Music Generation and Emotion Estimation from EEG Signals for
Inducing Affective States
Kana Miyamoto

Hiroki Tanaka

Satoshi Nakamura

Division of Information Science, Nara
Institute of Science and Technology
Ikoma-shi, Nara, Japanmiyamoto.kana.mk4@is.naist.jp

Division of Information Science, Nara
Institute of Science and Technology
Ikoma-shi, Nara, Japan
hiroki-tan@is.naist.jp

Division of Information Science, Nara
Institute of Science and Technology
Ikoma-shi, Nara, Japans-nakamura@is.naist.jp

ABSTRACT

1

Although emotion induction using music has been studied, theemotions felt by listening to it vary among individuals. In order toprovide personalized emotion induction, it is necessary to predict
an individual’s emotions and select appropriate music. Therefore,
we propose a feedback system that generates music from the continuous value of emotion estimated from electroencephalogram(EEG). In this paper, we describe a music generator and a method of
emotion estimation from EEG to construct a feedback system. First,
we generated music by calculating parameters from the valence
and arousal values of the desired emotion. Our generated music
was evaluated by crowdworkers. The median of the correlation
coefficients between the input of the music generator and the emotions felt by the crowdworkers were valence r=0.60 and arousal
r=0.76. Next, we recorded EEG when listening to music and estimated emotions from them. We compared three regression models:linear regression and convolutional neural network (with/withouttransfer learning). We obtained the lowest RMSE (valence: 0.1807,
arousal: 0.1945) between the actual and estimated emotional values
with a convolutional neural network with transfer learning.

Music is known to induce emotions [6, 18]. Music has been used
to improve health such as elderly people’s mental health [11]. Currently, music therapy is done by music therapists who carefully
observe participant behaviors and decide what kind of music touse. Since musical possibilities include a variety of existing kinds
as well as musical improvisation, therapists are burdened by needing to select music. Therefore, Sourina et al. proposed a systemthat automatically plays music that is designed to induce emotions[9, 17].
The system suffers from two problems. The first is how music
is chosen. The emotions that are promoted when listening to music vary among individuals. The same person may have different
emotional levels depending on the situation. The system needs tochoose music that is appropriate for the individual and the situation.
The second problem is a method that predicts the emotional state ofparticipants. The system needs to observe emotion as diligently as amusic therapist. Hence, Sourina et al. proposed emotion prediction
using EEG of biological signals, and music selection by predicted
emotion to solve these problems. EEG records the brain’s electricalactivity and measures time-series data in multiple channels. There
are various researches using EEG, such as the classification of the
dynamic representation of speaker stance [5] and the detection ofsyntactic violations [10]. EEG is also effective for emotional prediction. The method using EEG has been actively researched for thesystem to recognize emotions such as the classification of emotions[2, 16]. After emotion estimation from EEG, Sourina et al. planned
to compare the desired emotion with the predicted emotion andselect music from music databases. Although it is thought to be possible to induce emotions using this method, it is difficult to express
emotions in detail and to change the music flexibly. Therefore, we
propose a system that expresses emotions as continuous values andgenerates music from EEG.
Emotions are expressed in two-dimensional space [12]. The horizontal dimension is valence, representing a range from pleasant
to unpleasant. The vertical dimension is arousal, representing a
range from activation to deactivation. In our research, these areexpressed as continuous values from 0 to 1 and express emotions indetail. Ehrlich et al. proposed a music generation method to makeparticipants perceive the continuous values of emotions intended
by the music, and they estimated the emotions using EEG while
listening to music created by the method [3]. Our research was
based on their research, and we proposed new methods of music
generation for emotional elicitation and emotion estimation withthree regression models by EEG.

CCS CONCEPTS• Human-centered computing → Interaction design.

