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International Journal of Bifurcation and Chaos, Vol. 26, No. 1 (2016) 1650001 (9pages)
c
World Scientific Publishing Company
DOI: 10.1142/S0218127416500012
Modulation of EEG Theta Band Signal
Complexity by Music Therapy
Joydeep Bhattacharya∗
Department of Psychology, Goldsmiths,
University of London, New Cross, SE14 6NW, London, UK
j.bhattacharya@gold.ac.uk
Eun-Jeong Lee
Institute for Medical Psychology, University Hospital,
University of Heidelberg, Bergheimer Strasse 20,
D-69120 Heidelberg, Germany
Received November 14, 2011; Revised April 2, 2012
The primary goal of this study was to investigate the impact of monochord (MC) sounds, a
type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained
from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal
complexity values for monochords with those for progressive muscle relaxation (PMR), an alter-
native therapy for relaxation. Forty cancer patients were randomly allocated to one of the two
relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were
recorded during the first and last sessions. EEG signals were analyzed by applying signal mode
complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed
a modulation of complexity of beta-2 band (20–29Hz) at midfrontal regions, but only MC group
showed a modulation of complexity of theta band (3.5–7.5Hz) at posterior regions. Therefore,
the neuronal complexity patterns showed different changes in EEG frequency band specific com-
plexity resulting in two different types of interventions. Moreover, the different neural responses
to listening to monochords and PMR were observed after regular relaxation interventions over
ashorttimespan.
Keywords: EEG; complexity; oscillations; theta band; music therapy; PMR.
1. Introduction
The human brain is often considered as the most
complex object in the known universe, and music,
with all its complexities and richness, is consid-
ered as one of the most unique characteristics of
our species. Music is present in all cultures, and
listening to (and performing) music is consistently
rated as one of the most pleasurable experiences
in our lives [Vuust & Kringelbach, 2010]. Listening
to music can engage a multitude of brain regions
including anterior cingulate cortex, hippocam-
pal formation, and dopaminergic neural networks
[Koelsch, 2010]. In particular, changes in brain
activity were shown during strong emotional experi-
ence induced by the listener’s most preferred music,
and this phenomenon, termed as “chill sensation”,
is perceived as an intensely positive or peak experi-
ence. Intensity of the chill experience was positively
∗
Author for correspondence
Author Contributions: JB developed the complexity index and performed data analysis; EJL performed data collection; JB
and EJL wrote the paper.
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correlated with activations of right thalamus,
anterior cingulate cortex, supplementary motor
area, and insula, and negatively correlated with
activations of right amygdala, left hippocam-
pus/amygdala, and ventral medial prefrontal cor-
tex [Blood & Zatorre, 2001]. Listening to music
also alters the dynamical brain responses, i.e. oscil-
latory component(s) of the electroencephalogram
(EEG) signals, particularly theta frequency band
(3.5–7.5 Hz). For instance, pleasant music elicits
an increase of the frontal midline theta power
[Sammler et al., 2007], and listening to classical
music causes an increase in posterior theta power
[Chan et al., 2008], whereas the relaxation effects
of music are associated with a change in the total
theta power [Kabuto et al., 1993]. Further, broad-
band EEG complexity as measured by the correla-
tion dimension increases with increasingly complex
music and with musical sophistications [Birbaumer
et al., 1996]. Furthermore, EEG responses for music
with fractal or self-similar scaling properties were
associated with reduced correlation dimension and
largest Lyapunov exponent than for music without
scaling properties [Jeong et al., 1998].
It is therefore important to consider to what
extent the results from neurophysiological and neu-
rophenomenological studies on the effects of listen-
ing to music are significant for the clinical usage
of music. Previously, only a few neurobiological
studies have demonstrated the positive effect of
music (listening or playing) in a clinical context,
including in the music therapeutic context, such
as improvements for tinnitus sufferers [Okamoto
et al., 2010], cognitive rehabilitation after a stroke
[Sarkamo et al., 2008], improvement in fine as well
as gross motor skills after a stroke [Altenmuller
et al., 2009], or improvement of speech in patients
suffering from Broca’s aphasia [Schlaug et al., 2009].
