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Research Article
Effect of Pilates Training on Alpha Rhythm
Zhijie Bian,1Hongmin Sun,2Chengbiao Lu,1Li Yao,3Shengyong Chen,4and Xiaoli Li3
1Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2College of Physical Education, Yanshan University, Qinhuangdao 066004, China
3National Lab of Cognitive Neuroscience and Learning, Beijing Normal University, Xin Jie Kou Wai Avenue, Haidian District,
Beijing 100875, China
4College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Correspondence should be addressed to Xiaoli Li; xiaoli@bnu.edu.cn
Received April ; Accepted May
Academic Editor: Carlo Cattani
Copyright © Zhijie Bian et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this study, the eect of Pilates training on the brain function was investigated through ve case studies. Alpha rhythm changes
during the Pilates training over the dierent regions and the whole brain were mainly analyzed, including power spectral density
and global synchronization index (GSI). It was found that the neural network of the brain was more active, and the synchronization
strength reduced in the frontal and temporal regions due to the Pilates training. ese results supported that the Pilates training is
very benecial for improving brain function or intelligence. ese ndings maybe give us some line evidence to suggest that the
Pilates training is very helpful for the intervention of brain degenerative diseases and cogitative dysfunction rehabilitation.
1. Introduction
Pilates was created in the s by physical trainer Joseph
H. Pilates and has been developed based on the Eastern
and Western health preservation methods, such as Yoga and
Taichi. is exercise is suitable for all the people and may
be one of the most attractive tness trainings [,]. Pilates
exercise was found to be able to correct body posture, relax
thewaistandneck,solvetheproblemofshoulder,andreduce
fatofarmandabdomen[–]. Pilates can improve the blood
circulation and cardiopulmonary function as the exercise is
dominated by the rhythmic breath, particularly the lateral
thoracic breathing that can eectively promote the exchange
of oxygen. e Pilates has been proven to impact personal
autonomy [], pain control [], improved muscle strength [],
exibility [], and motor skills []. Physical activity can be
considered as an approach to improve organic conditions and
prevent physical degeneration []. Further studies suggest
that Pilates can release the stress of mind, increase brain’s
oxygen supply, and enhance brain function [,], and
studies in aged samples also suggest that Pilates is benecial
to mental state, including sleep quality, emotion, and self-
condence [].
However, the direct evidence of Pilates on brain activity
such as electroencephalographic (EEG) is lacking. In this
study, we recorded resting-state EEG signals before and aer
Pilates exercise. We concentrated on the analysis of alpha
rhythm (– Hz) changes of the EEG, which is associated
with the intelligence. e aim is to demonstrate whether or
not Pilates can impact the brain functions or intelligence.
2. Methods
2.1. Subjects. Aer providing informed consent, ve healthy
postgraduate girls (mean age 24 ± 1 years) voluntarily
participatedinthisstudy.eywerefreetowithdrawfrom
the experiments at any time. All subjects included in this
experiment were right-handed, nonathletes, and had never
been suering from neurological and psychiatric disorders.
e study was approved by the local ethics committee, and
all participants gave written informed consent for this study.
2.2. Pilates Training. e ve girls were trained with Pilates
four sessions a week (Monday, Tuesday, ursday, and Fri-
day) in a well-ventilated room, at least minutes per session.
For the rst three weeks, they were taught Pilates movements
Computational and Mathematical Methods in Medicine
step by step, and they reviewed the former movements in
each training session and were corrected by the coach aer
learning the new ones. Aer they were taught a total of
movements, they practiced for – times in each session, and
they were instructed to perform the sequences as accurately
and smoothly coupled with breathing. e training lasted for
weeks. And the resting-state EEG rhythms were recorded
with eyes closed before Pilates training and aer each two
weeks training.
2.3. Data Acquisition. EEG recordings were performed at six
dierent time points. e rst recording was performed just
prior to the onset of training week (week ). Aer each two
weeks training, there was one recording, such as week ,
week , week , week , and week . During recordings, the
subjectswereaskedtoclosetheireyesandsitinacomfortable
armchair, who were relaxed and awake in a dim room for
minutes during each recording.
e EEG data acquisition was performed with Neuroscan
EEG/ERP recording system ampliers (SynAmps) with
Ag/AgCl surface electrodes, which were xed in a cap at the
standard positions according to the extended international
– system, and with bit SCAN. acquisition system
thatcouldalsobeusedtocontinuouslyviewtheEEGrecord-
ings. A reference electrode was placed between Cz and CPz,
and ground electrode was placed between FPz and Fz. Hori-
zontal and vertical electrooculograms (EOG) were recorded
as well. e EEG was recorded with unipolar montages except
for the EOG with bipolar montages. e impedances of all
electrodes were < k. During the recording, the data was
band-pass ltered in the frequency range .– Hz and
sampled at KHz. Digital conversion of the measured analog
signals was accomplished with a bit digitizer.
