A Preliminary Study about the Music Inﬂuence on EEG and ECG

Signals

Manuel Merino-Monge, Isabel M. Gómez-González, Juan A. Castro-García,

Alberto J. Molina-Cantero and Roylán Quesada-Tabares

Departamento de Tecnología Electrónica, Universidad de Sevilla, Spain

Keywords:

Music, Electrocardiogram, Electroencephalogram, Signal Processing.

Abstract:

In this work, music is used to elicit emotions and the impact produced by it on the electrocardiogram and

electroencephalogram signals is measured. Test consists a sequence of 12 songs where each one is played

during 1 minute. Songs were grouped in 4 sets based on pleasant/activation level. In this preliminary study, 6

male subjects realized the trial. Individuals scored each song using Self-Assessment Manikin (SAM) survey.

Biosignal parameters were analyzed with Kruskal-Wallis test (KWT). Although the sample of the subjects

on whom the test was performed is small, signiﬁcant variation is observed in 3 parameters extracted from

the electrocardiogram when features are grouped using SAM values from survey ﬁlled in by subjects. These

parameters show an increasing of heart rate with arousal level and when songs are not totally matched with

individual preferences. The use of information extracted from biosignals in therapies for individuals with low

interaction is proposed for future studies.

1 INTRODUCTION

The potential of music to evoke emotions make it

a valuable tool in multiple situations. One of the

most interesting applications is music therapy whose

goal is to increase awareness and attention, promote

sensory processing, create a feeling of enjoyment

and develop sense of autonomy and control (Adler

et al., 2017). In (Stephenson, 2006) a paper review

is done about aims and methodologies used by mu-

sic therapists working with individuals with severe

disabilities; music therapy sessions, when designed

in collaboration with educators, may supply a fra-

mework for eliciting and practicing communication

abilities. In (Bradt et al., 2013) a review is done

about the effect of music in in coronary heart dise-

ase (CHD), result shows that music may have a ther-

apeutic effect on anxiety in individuals with CHD,

mainly those with a myocardial infarction; anxiety-

reducing effects are greatest when people can select

which music to listen to; moreover, music may im-

proved systolic blood pressure, heart rate (HR), res-

piratory rate, quality of sleep and pain in persons

with CHD. In another plane it can be found appli-

cations that help us to classiﬁed music according to

our mood, for instance in Spotify there are list ac-

cording to different emotional situations in our daily

life

. In (Thoma et al., 2012), songs that were emo-

tionally congruent with individual’s mood were pre-

ferred, such that emotion-regulation styles affect the

selection of song characterized by determinate emoti-

ons. Music plays a role in emotion that a ﬁlm arise,

different perspectives of this can be found in (Ku-

chinke et al., 2013).

There are different strategies that allow to charac-

terize the music with respect to the emotion it arou-

ses. One of them is the evaluation of users. In or-

der to accomplish this it can be used two models for

emotions representation: dimensional and categori-

cal. Dimensional representations used by psycholo-

gists often employ a n-dimensional space to repre-

sent emotions (commonly 2 or 3-dimensional). The

valence-arousal (V-A) representation is one of most

used example of emotional space (Russell, 1980). Va-

lence indicates positive versus negative emotion, and

arousal indicates emotional intensity. The categorical

model uses 6 basic emotions from which the others

can be derived. Another strategy is to extract features

from the audio, the parameter values can determine

the kind of emotion that the music is able of eliciting.

In (Laurier et al., 2012) a mapping between musi-

Accessed on April, 2018: https://open.spotify.com/

view/mood-page

100

Merino-Monge, M., Gómez-González, I., Castro-García, J., Molina-Cantero, A. and Quesada-Tabares, R.

A Preliminary Study about the Music Inﬂuence on EEG and ECG Signals.

DOI: 10.5220/0006960001000106

In Proceedings of the 5th International Conference on Physiological Computing Systems (PhyCS 2018), pages 100-106

ISBN: 978-989-758-329-2

cal features (tempo, mode, harmony, loudness, pitch,

etc.) an emotions categories (happiness, sadness, an-

ger, fear, tenderness) is reported, each independent

parameter is presumably insufﬁcient to decide about

one emotion; on the contrary this may need a lush of

musical descriptors. Many studies have demonstrated

that emotions from music are not too subjective, in-

deed within a common culture, the responses can be

greatly consonant among listeners, such that it may

be possible to replicate this in machines. The goal in

(Laurier et al., 2012) is built a system to assess mu-

sical emotions from a song. For this, supervised ma-

chine learning techniques are used.