KEYWORDS
Electroencephalogram; emotion induction; music generation; brain
computer interface; human computer interaction
ACM Reference Format:
Kana Miyamoto, Hiroki Tanaka, and Satoshi Nakamura. 2020. Music Generation and Emotion Estimation from EEG Signals for Inducing Affective
States. In Companion Publication of the 2020 International Conference on
Multimodal Interaction (ICMI ’20 Companion), October 25–29, 2020, Virtual
event, Netherlands. ACM, New York, NY, USA, 5 pages. https://doi.org/10.
1145/3395035.3425225

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACM
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to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.
ICMI ’20 Companion, October 25–29, 2020, Virtual event, Netherlands© 2020 Association for Computing Machinery.
ACM ISBN 978-1-4503-8002-7/20/10. . . $15.00
https://doi.org/10.1145/3395035.3425225

INTRODUCTION

2

FRAMEWORK OF AN EMOTION-INDUCINGSYSTEM

We propose a feedback system that generates music from the continuous value of emotion estimated from EEG. The music generation
feedback system’s outline, shown in Figure 1, consists of a music
generator that creates appropriate music to emotion elicitation andan emotion estimator that estimates emotions by EEG. First, the desired emotion that specified in the experiment is input to the music
generator. Next, the participant’s current emotion is estimated fromthe EEG while they are listening to music. The difference value
between the estimated emotion and the desired emotion is added
to the previous input of the music generator. The new input to themusic generator is calculated and music is generated again. The
system induces their emotions by repeating this cycle.
In this paper, we describe how to create the music generator
and the emotion estimator to construct the system based on the
research of Ehrlich et al. [3]. We examined a new method of emotion
elicitation using the music generator, and new models such as CNN
for emotion estimation by EEG.
arousalvalenceCalculation of input
to music generator

arousalDesiredemotionvalence
Estimated
emotionEstimated
emotionMusic generator
LoopBe1
MIDI
Calculation of tempo
piano
parameters rhythm
MIDI(MATLAB)
loudness Generation of violin
Music
tempo
rhythmpitch MIDI signals MIDI generation
loudness(MATLAB)(Cakewalk)
cello
pitch
mode
mode

Emotion estimator
arousalvalence

Estimated
emotion

EEG
User
Music

Figure 1: System that generates music from EEG

3MUSIC GENERATOR

We created a music generator using the method of Ehrlich et al.
whose music generator was designed to help the participants perceive the intended emotion of the music [3]. However, we design tochange the emotions being experienced by the participants throughmusic. Based on previous research, the felt and perceived emotions
are different, but it is thought that these emotions are the same or
the felt emotion often appears weaker than the perceived emotion[4, 14]. Therefore, we thought the music generator of Ehrlich etal. might also effectively induce emotions and created it. In addition, we evaluated the music generator and recreated the music
generator to produce music more suitable for emotional elicitation.

3.1

Parameter-based music generation

Previous research generated music by five parameters: tempo, rhythm,
loudness, pitch, and mode [3]. We generated music using the following formula.
tempo : 𝑛𝑜𝑡𝑒 dur = 0.3 − aro ∗ 0.15 ⊂ Rrhythm : 𝑝 (𝑛𝑜𝑡𝑒 = 1) = aro
loudness : 𝑛𝑜𝑡𝑒 vel = 𝑢𝑛𝑖 𝑓 {50, 30 ∗ aro + 60} ⊂ N

pitch :
𝑛𝑜𝑡𝑒 reg =mode : 𝑝 (C3) = 2 ∗ (0.5 − val)𝑝 (C5) = 2 ∗ (val − 0.5)
 C47 − (6 ∗ val) ∈ 1, ..., 7 ⊂ N.

𝑖 𝑓 val < 0.5
𝑖 𝑓 val ≥ 0.5
otherwise

Tempo represents a note’s length in seconds. Rhythm represents
the probability that a note appears. However, in this expression,music isn’t generated when the arousal is 0. Therefore, when thearousal was 0.03 or less, it was set to 0.03 and rhythm was calculated.
Loudness represents the note’s volume. Pitch represents which scale
is used. Mode determines which method to use: 1. Lydian (4th mode),
2. Ionian (1st mode), 3. Mixolydian (5th mode), 4. Dorian (2nd mode),
5. Aeolian (6th mode ), 6. Phrygian (3rd mode), and 7. Locrian (7thmode) [13]. We used a C major chord. The chord symbol changes as
I-IV-V-I for each measure. To generate music, we calculated thesemusic parameters by MATLAB from valence and arousal inputs
between 0 and 1 and generated musical instrument digital interface(MIDI) signals with MATLAB from the music parameters. MIDI
signals were sent to DAW software called Cakewalk using LoopBe1
of virtual MIDI cable software, and music was generated by a piano,a violin, and a cello.