2.4. Data Analysis. In this study, the alpha rhythm (– Hz)
in the EEG recordings was concentrated on. In order to detect
the alpha rhythm’s changes over dierent regions, the brain
was divided into ve regions: frontal, le temporal, central,
right temporal, and posterior (see Figure ). Power spectral
density and global synchronization index (GSI) at the alpha
frequencybandwerecomputedinallregions.
2.4.1. Preprocessing for EEG. e raw EEG data was analyzed
oine using EEGLAB (http://sccn.ucsd.edu/eeglab/ []). It
was rereferenced to M (le mastoid process) and M (right
mastoid process), the two EOG channels were extracted, the
band-passlter(–Hz)wasinitiallyusedtoincludethe
frequency band of interest, and then the data was resampled
to Hz for further analysis.
2.4.2. Spectral Analysis. Aer preprocessing, we chose EEG
data of minutes for analysis. Power spectral density (PSD)
was estimated using pwelch method, which has a better noise
performance compared with other power spectra estimation
methods. e PSD was calculated using s epochs for each
signal. Each epoch was divided into overlapping segments
using periodic -s hamming window with % overlap.
And then the peak power and peak power frequency were
FPz FP2
FP1
Fz
Cz
Pz
Oz
O1 O2
T7 T8
F7 F8
P7 P8
F3 F4
C3 C4
P3 P4
AF3 AF4
F5 F1 F2 F6
FCz
FT7 FT8
FC5 FC3 FC1 FC2 FC4 FC6
CPz
TP7 TP8
CP1
CP3 CP2 CP4 CP6
CP5
POz PO8
PO7
P5 P2 P6
PO4 PO6
PO3
PO5
C2 C6
C1
C5
CB1 CB2
P1
1
234
5
F : Extended – electrodes system and area electrodes’
partition. e dotted lines divided the whole into regions: the
numbers , , , , and separately denote the frontal, le temporal,
central, right temporal, and posterior regions, respectively.
calculated for the alpha band in each epoch. Outliers rejection
was performed using generalized extreme studentized deviate
(GESD) [] for all epochs in each channel. e remained
epochs were averaged.
e PSD for each channel in all frequency bands was
obtained. In order to estimate the changes of peak power
and corresponding frequency during the Pilates training over
dierent regions and the whole brain, the PSD was averaged
overeachregionandthewholebrain.
2.4.3. GSI. Synchronization is known as a key featureto eval-
uate the information process in the brain. For long EEG data,
global synchronization index (GSI) can reveal the true syn-
chronization features of multivariable EEG sequences better
than other methods [].
To eliminate the eect of amplitude, the EEG signals pre-
processed need to be normalized by
=
𝑖()(=1,...,;=1,...,),
𝑖()=𝑖()−
𝑖
𝑖,
=
𝑖(),
()
where is considered as the multivariate EEG data, is the
number of channels, is the number of data points in time
window ,𝑖()is the normalized signal, and is a vector of
𝑖(),and𝑖and 𝑖are the mean and standard deviation of
𝑖(),respectively.
Computational and Mathematical Methods in Medicine
T : Comparisons of global changes before training (BT) and aer training (AT) for each case.
Persons
Changes
Alpha peak power Alpha peak frequency GSI
BT (V/Hz) AT (V/Hz) BT (Hz) AT (Hz) BT AT
First . 213.47 ± 32.79 . 10.02 ± 0.06 . 0.43 ± 0.03
Second . 9.67 ± 1.27 . 9.76 ± 0.09 . 0.31 ± 0.03
ird . 3.91 ± 0.52 . 11.48 ± 0.25 . 0.28 ± 0.02
Forth . 65.95 ± 10.97 . 9.61 ± 0.08 . 0.32 ± 0.05
Fih . 57.34 ± 9.25 . 10.06 ± 0.06 . 0.29 ± 0.02
Ave rag e . 70.07 ± 10.96 . 10.18 ± 0.11 . 0.33 ± 0.03
TocalculatetheGSIofmultivariateEEGdata,aphase
correlation matrix Cwas constructed. e phase of the each
EEG series is estimated using continuous wavelet transform.
e phase dierence of two EEG traces is dened by
𝑤
𝑥𝑖𝑥𝑘(,)=
𝑤
𝑥𝑖(,)−
𝑤
𝑥𝑘(,)(
=1,...,).()
en, the phase synchronization is calculated by
𝑖𝑘 =
𝑗Δ𝜑𝑤
𝑥𝑖𝑥𝑘(𝑠,𝜏)𝑇
∈[0,1],()
where ⋅𝑇indicates the average of the time window .