Related with these concepts in (Fritz et al., 2009) a

cross-cultural study is done. Two sets of subjects par-

ticipated: native African population (Mafa) and Wes-

tern population. Each group listened the music of the

other respective culture is done. The skill to identify

three basic emotion (joy, sad, fear) from Western mu-

sic is investigated in experiment 1. Results show that

emotions from Western songs are universally recog-

nized (Mafa identiﬁed the three basic emotions). The

second experiment analyzed pleasantness levels chan-

ges due to spectral manipulation of music in both sub-

ject sets. Several spectral features were altered, like

sensory dissonance. The manipulated songs were un-

preferred with respect to original versions, such that

consonance and dissonance of the music may univer-

sally inﬂuence in the pleasantness level.

In (Vieillard et al., 2008) the aim is validated 56

musical extracts.The stimuli were composed with ﬁlm

genre music. These transmitted four emotions depen-

ding of music features (happy, sad, threat and peace-

fulness), so the study provides suitable material for

research on emotions. In Ekman’s classiﬁcation sets

happiness, sadness and threat as basic emotions (Ek-

man et al., 1972). The fourth emotion, peacefulness,

was added as oppositeness to threat. These emotions

can be deﬁned in the 2-dimensional space from va-

lence and arousal model.

In (McAdams et al., 2017) is said that ” Of interest

to both music psychology and music informatics from

a computational point of view is the relation between

the acoustic properties that give rise to the timbre at

a given pitch and the perceived emotional quality of

the tone. Musician and non musician listeners heard

137 tones generated at a set dynamic marking (forte)

playing tones at pitch class D across each instru-

ment’s whole pitch interval and with several playing

techniques for standard orchestral instruments drawn

from the brass, woodwind, string, and pitched per-

cussion families”. They scored each tone on six

analogical-categorical scales in terms of valence (po-

sitive/negative and pleasant/unpleasant), energy arou-

sal (awake/tired), tension arousal (excited/calm), pre-

ference (like/dislike), and familiarity. Twenty-three

audio descriptors from the ”Timbre Toolbox” were

processed for each audio and analyzed in two ways

(Peeters et al., 2011): linear partial least squares re-

gression and nonlinear artiﬁcial neural net modeling.

These two analyses coincided in terms of the signi-

ﬁcance of various audio descriptors in revealing the

emotion ratings, but some differences were found,

such that, distinct acoustic properties are being sug-

gested.

In (Soleymani et al., 2013) a dataset contains 1000

songs, each one annotated by a minimum of 10 sub-

jects is presented, which is larger than many currently

available music emotion dataset. This study supplies

a dataset for music emotion recognition research and

a baseline system. The dataset consists entirely of cre-

ative commons music from the Free Music Archive,

which as the name suggests, can be shared freely wit-

hout penalty.

The aims of (Rodà et al., 2014) are: check how

music excerpt are grouped as a function of the con-

straints applied to the stimuli; to study which dimen-

sions, accompanied by valence and arousal, can be

employed to represent emotional features of music; to

establish computable musical parameters related with

those dimensions in classiﬁcation activities. The uses

of verbal labels to express emotions is avoided. Parti-

cipant were asked to completely focused on their own

feelings from musical extracts and to group that trans-

mitted similar subjective emotions.

During recent years neuroscientiﬁc research on

music-evoked emotions have increased and in (Koel-

sch, 2014) it can be found a recompilation of studies

of brain structures involve in this. In this work is esta-

blished that the emotional effects caused by music can

be motivated by memory associated with music but a

part of them are induced only by the music itself. In

the previous works, the emotions aroused by the mu-

sic were evaluated using two sources of information:

on the one hand, the extraction of the musical cha-

racteristics of the audios and on the other, the testing

of the users where the feeling of being causes a cer-

tain piece of music This deﬁnition can be made using

either the dimensional representation of the emotions

or the categorical one. One way to objectively mea-

sure the emotion desperate for music is to measure the

physiological response that the hearing causes.

