A new method for off-line

removal of ocular artifact. Electroencephalogr Clin Neurophys-

iol 55:468484

Hari R, Forss N, Avikainen S, Kirveskari E, Salenius S, Rizzolatti G

(1998) Activation of human primary motor cortex during action

observation: a neuromagnetic study. Proc Natl Acad Sci USA

95:1506115065

Homan RW, Herman J, Purdy P (1987) Cerebral location of

international 10-20 system electrode placement. Electroencep-

halogr Clin Neurophysiol 66:376382

Iacoboni M (2006) Visuo-motor integration and control in the human

posterior parietal cortex: evidence from TMS and fMRI.

Neuropsychologia 44:26912699

Iacoboni M, Molnar-Szakacs I, Gallese V, Buccino G, Mazziotta J,

Rizzolatti G (2005) Grasping the intentions of others with ones

own mirror neuron system. PloS Biol 3:e79

Kessler K, Biermann-Ruben K, Jonas M, Hartwig RS, Baumer T,

Munchau A, Schnitzler A (2006) Investigating the human mirror

neuron system by means of cortical synchronization during the

imitation of biological movements. NeuroImage 33:227238

Manganotti P, Gerloff C, Toro C, Katsuta H, Sadato N, Zhuang P,

Leocani L, Hallett M (1998) Task-related coherence and task-

related spectral power changes during sequential finger move-

ments. Electroencephalogr Clin Neurophysiol 109:5062

Muthukumaraswamy SD, Johnson BW (2004) Primary motor cortex

activation during action observation revealed by wavelet anal-

ysis of the EEG. Clin Neurophysiol 115:17601766

Neuper C, Scherer R, Reiner M, Pfurtscheller G (2005) Imagery of

motor actions: differential effects of kinestetic and visual-motor

mode of imagery in single-trial EEG. Cogn Brain Res 25:668

677

Nichols TE, Holmes AP (2002) Nonparametric permutation tests for

functional neuroimaging: a primer with examples. Hum Brain

Mapp 15:125

Oberman LM, Hubbard EM, McCleery JP, Altschuler EL, Rama-

chandran VS, Pineda JA (2005) EEG evidence for mirror neuron

dysfunction in autism spectrum disorders. Cogn Brain Res

24:190198

Oberman LM, McCleery JP, Ramachandran VS, Pineda JA (2007)

EEG evidence for mirror neuron activity during the observation

of human and robot actions: toward an analysis of the human

qualities of interactive robots. Neurocomputing 70:21942203

Pascual-Marqui RD, Michel CM, Lehmann D (1994) Low resolution

electromagnetic tomography: a new method for localizing

electrical activity in the brain. Int J Psychophysiol 18:4965

Perani D, Fazio F, Borghese NA, Tettamanti M, Ferrari S, Decety J,

Gilardi MC (2001) Different brain correlates for watching real

and virtual hand actions. NeuroImage 14:749758

Pfurtscheller G, Neuper C (1997) Motor imagery activates primary

sensorimotor area in humans. Neurosci Lett 239:6568

Pfurtscheller G, Neuper C, Ramoser H, Muller-Gerking J (1999)

Visually guided motor imagery activates sensorimotor areas in

humans. Neurosci Lett 269:153156

Pineda JA (2005) The functional significance of mu rhythms:

translating seeing and hearing into doing. Brain Res Rev

50:5768

Pollok B, Gross J, Muller K, Aschersleben G, Schnitzler A (2005)

The cerebral oscillatory network associated with auditorily

paced finger movements. NeuroImage 24:646655

Press C, Bird G, Flach R, Heyes C (2005) Robotic movement elicits

automatic imitation. Cogn Brain Res 25:632640

Rappelsberger P, Petsche H (1988) Probability mapping: power and

coherence analyses of cognitive processes. Brain Topogr 1:4654

Rizzolatti G, Craighero L (2004) The mirror-neuron system. Annu

Rev Neurosci 27:169192

Rossi S, Tecchio F, Pasqualetti P, Ulivelli M, Pizzella V, Romani GL,

Passero S, Battistini N, Rossini PM (2002) Somatosensory

processing during movement observation in humans. Clin J

Neurophysiol 113:1624

Sauseng P, Hoppe J, Klimesch W, Gerloff C, Hummel FC (2007)

Dissociation of sustained attention from central executive

functions: local activity and interregional connectivity in the

theta range. Eur J Neurosci 25:587593

Toma K, Mima T, Matsuoka T, Gerloff C, Ohnishi T, Koshy B,

Andres F, Hallet M (2002) Movement rate effect on activation

and functional coupling of motor cortical areas. J Neurophysiol

88:33773385

Ulloa ER, Pineda JA (2007) Recognition of point-light biological

motion: mu rhythms and mirror neuron activity. Behav Brain

Res 183:188194

von Stein A, Sarntheim J (2000) Different frequencies for different

scales of cortical integration: from local gamma to long range

alpha/theta synchronization. Int J Psychophysiol 38:301313

Wolfensteller U, Schubotz RI, von Cramon DY (2007) Understanding

non-biological dynamics with your own premotor system.

NeuroImage 36:3343

Brain Topogr (2008) 21:9399 99

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EEG Correlates of Action Observation in HumansAbstractIntroductionMethodsResultsSSEPs on the Scalp LevelCurrent Source Density of SSEPsInterregional CoherenceMoving Finger Versus Moving Object ConditionMoving Finger Versus Resting ConditionMoving Object Versus Resting Condition

DiscussionAcknowledgementsReferences

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