𝑖𝑘 indicates the phase synchronization of signals 𝑖() and
𝑘(). For all EEG series, a phase correlation matrix can be
written as C={
𝑖𝑘}.
en, the eigenvalue decomposition of Cis dened as
follows:
Ck𝑖=
𝑖k𝑖,()
where eigenvalues 1≤
2≤⋅⋅⋅≤
𝑀areinincreasingorder
and k𝑖,=1,...,are the corresponding eigenvectors.
In order to reduce the “bias” caused by the algorithm
and length of data, amplitude adjusted Fourier transformed
(AAFT)surrogatemethod[]wasusedinthisstudy.Based
on the surrogate series surr,thenormalizedphasesurrogate
correlation matrix Rwas calculated, and the 𝑠
1≤
𝑠
2≤
⋅⋅⋅≤
𝑠
𝑀were the eigenvalues of surrogate correlation
matrix R. e distribution of the surrogate eigenvalues can
reect the random synchronization of the multivariate time
series. To reduce the eects of the random components in
the total synchronization, the eigenvalues were divided by the
averaged surrogate eigenvalues. e GSI was calculated by
𝑔
𝑖=𝑖/𝑠
𝑖
∑𝑀
𝑖=1 𝑖/𝑠
𝑖(=1,...,),
GSI =1+∑𝑀
𝑖=1 𝑔
𝑖log 𝑔
𝑖
log (),
()
where 𝑠
𝑖is the averaged eigenvalues of the surrogate series.
CalculatingtheGSIusedsepochswith%overlapfor
the alpha rhythm over the ve regions and the whole brain.
Outlier’s rejection [] was also used, and then the remained
epochs were averaged. Average of GSI over dierent regions
andthewholebrainwasobtainedaswell.
2.4.4. Calculation of the Relative Variable Ratio. In order to
estimate the changes during the Pilates training, the relative
variable ratio may be calculated by
(𝑘)
𝑗𝑖 =(𝑘)
𝑗𝑖 −
(𝑘)
𝑗1
(𝑘)
𝑗1
=1,...,,=6;=1,...,=5;=1,2,3,
()
where isthenumberoftests,isthenumberofsubjects,
and (𝑘)
𝑗𝑖 is the relative variable ratio to the rst test. (𝑘)
𝑗𝑖 is the
feature value of EEG recordings. When =1,
(𝑘)
𝑗𝑖 presents
the changes of the peak power; when =2,
(𝑘)
𝑗𝑖 presents the
changes of the peak frequency; when =3,
(𝑘)
𝑗𝑖 presents the
changes of GSI. All changes were over the Pilates training.
If the variables increased over the Pilates training, (𝑘)
𝑗𝑖 will
be greater than zero; if they decreased, (𝑘)
𝑗𝑖 will be less than
zero; if there are no changes, (𝑘)
𝑗𝑖 will be approximate to zero.
For the limited numbers of only ve subjects, boxplot is used
to describe the changes over the Pilates training duration.
3. Results
3.1. Spectral Analysis. e results of alpha peak power and
alpha peak frequency in each region and over the whole brain
were shown in Figure . e comparisons of global changes
before training (BT) and aer training (AT) for each case were
shown in Table .
e alpha peak powers were dierent among the ve
cases. e power that is in the rst case was the largest.
A relative lower peak power was observed in the second
and the third cases. ere may be individual dierence, but
the trend of changes was the same. Tabl e presented that
the alpha peak power increased in all cases and the average
value increased as well (. to 70.07 ± 10.96)(Table ). e
changes of alpha peak frequencies varied among dierent
individuals: decreased in three cases, increased in one case,
and unchanged in one case, and the average value was slightly
decreased (. to 10.18 ± 0.11)(Tabl e ).
eratiosofalphapeakpowerandalphapeakfrequency
couldeliminatetheeectofindividualfactor(seeFigure ).