The objective of (Goshvarpour et al., 2016) is to

propose an accurate emotion recognition methodo-

logy. To this end, a novel fusion framework based on

wavelet transform, and matching pursuit (MP) algo-

rithm is chosen. Electrocardiogram (ECG) and galva-

nic skin response (GSR) of 11 healthy students were

A Preliminary Study about the Music Inﬂuence on EEG and ECG Signals

101

collected while subjects listened to emotional music

clips in (Vieillard et al., 2008), after the section, the

subjects were asked to ﬁll in the questionnaire for the

evaluation of induced emotions. To describe emoti-

ons, three schemes were adopted: two-dimensional

model (ﬁve classes), valence (three classes), and arou-

sal (three classes) based emotion categories. Sub-

sequently, the probabilistic neural network was app-

lied to classify affective states. The experiments in-

dicate that the MP-based fusion approach outperform

the wavelet-based fusion technique or methods using

only ECG or GSR indexes. Considering the proposed

fusion techniques, the maximum classiﬁcation rate of

99.64% and 92.31% was reached for the fusion met-

hodology based on the MP algorithm (ﬁve classes of

emotion) and wavelet-based fusion technique (three

classes of valence), respectively. In (Goshvarpour

et al., 2017) results are improved to 100%.

In (Wagner et al., 2005), the most important stages

of a fully implemented emotion recognition system

including data analysis and classiﬁcation is discussed.

For recording biosignals in different affective states, a

music induction method is used based on four songs

chosen by the users taking into account that can pro-

voke some special memories. Four-channel biosen-

sors are utilized to record electromyogram, electro-

cardiogram, skin conductivity and respiration chan-

ges. After several parameters were calculated from

the raw signals. Linear discriminant function, k-

nearest neighbor and multilayer perceptron were app-

lied together with feature reduction methods. Correct

classiﬁcation rates of about 92% were reached for all

three classiﬁers.

In this work, a preliminary study is carried out

with two objectives. The ﬁrst target is investigating

if the emotions elicited by the music can be detected

in the physiological signals, that is, if it is possi-

ble setting a link between physiological changes and

the pleasant/agitation levels causes to listening music.

For this purpose, parameters of the recorded signals

will be established and an evaluation of their signi-

ﬁcance will be made. The second goal is evaluating

if the background of the subject in terms of experien-

ces, culture and preferences can inﬂuence the emotion

aroused by music.

In this ﬁrst evaluation, the chosen stimuli were

songs not speciﬁcally composed to awaken emotions

and which have been previously evaluated in terms of

valency and arousal by other subjects. The evalua-

tion is pending, making use of other types of stimuli.

From songs with which the subjects are more fami-

liar to synthesized music with certain values in the

audio parameters that facilitate the elicitation of cer-

tain emotions.

Table 1: Selected songs and their affective values.

Group File Valence Arousal

1 88 5.5 6.7

1 102 5.4 6.5

1 898 5.5 6.4

2 127 5.3 3

2 979 5.5 3.1

2 686 5.5 3.4

3 227 3.5 3.1

3 297 3.6 3.1

3 530 3.5 3.3

4 161 3.5 6.3

4 729 3.5 6.4

4 987 3.6 6.3

2 METHODOLOGY

The experimentations took place in a small corner,

visually isolated, of 235cm of width and 140cm of

depth of a room. A comfortable temperature was

kept in (22–24

◦

) with sound environment of 29 dB

and light lighting to reinforce subject’s focus on the

display (around of 217 lux

). Individuals were se-

ated on a padded chair placed 120cm away from a

18.5” monitor (aspect ratio 4:3) with a resolution of

1280 ×1024 pixels and 32 bits of color. Monitor was

aligned with individual head. Each subject was asked

to attend around 18-minute session. The recording in-

terval was split in 2 parts (ﬁgure 1): the initial rest pe-

riod of 2 minutes and 12 blocks of songs. Each song’s

block is compounded of a initial recovery (30s), 1 mi-

nutes of audio and ﬁlling in emotional survey as soon

as possible (time limitless). A black screen with a

gray cross is shown in the display center during non-

survey periods. Four sets of 3 songs were selected

from (Soleymani et al., 2013) (ﬁgure 2 and table 1).

Songs of each set have similar V-A values. Domi-

nance values were ignored based on circumplex mo-

del of affects (Posner et al., 2005), where valence and

arousal are enough to evaluate physiological changes

with behavioral, cognitive activity, and development

studies. On the other hand, survey periods consist in

scoring valence and arousal levels of previous song in

integer scale from 1 (unpleasant/deactivation) up to 9

(pleasant/activation).

Measurement was done using a Samsung Galaxy

S5 and Sound Meter App (available on April, 2018:

https://play.google.com/store/apps/details?id=coocent.app.

tools.soundmeter.noisedetector).

Measurement was done using a Samsung Galaxy S5

with its hidden menu.

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

102

Figure 1: Test scheme.

Figure 2: Selected songs and the other ones from music

database.

2.1 Subjects

The trials were conducted with 6 healthy man volun-

taries aged between 21 and 61 years old (average=43;

standard deviation=15; no woman voluntaries).