Apart from functional improvements, music has also
been used in various clinical contexts to positively
influence patients’ psychological and physiological
states by reducing pain [Nilsson et al., 2003; Tan
et al., 2010], anxiety [Singh et al., 2009], and by
promoting relaxation [Nilsson, 2009]. Typically, in
oncological context, music has been shown to be
effective by reducing anxiety [Burns et al., 2008;
Sabo & Michael, 1996] and side effects [Ezzone
et al., 1998] in patients undergoing chemotherapy.
Despite such success with musical intervention
in clinical settings, the underlying neurophysiolog-
ical responses have rarely been studied, yet neural
correlates to these changes caused by music can
provide deeper insights into the effect of music in
patients. Moreover, these insights can be used as
neuroscientific evidence towards the broader accep-
tance of musical intervention and further establish
the evidence-based use of music and music therapy
as a scientifically supported therapeutic method in
the clinical context [Hillecke et al., 2005]. It is there-
fore necessary to investigate the neural changes
accompanying not only functional improvement but
also psychological and physical support (i.e. the
anxiolytic, analgesic, or relaxing effects) achieved
by listening to music.
The current study aims to fill this gap between
the music psychological, music physiological (neu-
rophysiological), and music therapeutic approaches
to analyzing how patients benefit from the receptive
use of music with a particular focus on listening to
monochord sounds. Monochord is an ancient music
instrument with approximately 30 strings tuned
on the same tone with many induced overtones,
and has been shown to improve the psychological
and physiological states of patients [Rose & Weis,
2008]. Unlike the excerpts of familiar music used
in music therapy [Khalfa et al., 2003], monochord
is rather unfamiliar, and contains minimal musi-
cal parameters, thereby minimizing the involvement
of different music psychological factors (subjective
preference, arousal, etc).
This study focused on neurophenomenological
patterns and on changes in the mode complexity
values in standard EEG frequency bands during
relaxation induced by monochord sounds and com-
pared them to those induced by the progressive
muscle relaxation (PMR) method. PMR is a widely
established technique for relaxation by alternately
tensing and relaxing the muscles [Jacobson, 1938].
Using a recently proposed index, signal mode com-
plexity, it is possible to quantify the complexity of
EEG signals by investigating the constituent oscil-
latory components within standard EEG frequency
bands [Bhattacharya & Pereda, 2010].
2. Materials and Methods
This randomized clinical study has been conducted
at the Women’s Hospital, University of Heidel-
berg, Germany. A total of 43 female patients with
gynaecological cancer receiving chemotherapy were
recruited into the study. Of these, 22 patients were
randomly assigned to the monochord group (MC)
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EEG Complexity in Music Therapy
(mean age: 49, range: 27–55) and 21 patients to the
PMR group (mean age: 51, range: 31–56). During
both relaxation treatments, patients were lying
down, awake but with eyes closed. Both groups
received the recorded intervention (either mono-
chord sounds or instructions for PMR) for 25 min
after a period of verbal introduction (4 min). Each
session was in sync with the onset of chemother-
apy. Patients received individual relaxation treat-
ment sessions — a total of four times over a span
of six months.
During the first and the last treatment sessions,
EEG signals were recorded with 23 Ag/AgCl elec-
trodes attached to the scalp (Fp1, Fp2, F3, F4, F7,
F8, C3, C4, T1, T2, T3, T4, T5, T6, P3, P4, O1,
O2, Fz, Cz, Pz, A1, A2) according to the Inter-
national 10–20 electrode placement system [Jasper,
1958]. All electrode impedances were kept below
10 KΩ. The sampling frequency was 128 Hz. The
EEGs were re-referenced offline to the algebraic
mean of the two earlobe electrodes. During EEG
recording, participants were awake but their eyes
were closed.