e ratios were obtained to investigate the two indicators’
changes during Pilates training. Figure (a) showed that
alpha peak power was increased in various regions and
Computational and Mathematical Methods in Medicine
Frontal ratioRight temporal ratio
Le temporal ratio
Central ratio
Occipital ratio
Global ratio
0
1
2
3
123456123456 123456
N test N test
−1
0
1
2
3
−1
0
1
2
3
−1
0
1
2
3
−1
0
1
2
3
−1
0
1
2
3
−1
Ntest
123456123456 123456
N test N test
Ntest
(a) Alpha Peak Powe r
−0.2
−0.1
0
0.1
−0.2
−0.1
0
0.1
−0.2
−0.1
0
0.1
−0.2
−0.1
0.1
−0.2
−0.1
0.1
−0.2
−0.1
000
0.1
Frontal ratioRight temporal ratio
Le temporal ratio
Central ratio
Occipital ratio
Global ratio
123456 123456123456
N test N test
Ntest
123456123456 123456
N test N test
Ntest
(b) Alpha Peak Frequency
F : Relative changes of alpha peak power (a) and peak frequency (b) during the Pilates training. Alpha peak power increased in the
ve regions and the whole brain as (a) shows. As (b) shows, most of the median of alpha peak frequency decreased but was not signicant.
One box represented one test in (a) and (b).
Computational and Mathematical Methods in Medicine
−0.5
0
0.5
−0.5
0
0.5
−0.5
0
0.5
−0.5
0
0.5
−0.5
0
0.5
−0.5
0
0.5
Frontal ratio
Right temporal ratio
Le temporal ratio
Central ratio
Occipital ratio
Global ratio
123456 123456 123456
N test N test
Ntest
123456123456123456
N test N test
Ntest
F : Relative changes of GSI for alpha rhythm during the Pilates training. e GSI in the frontal and temporal regions was decreased,
but it almost increased in the central region, and the changes in the occipital region were not obvious. e GSI over the whole brain decreased
obviously. One box represented one test.
the whole brain. e median of ratios was greater than zero.
e ratios of alpha peak power versus alpha peak frequency
were increased by about % to %, (especially in the second
test, which was two weeks aer Pilates training), % to %,
%to%,and%to%,forthefrontal,temporal,
central, occipital, and the whole brain, respectively. e alpha
peak frequency decreased in small degree during Pilates
training, and the changes were not statistically signicant (see
Figure (b)).
3.2. GSI. e GSI changes of the whole brain before and aer
pilates training in individuals and the average value of the ve
subjects were listed in Ta b l e . e GSI values were decreased
during the Pilates training signicantly.
e time-dependent changes of GSI during the Pilates
training in dierent regions and over the whole brain were
also studied. Figure plotted the relative variable ratios of
GSI. For the frontal region, the GSI has decreased by about –
%, %–%, and % aer two, four, and six weeks training,
respectively, but increased in some subjects aer eight weeks
training.Fortheletemporalregion,theGSIdecreased
at least by –% aer two weeks training. For the right
temporal region, the GSI decreased at least by –% aer
four weeks training, but there was inconsistent variation
aer the two weeks training. For the central region, the GSI
increased in varying degrees aer two weeks training. For
the occipital region, there were no consistent changes during
Pilates training. For the whole area of the brain, the GSI
decreased slightly aer two weeks training but decreased at
least by % aer four weeks training.
4. Discussions
In this study, we used the resting-state EEG recording to
investigate the eects of the Pilates training on the brain EEG.
e results showed that the Pilates training could increase
the power of the brain alpha rhythm and reduce the synchro-
nization strength of alpha rhythm in the frontal and temporal
regions. ese ndings may support that the Pilates training
maybe benecial for improving brain function because the
alpha rhythm and its synchronization are associated with
the human brain higher function such as intelligence. ese
results suggest that Pilates training may be helpful for the
intervention of brain degenerative diseases and cogitative
dysfunction rehabilitation. Future study will demonstrate this
hypothesis.