2.2 Data Acquisition

Three different biosignals were simultaneously recor-

ded during the tests: ECG, GSR, and electroencepha-

logram (EEG). The electrode locations are shown in

the ﬁgure 3. The two ﬁrst ones were recorded utilized

Arduino UNO and an own design platform connected

to Arduino (Molina et al., 2017) employed Ag/AgCl

electrodes with self-adhesive/conductive gel. Ar-

duino was programmed the library developed by

(Molina-Cantero et al., 2018). Sampling rate was set

to 256 Hz. ECG employed a monopolar assembly,

and GSR assembly was placed on the medial pha-

lanx of index and middle ﬁngers. EEG was recor-

der from Muse device (Krigolson et al., 2017) with

220-Hz sampling rate. The ground electrode is lo-

cated in Fpz, and the recorded channels were: Fp1,

Fp2, TP9, and TP10. These positions are suited for

the goal of this study because frontal positions are

linked with positive/negative emotions(Alves et al.,

2008), while temporal regions are associated audi-

tory processing(Nunez and Srinivasan, 2006). ECG

and EEG data were respectively ﬁltered applied a har-

dware bandpass ﬁlter with 0.1-30 and 1-60 Hz, whe-

reas non ﬁlter was applied to GSR data. On the ot-

her hand, EEG data must face the presence of several

Figure 3: Electrode locations: a) ECG; b) GSR; c) EEG.

artifacts, such that environmental noise, drift, blink,

saccadic eye movement, face muscle contraction, and

heart rate. A bandpass ﬁlter between [0, 8)Hz and a

notch ﬁlter (48, 52)Hz to reduce these problems were

applied to EEG signals.

2.3 Biosignal Features

EEG signal was divided in 3 frequency bands:

(8,14]Hz is α, (14,30]Hz is β, and (30, 45]Hz is γ.

α wave is mainly related with occipital region with

awaken, relaxation, and no-open-eye states; β rhythm

is mainly linked with moderate cognitive activity in

central and frontal areas; and γ band is associated with

high cognitive requisites and information processing.

Extracted band parameters are congregated in table 2.

Normalized power of band (NPB) is the normalized

power of the band, relative power (RP) means the

percentage of the band with respect to the total po-

wer of EEG bands, power spectrum centroid (PSC)

corresponds to spectral centroid that is the weighted

mean of the frequencies present in the band, that is,

the ”center of mass” of this one, power spectrum de-

viation respect to the centroid (PSDC) is the gather-

ing of spectrum around the PSC in the band, rather

spectral deviation, and ﬂatness (FL) means the rand-

omness level of signal whose value is in the interval

[0, 1], so that 1 corresponds to a white noise, and 0

indicates spectrum is concentrated in a small number

of harmonics. Notice that value of all features is in

the interval [0, 1].

The RR segments are obtained from the ECG sig-

nal through the algorithm based on lower envelope

developed in (Merino et al., 2015). Different para-

meters are extracted from heart rate and they may be

classiﬁed in two groups: information based on tem-

poral analysis (Sörnmo and Laguna, 2005) and shape

waves. The more common calculated parameters of

the ﬁrst group are the standard deviation of RR in-

tervals (SDNN), the square root of the mean squared

difference of successive RR segments (RMSSD),the

proportion of RR intervals that differ by more than

50ms (pNN50), and the width of the minimum square

difference triangular interpolation of the highest peak

A Preliminary Study about the Music Inﬂuence on EEG and ECG Signals

103

Table 2: EEG Band Parameters. x ∈

{

α, β, γ

}

Parameter Equation

Normalized power

of band (NPB)

NPB

∑

f ∈x

PSD( f )

Relative power

(RP)

NPB

∑

y=δ

NPB

Power spectrum

centroid (PSC)

∑

f ∈x

f ·PSD( f )

NPB

= max(x) −min(x)

PSC

(FC

−min(x))

Power spectrum

deviation respect

to the centroid

(PSDC)

( f ) =

( f −min(x))

( f ) = PSD( f ) ·(NF

( f ) −PSC

)

PSDC

∑

f ∈x

( f )

NPB

Flatness (FL)

∏

f ∈x

n·

√

PSD( f )

NPB

of the histogram of all RR intervals (TINN). These

are used to determine the variability over a short

time period to obtain the inﬂuence of parasympat-

hetic nervous system. Important information about

quick random changes of heartbeat is extracted from

pNN50. The second feature set are: amplitude dis-

tance between Q and S waves, Q-S time, R-Q slope,

S-R slope and the kurtosis and skewness of them and

RR segments. Finally, the median of RR segments

(MRR) was obtained too. This measure is based on

the histogram too, it is simple but not as common as

the other measures based in HR.