Data from five patients were excluded due to
excessive artefacts, and the data of a total of
38 patients were analyzed (MC: n=20,PMR:n=
18). EEG signal at each electrode location was
bandpass filtered in six standard frequency bands:
delta (<3 Hz), theta (3.5–7.5 Hz), alpha (8–12 Hz),
beta 1 (12–19.5 Hz), beta 2 (20–29 Hz), and gamma
(>30 Hz); in this study, we strategically focused our
analysis on theta, alpha and beta-2 bands based
on our earlier analysis (unpublished observation).
The EEG analysis concentrated on two 5 min long
periods within each treatment session: Begin period
(from the beginning phase of a treatment session)
and End period (from the final phase of a treatment
session). These periods were chosen so that the
within-session effect of each relaxation treatment
could be reliably assessed. Within each period, the
data were divided into 30 nonoverlapping epochs
of 10 sec. Epochs with maximum absolute ampli-
tude larger than 75 µV were considered as artefacts
and eliminated from subsequent complexity analy-
sis. The complexity values were computed for indi-
vidual epochs.
The procedure of calculating signal mode com-
plexity is described briefly as follows. Consider an
EEG time series, {x(k),k =1,2,...,N},whichis
normalized to zero mean and unit variance. We form
am×nmatrix,
An=
x(1) x(2) ··· x(n)
x(n+1) x(n+2) ··· x(2n)
.
.
..
.
.....
.
.
x(n(m−1) + 1) x(n(m−1) + 2) ··· x(mn)
and calculate its singular values (σ1,σ
2,...,σ
p,p=
min(m, n)) [Golub & Van Loan, 1996]. By varying
the row length (n), new matrices are formed, and
their singular values are subsequently calculated.
For each configuration of An, the singular values
are linearly mapped to Rnormalized singular val-
ues but preserving the total energy spanned by the
sum of squares of singular values. This way all the
singular values for each matrix configuration were
considered and the associated singular value profiles
were made linearly equivalent to each other. Hence
for Mdifferent values of row length n,Msets of R
singular values are obtained:
{σi,j :i=1,2,...,M;j=1,2,...,R}.
An average singular value profile is obtained as:
ˆσj=1
M
M
i=1
σij .
Earlier we have shown that this average profile
could distinguish chaotic time series from a random
one, and further could be used to characterize phys-
iological signals.
The signal mode complexity, CS, is computed
as:
CS=
R
j=1
j2ˆσj
R
j=1
j2
.
The lower the CSvalue, the higher the likeli-
hood of regularly occurring pattern of synchronized
oscillations. CSis found to be able to detect changes
in the complexity of the signal that is very similar
to the changes in the largest Lyapunov exponent,
a hallmark of chaotic complexity. Further, CSis a
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measure with high reliability that can be applied
to small data sets, and further the method is
assumption-free. See [Bhattacharya & Pereda, 2010]
for further details.
The complexity values were log-transformed
prior to statistical analysis. A mixed factorial
ANOVA was performed with between-subject fac-
tors, group (2 levels: MC and PMR), and within-
subject factors, session (2 levels: Pre and Post),
time (2 levels: Begin-period and End-period), and
regi on (two levels: anterior (Fp1, Fp2, F3, F4, F7,
F8,T1,T2,Fz),andposterior(T5,T6,P3,P4,
Pz, O1, O2)). The statistical significance was set
at p<0.05. All statistical analyses were performed
using SPSS (version 16.0).
3. Results
First, we investigated the CSdifferences across ses-
sions. Figure 1 shows the CSvalues at 21 electrode
regions in three standard EEG frequency bands
(theta, alpha, and beta-2) for both groups during
the first (designated as Pre) and last (designated
as Post) sessions of treatment. At first glance, the
spatial profiles of CSvalues appeared to be similar
between Pre and Post within each group, and fur-
ther the scalp distribution of differences in CSval-
ues (Pre–Post) were quite similar across frequency
bands within each group. However, upon closer
inspection, several interesting features emerged.