Human EEG activity reects the synchronization of cor-
tical pyramidal neurons. Alpha rhythm in the spontaneous
EEG signals is an important predictor of the ecacy of
cortical information processing during cognitive and sen-
sorimotor demand []. Alpha rhythm is oen considered
as one of the indicators of the brain function and has a
signicant correlation with performance on memory tasks
[], and the alpha power is considered as an important
parameter to represent neural activities and processing
mechanisms []. Although the exact mechanisms of alpha
Computational and Mathematical Methods in Medicine
rhythm generation and its functional signicance are not
understood completely so far, there is increasing evidence
that synchronized oscillatory activity in the cerebral cortex
is essential for spatiotemporal coordination and integration
of activity of anatomically distributed but functionally related
neural elements []. Alpha power was positively correlated
with intelligence variables, while some lower frequency bands
negatively correlated with them []. e higher the absolute
amplitude or power of the EEG, the stronger the background
neural synchronization, then the better the cognitive per-
formance [],andthehighertheIQ[]. Lower alpha
power is associated with many diseases, such as obsessive-
compulsive disorder [], Down’s syndrome [], Alzheimer’
[], and restless legs syndrome []. Patients with these
diseases showed intelligence, memory loss, and alpha rhythm
abnormalities []. ere is also a correlation between alpha
power and intelligence []. Cortical neural synchronization
at the basis of eye-closed resting-state EEG rhythms was
enhancedinelitekarateathletes[]. In this study, the alpha
peak power was increased during the Pilates training, which
suggests the increased neural network activity and perhaps
the intelligence during the Pilates training.
Previous study found that right postcentral gyrus and
bilateral supramarginal gyrus were sensitive to the motor skill
training [], and the functional connectivity in the right
postcentral gyrus and right supramarginal gyrus strength-
ened from week to week and decreased from week to
week . e ndings in these case studies are very similar
totheaboveresults,andthefunctionalconnectivitychanges
based on the resting-state EEG recordings are associated with
motor skill learning. Another similar study also demonstrates
that the frontoparietal network connectivity increased one
week aer two brief motor training sessions in a dynamic
balancingtask[], and there is an association between
structural grey matter alterations and functional connectivity
changes in prefrontal and supplementary motor areas. e
GSI is a synchronization method of reecting the multichan-
nel synchronization strength. As shown in Figure ,theGSI
values of the alpha rhythm decreased in varying degrees over
the frontal and temporal regions, increased over the central
region,anddecreasedoverthewholebrainforallcasesaer
two weeks training. e frontal and temporal regions are
associated with cognition (i.e., attention and planning), and
the central region is motor related. Because the Pilates can
improve the balance, control, and muscle strength [], the
GSI of alpha rhythm in the frontal and temporal regions
decreased when the subjects were in the resting state, in
which the subjects were in a very relaxed condition, without
attention and planning procession. e reduction of the
synchronization strength in those regions can support what
is mentioned above. is study demonstrates that the Pilates
training may improve the function of control.
Acknowledgments
is research was funded in part by the National Science
Fund for Distinguished Young Scholars () and by the
National Natural Science Foundation of China ().
References
[] K. Caldwell, M. Harrison, M. Adams, and N. T. Triplett, “Eect
of Pilates and taiji quan training on self-ecacy, sleep quality,
mood, and physical performance of college students,” Journal of
Bodywork and Movement erapies, vol. , no. , pp. –,
.
[]V.Gladwell,S.Head,M.Haggar,andR.Beneke,“Doesa
program of pilates improve chronic non-specic low back
pain?” Journal of Sport Rehabilitation,vol.,no.,pp.–,
.
[] N. H. Turner, “Simple Pilates techniques for back and abdomen
muscles,” Exercise: Pilates & Yoga, , http://w ww.helium
.com/.
[] K.S.Keays,S.R.Harris,J.M.Lucyshyn,andD.L.MacIntyre,
“Eects of pilates exercises on shoulder range of motion, pain,
mood, and upper-extremity function in women living with
breast cancer: a pilot study,” Physical erapy,vol.,no.,pp.
–, .
[] D. Curnow, D. Cobbin, J. Wyndham, and S. T. B. Choy, “Altered
motor control, posture and the Pilates method of exercise
prescription,” Journal of Bodywork and Movement erapies,
vol. , no. , pp. –, .
[]E.G.Johnson,A.Larsen,H.Ozawa,C.A.Wilson,andK.
L. Kennedy, “e eects of Pilates-based exercise on dynamic
balance in healthy adults,” Journal of Bodywork and Movement
erapies,vol.,no.,pp.–,.
[] J.M.Schroeder,J.A.Crussemeyer,andS.J.Newton,“Flexibilty
and heart rate response to an acute Pilates reformer session,”
Medicine and Science in Sports and Exercise,vol.,no.,article
S, .
[]N.A.Segal,J.Hein,andJ.R.Basford,“eeectsofpilates
training on exibility and body composition: an observational
study,” Archives of Physical Medicine and Rehabilitation,vol.,
no.,pp.–,.