GSR data were rejected due to saturation pro-

blems.

3 RESULTS

The outlier values were avoided in our analysis using

the interquartile-range method, that is, the values out

of [Q1 - 1.5(Q3 - Q1), Q3 + 1.5(Q3 - Q1)] were eli-

minated, where Q1 and Q3 are the lower and upper

quartiles respectively.

We are interested in discovering how the biosig-

nals features change through the different musics due

to emotions elicited by them. Therefore, the KWT

with 0.05 statistical signiﬁcance (p-value) is applied

to the parameters. Three analyses were realized. In

the ﬁrst one, data were grouped based on selected

song sets (ﬁgure 2), that is, statistical analysis compa-

res variations of features sorted by 4 different groups

(onwards, KWT4), this means that in this analysis

the subjects’ particular characteristics have not been

taken into account, only the music itself. We were

interested in studying whether the emotion elicited

by the music depends on the user’s background or

not, that is why the two other analyzes have been

proposed. In these ones the features are categorized

base on subjects’ V-A values. The mean of the sco-

res (valence = 5.81 ±2.20; arousal = 3.03 ±1.96)

splits features in 2 categories: low (V1/A1) and high

(V2/A2). Statistical analyses compare variations of

features sorted by 2 different labels in each affective

axis (onwards, KWT2V and KWT2A for valence and

arousal groups respectively). Also, KWT analysis

was applied between baseline and low (V1/A1) and

high (V2/A2) affective groups.

The expected behavior is signiﬁcant difference be-

tween groups in each analysis. However, EEG data

were non signiﬁcant for all KWT analysis, while

ECG only three features have signiﬁcant variations

(ﬁgure 4): skewness of RR segments in the KWT2V

analysis, and MRR and R-S slope in the KWT2A ana-

lysis. Positive skewness value indicates a bias to lo-

wer RR segment values, that is, HR is increased when

a song is not totally matched with subject preferen-

ces. Similar meaning is linked with MRR. A smal-

ler value of RR segments is associated with a higher

activation, while these increase with a greater calm

level. This fact coincides with a previous study focu-

sed on stress effect (Monge et al., 2014). Likewise,

the signiﬁcant of the R-S slope may be caused by

increasing of HR when activation level is increased.

On the other hand, affective groups are not signiﬁ-

cantly different with respect to baseline. Songs with a

less pleasant level (V1) show a greater skewness than

baseline, while more pleasantness musics (V2) draw

normal distribution (similar to baseline). Also, songs

with lower and higher arousal levels exhibit smaller

and greater MRR values with respect to baseline, and

R-S slope is smaller in A1 group ,but almost equal

in A2 group. This fact may be caused by subjects’

expectation about what kind of music they will be lis-

tened.

4 CONCLUSIONS

The target of this study was to analyze the variations

of EEG and ECG and how these are related with sub-

ject’s music preferences. The behavior of EEG fea-

tures are similar in all cases, such that, none of them

show signiﬁcant variations, while three of ECG para-

meters draw variations due to song if results are grou-

ped according to user punctuations in arousal and va-

lence. Despite, the reduced number of subjects limit

the signiﬁcant of results.

In future work, the number experimental subjects

of test must be increasing for getting a stronger sta-

tistical analysis. On the other hand, different musi-

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

104

L V1 V2

-0.8

-0.6

-0.4

-0.2

0.2

0.4

0.6

0.8

VAlence: Skewness of RR segments

L A1 A2

-50

-40

-30

-20

-10

mV/s#

Arousal: R-S Slope

L A1 A2

750

800

850

900

950

1000

1050

1100

1150

miliseconds#

Arousal: Median of RR segments

Figure 4: Boxplots of signiﬁcant features from KWT statistical analyses. Left ﬁgure is the variations of skewness of RR

segments; middle ﬁgure contains the changes in median of RR segments; right ﬁgure is the variations of slope of R-S wave.

Symbol meanings: “L” indicates baseline, “V” identiﬁes valence, “A” means Arousal, and numbers “1” and “2” indicate group

of lowest and highest levels respectively.

cal stimulus may be employed with the objective of

evaluating inﬂuence musical characteristics by them-

selves without considering the proﬁle of the subject.

Based on the results conducted in this work, the in-

formation provided by the physiological signals will

be used to developed an adaptive music system will to

improve interaction capacity of individuals with disa-

bilities, mainly children with special needs.

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