The theta band complexity in the MC group was
0.014
0.016
0.018
0.02
0.022
CS
PMR Theta
Pre
Post
Difference
0.014
0.016
0.018
0.02
0.022
Monochord Theta
Pre
Post
0.014
0.016
0.018
0.02
0.022
CS
PMR Alpha
0.014
0.016
0.018
0.02
0.022
Monochord Alpha
0.02
0.03
0.04
0.05
0.06
CS
PMR Beta−2
Fp2 F4 C4 P4 O2 F8 T6 T4 T2 Fz Pz
Fp1F3C3P3O1F7T5T3T1Cz
0.02
0.03
0.04
0.05
0.06
Monochord Beta−2
Fp2F4C4P4O2F8T6 T4T2FzPz
Fp1F3C3P3O1F7T5T3T1Cz
−0.15
−0.1
−0.05
0
0.05
0.1
0.15
Difference
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 1. Signal mode complexity (CS) of three frequency bands (theta, alpha and beta-2) at 21 scalp electrodes in the first
(Pre) and the last (Post) treatment sessions for (a)–(c) the PMR and (d)–(f) the MC groups. Results were averaged across two
periods (Begin and End, see text) within a session. Scalp map adjacent to each plot describes the topographical distribution
of difference (Pre–Post) CSvalues. Red color indicates a decrease in CSin the last session compared to the first session.
Electrodes showing statistically significant (p<0.05, Bonferroni corrected) changes are shown by bigger filled circles.
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EEG Complexity in Music Therapy
considerably lower in the last session compared to
the first session over a multitude of electrode regions
[Fig. 1(d)], but with a stronger emphasis in the pos-
terior region (F(1,19) = 5.15, p=0.03). Interest-
ingly, the PMR group displayed an opposite trend:
theta band complexity increased in the last session
compared to the first one over many brain regions
except frontopolar and midfrontal regions showing
the reverse pattern [Fig. 1(a)]. The alpha band com-
plexity in the MC group was rather similar between
first and last sessions but a trend towards decreased
complexity was found in midfrontal (Fz), right tem-
poral (T4, T2, T1) and occipital (O1, O2) elec-
trode regions [Fig. 1(e)]. In beta-2 band, CSvalue
in midfrontal electrode region was significantly low-
ered in the last session compared to the first session
in the MC group (F(1,19) = 5.66, p=0.02). The
results in the PMR group were quite similar across
frequency bands and the midfrontal region showed
consistently decreased complexity.
Next, we studied the within session CSdiffer-
ences. Figure 2 shows the general tendencies of the
change in theta band complexity values between
Begin (5min period from the beginning phase of a
session) and End (5minperiodfromtheendphase
of a session) periods within both first (Pre) and
last (Post) sessions. Within a session, the PMR
group showed very similar distributions without
any significant difference between the two periods
[Figs. 2(a) and 2(b)], whereas the MC group showed
large significant differences between the two periods
in both anterior (F(1,19) = 5.69, p=0.02) and
in posterior (F(1,19) = 8.80, p=0.00) electrode
regions. In both sessions, CSvalues decreased in
the End period from its values in the Begin period,
and the effect was larger in the anterior region in
the last session than in the first session. Regard-
ing alpha band complexity, the MC group showed
robust significant differences between the two peri-
odsinbothanterior(F(1,19) = 13.03, p=0.00)
and posterior (F(1,19) = 7.28, p=0.01) regions,
where the complexity value was decreased during
the End period compared to the Begin period.
Figure 3 shows the within session CSdiffer-
ences for beta-2 frequency band. Both MC and
PMR groups showed a robust increase of CSin the
frontopolar and midfrontal electrode regions during
the End period as compared to the Begin period
(p<0.00). Scalp maps were quite consistent across
both periods within individual groups.