[] C.Lange,V.B.Unnithan,E.Larkam,andP.M.Latta,“Max-
imizing the benets of Pilates-inspired exercise for learning
functional motor skills,” Journal of Bodywork and Movement
erapies,vol.,no.,pp.–,.
[] B. J. May, “Mobility training for the older adult,” Topics in
Geriatric Rehabilitation,vol.,no.,pp.–,.
[] W. McNeill, “Decision making in Pilates,” Journal of Bodywork
and Movement erapies,vol.,no.,pp.–,.
[] W. McNeill, “Neurodynamics for Pilates teachers,” Journal of
Bodywork and Movement erapies,vol.,no.,pp.–,
.
[] A. Delorme and S. Makeig, “EEGLAB: an open source toolbox
for analysis of single-trial EEG dynamics including indepen-
dent component analysis,” Journal of Neuroscience Methods,vol.
,no.,pp.–,.
[] J. E. Seem, “Using intelligent data analysis to detect abnormal
energy consumption in buildings,” Energy and Buildings,vol.,
no. , pp. –, .
[] D.Cui,X.Liu,Y.Wan,andX.Li,“Estimationofgenuineand
random synchronization in multivariate neural series,” Neural
Networks,vol.,no.,pp.–,.
[] K. T. Dolan and M. L. Spano, “Surrogate for nonlinear time
series analysis,” Physical Review E,vol.,no.,part,Article
ID , pages, .
[] V. K. Lim, J. P. Hamm, W. D. Byblow, and I. J. Kirk, “Decreased
desychronisation during self-paced movements in frequency
Computational and Mathematical Methods in Medicine
bands involving sensorimotor integration and motor function-
ing in Parkinson’s disease,” Brain Research Bulletin,vol.,no.
–, pp. – , .
[] E. A. Golubeva, Individual Characteristics of Human Memory:
A Psychophysiological Study, Pedagogika, Moscow, Russia, .
[] T. Liu, J. Shi, D. Zhao, and J. Yang, “e relationship between
EEG band power, cognitive processing and intelligence in
school-age children,” Psychology Science Quarterly,vol.,no.
, pp. –, .
[] A. Anokhin and F. Vogel, “EEG rhythm frequency and
intelligence in normal adults,” Intelligence,vol.,no.,pp.–,
.
[] R. G. S chmid,W. S. Tirsch, and H. Scherb, “Correlation between
spectral EEG parameters and intelligence test variables in
school-age children,” Clinical Neurophysiology, vol. , no. ,
pp.–,.
[] W. Klimesch, “EEG and theta oscillations reect cognitive and
memory performance: a review and analysis,” Brain Research
Reviews,vol.,no.-,pp.–,.
[] R. W. atcher, D. North, and C. Biver, “EEG and intelligence:
relations between EEG coherence, EEG phase delay and power,”
Clinical Neurophysiology,vol.,no.,pp.–,.
[] Y. W. Shin, T. H. Ha, S. Y. Kim, and J. S. Kwon, “Association
between EEG power and visuospatial function in obsessive-
compulsive disorder,” Psychiatry and Clinical Neurosciences,vol.
,no.,pp.–,.
[] O.Devinsky,S.Sato,R.A.Conwit,andM.B.Schapiro,“Relation
of EEG background to cognitive function, brain atrophy,
and cerebral metabolism in Down’s syndrome. Age-specic
changes,” Archives of Neurology,vol.,no.,pp.–,.
[] D. Arnaldi, G. Rodriguez, and A. Picco, “Brain functional net-
work in Alzheimer’s disease: diagnostic markers for diagnosis
and monitoring,” International Journal of Alzheimer’s Disease,
vol. , Article ID , pages, .
[] S¸. Akpinar, “e primary restless legs syndrome pathogene-
sis depends on the dysfunction of EEG activity,” Medical
Hypotheses, vol. , no. , pp. –, .
[] C. Babiloni, N. Marzano, M. Iacoboni et al., “Resting state
cortical rhythms in athletes: a high-resolution EEG study,” Brain
Research Bulletin,vol.,no.,pp.–,.
[] L. Ma, S. Narayana, D. A. Robin, P. T. Fox, and J. Xiong,
“Changes occur in resting state network of motor system during
weeks of motor skill learning,” NeuroImage,vol.,no.,pp.
–, .
[] M. Taubert, G. Lohmann, D. S. Margulies, A. Villringer, and
P. Ragert, “Long-term eects of motor training on resting-state
networks and underlying brain structure,” NeuroImage,vol.,
no. , pp. –, .