0.018
0.019
0.02
0.021
0.022
CS
PMR Pre
Begin
End
Difference
0.018
0.019
0.02
0.021
0.022
Monochord Pre
Begin
End
Difference
0.018
0.019
0.02
0.021
0.022
CS
PMR Post
Fp2F4C4P4O2F8T6T4 T2FzPz
Fp1 F3 C3 P3 O1 F7 T5 T3 T1 Cz
0.018
0.019
0.02
0.021
0.022
Monochord Post
Fp2F4C4P4O2F8T6T4T2Fz Pz
Fp1F3C3P3O1F7T5T3T1Cz
−0.02
0
0.02
(a) (c)
(b) (d)
Fig. 2. (a) Signal mode complexity (CS) of theta frequency band at 21 scalp electrodes shown separately for Begin and End
period for the first session of PMR group. Scalp map shows the topographical distribution of difference (Begin–End)CSvalues.
(b) Same as in (a) but for the last session of the PMR group. (c)–(d) Same as in (a)–(b) but for the MC group. Electrodes
showing statistically significant (p<0.05, Bonferroni corrected) changes are shown by bigger filled circles.
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Begin End
0
0.01
0.02
0.03
0.04
0.05
PMR
CS
PMR Begin PMR End
0.04
0.05
0.06
PMR Difference
Begin End
0
0.01
0.02
0.03
0.04
0.05
Monochord
CS
Monochord Begin Monochord End Monochord Difference
−0.1
0
0.1
(a)
(b)
Fig. 3. Signal mode complexity (CS) of beta-2 band at frontopolar and midfrontal electrode regions (Fp1, Fp2, F3, F4, Fz)
for Begin (empty bar) and End (gray bar) period for (a) PMR and (b) MC groups. Results were averaged across sessions
(first and last). Scalp maps on the right of each bar plot show the topographical distribution of CSfor Begin and End period
within each group. Electrodes showing statistically significant (p<0.05, Bonferroni corrected) changes are shown by bigger
filled circles.
4. Discussion
Listening to monochord sounds or practising PMR
to induce relaxation during chemotherapy pro-
duced different complexity profiles of electrical
brain responses of gynaecological patients in oncol-
ogy. The difference was most conspicuous in the
theta frequency band: in the MC group, theta band
complexity decreased both within a session and
across sessions, while no such decrease was observed
in the PMR group. Both groups showed an increase
of beta-2 band complexity within a session.
EEG signals are neither fully deterministic nor
fully stochastic, but rather a mix of both processes
[Lehnertz et al., 1999]. As the degree of stochasticity
or randomness increases or decreases, CSincreases
or decreases [Bhattacharya & Pereda, 2010]. There-
fore, a decrease of CSat an electrode region sug-
gests an increase of orderliness or regularity in the
oscillatory component of the time series recorded by
that electrode. Emergence of locally synchronized
oscillations is one candidate of such enhanced pat-
terned regularity. Hence, in the MC group, the theta
band spectral power was likely to be enhanced at
the end of a session compared to its beginning and
also at the last session compared to the first session.
This is in line with earlier studies showing that the
posterior theta band oscillation is an effective indi-
cator of induction of relaxation [Hari & Naukkari-
nen, 1977; Williams & Gruzelier, 2001]. This clearly
suggests that monochord sounds were quite effec-
tive as an inducer of relaxation even within one ses-
sion. Interestingly, the difference in midfrontal and
frontal theta complexity between End and Begin
period was larger in the second session than in
the first session [Figs. 2(c) and 2(d)]. This possibly
reflects a training effect of the monochord method.
Theta oscillations over midfrontal region are shown
to be modulated by various meditational techniques
[Aftanas & Golocheikine, 2002; Baijal & Srinivasan,
2010]. Training related changes in the MC group
were found also over other brain regions [Fig. 2(d)].
Decrease of signal mode complexity over multiple
brain regions is usually associated with an increase
of neuronal synchronization over these distant brain
regions [Bhattacharya & Pereda, 2010]; therefore
one could infer that the gradual intervention by
monochord sounds elicited a dense functionally con-
nected network.
While theta band effect was exclusive to
the MC group only, beta-2 band complexity was
increased in both MC and PMR groups. The
dimensional complexity of EEG during meditation
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EEG Complexity in Music Therapy
is negatively correlated with theta and positively
correlated with beta frequency band [Aftanas &
Golocheikine, 2002]. Therefore, if our earlier rela-
tionship between signal mode complexity and syn-
chronized oscillation holds, one expects a reduction
of beta band oscillations with both relaxation
methods. Note that the largest increase of beta
band complexity was found over frontal regions
[Figs. 3(a) and 3(b)], suggesting an anxiolytic
response common to both groups as frontal beta
oscillations are inversely correlated with the degree
of anxiety [Begic et al., 2001; Chen et al., 1989].
This is also the first study which has system-
atically analyzed the dynamic brain responses to
monochord sounds in a clinical context. Previously,
only two pilot studies investigated the effects of
body monochord using EEG [Fachner & Rittner,
2004; Sandler et al., 2008]. These studies demon-
strate altered states of consciousness induced by
the body monochord which was associated with an
increase of theta and beta-2 band spectral power
[Fachner & Rittner, 2004]. Both of these stud-
ies are purely exploratory and involve very few
healthy participants. Consequently, no neurophys-
iological account is available on the effect of mono-
chord sounds used in clinical context. Future studies
need to assess whether the relaxing effect or the
altered states as mentioned above in a specific clin-
ical context could offer similar therapeutic effects
to patients.
We acknowledge certain limitations of this
study. Firstly, the current study did not include
a control group. However, it was difficult to
conduct a comparison with a control group, given
that this was a clinical study with gynaecologic
cancer patients who were burdened both physi-
cally and psychologically with their illness. For
obvious ethical reasons, it was questionable to mea-
sure EEG, especially because these female patients
were already suffering from alopecia (a condition of
hair loss due to chemotherapy) and attaching elec-
trodes would therefore mean additional emotional
burden. It would nevertheless be scientifically inter-
esting to make a comparison with a control group,
provided that a study can be designed without
adding stress to the patients. Secondly, the length
of the intervention method should be investigated
more systemically. The process of relaxation and
the patterns of becoming relaxed are situational
and differ across individuals. It will be necessary to
track the changes in complexity (or in other suitably
chosen features) of brain responses over the course
of the intervention to find out the most effective
and efficient length of time for the relaxation inter-
vention and to develop an optimal and individually
adapted music relaxation treatment. Thirdly, one
could consider investigating the post effect after lis-
tening to monochord sounds, potentially increasing
the applicability of music in the clinical context.
Fourthly, the current study did not cover the entire
EEG frequency spectrum; it strategically focused
on three preselected frequency bands after previous
studies, yet other EEG frequency bands like gamma
(>35 Hz) are also shown to be modulated by varied
states of alertness [Aftanas & Golosheykin, 2005;
Sebastiani et al., 2005; Tei et al., 2009]. There-
fore, future research should attempt to establish a
relationship between music mediated relaxation and
signal complexity of higher EEG frequency bands.
Finally, any quantifier of complexity, be it CSor
any other, is, after all, a mathematically derived
index. In order to establish its prognostic value in
determining the efficacy in predicting the success of
an individual relaxation method, it would be essen-
tial to demonstrate that such a quantifier is sys-
tematically related to behavioral measures, thereby
establishing a link between neural and behavioral
responses. In the similar vein, it would also be rel-
evant, for future studies, to correlate changes in
signal mode complexity with peripheral or other
physiological measures (e.g. heart rate, respiration)
related with relaxation.
In summary, this is the first neuroscientific
study of the effect of listening to archaic sounds
(monochord) in patients that demonstrates the
changes in complexity in brain oscillation patterns
in comparison with PMR.
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