3.2

Evaluation of created music

We evaluated the music produced by the music generator. We investigated whether the music generator designed by Ehrlich et al.
was effective in inducing as well as perceiving emotions.
3.2.1 Assessment methods. Sample pieces of music (15 s) are{val, aro}={0,0}; {0,1}; {0.5,0.5}; {1,0}; {1,1}. The evaluated music (30 s)
are a combination of val=0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1
and aro=0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1. Crowdworkersof CrowdWorks, Inc. listened to the music and evaluated its valence
and arousal using self assessment mannequin (SAM) [1] in nine
steps between 0 to 1 every time.
3.2.2 Perceived emotion by the music generator. 101 crowdworkers
listened to the music and evaluated whether they could perceive theintended emotions in the music. Min-max normalization was used
to rescale the valence and arousal for each crowdworker. We investigated the Pearson’s linear correlation coefficients between the input
to the music generator and the evaluation by each crowdworker.
We excluded data of participants whose correlation coefficientswere negative. The median correlation coefficients were valence
r=0.76 and arousal r=0.86. The evaluation of the music generator
by Ehrlich et al. investigated the median correlation coefficientswere valence r=0.52 and arousal r=0.74.
Figure 2 shows the average evaluations of crowdworkers for thevalence and arousal input to the music generator. The horizontal
axis is the input of the valence, and the vertical axis is the input of
the arousal to the music generator. The colors represent the average
evaluations of crowdworkers. Figure 2 (a) of valence evaluation
tends to have difficulty perceiving a high valence when the arousal
input to the music generator is low because the colors are notaligned vertically. Figure 2 (b) of arousal evaluation does not seemto be affected by the valence input to the music generator because
the colors are aligned horizontally. The figures were similar to those

of Ehrlich et al. [3]. These results suggest that we could create themusic generator that perceives the emotion intended by music,
based on previous research.
3.2.3 Felt emotion by the music generator. 108 crowdworkers gathered separately from perceive emotion evaluation listened to themusic, and evaluated the emotions they felt. We investigated thecorrelation coefficients between the input to the music generator
and the evaluations of each crowdworker using the same method
as in the previous section. The median correlation coefficients werevalence r=0.59 and arousal r=0.83.
Figure 2 (c) of valence evaluation and Figure 2 (d) of arousal
evaluation show the same trend as Figure 2 (a) and (b). The change
in the color map is more gradual than the valence and arousal
evaluations, which were perceived as the music’s intention. This
result indicates that the emotion felt when listening to music is
weaker than the perceived emotion [4, 14].

3.3

Recreating music generator

In the previous section, the evaluation of felt valence was influenced by arousal input to the music generator. The created music
generator is likely to have a difference between the input valence
and the valence felt by the participants. Therefore, models that
input appropriate valence and arousal values to the music generator were trained by support vector regression and immediately
connected before the music generator. For the valence model which
input appropriate valence, the valence input was predicted fromthe evaluated valence and arousal. For the arousal model which
input appropriate arousal, the arousal input was predicted fromthe evaluated valence and arousal. The support vector regression
models were verified by 3-fold cross-validation. We obtained the
RMSE (valence: 0.0231, arousal: 0.0240). We performed training by
support vector regression using all the data and recreated the music
generator.

(a)

(c)

(e)

104 crowdworkers gathered separately from the previous evaluations listened to the music generated by recreated the music
generator, and evaluated the emotions they felt. We investigatedthe correlation coefficients between the input to the music generator
and the evaluations of each crowdworker using the same method
as in the previous section. The median correlation coefficients werevalence r=0.60 and arousal r=0.76.
Figure 2 (e) of valence evaluation and Figure 2 (f) of arousal
evaluation does not seem to be affected by the arousal or valence
input to the music generator. Although the correlation coefficientsof arousal were lower, the evaluation of valence was improved from
Figure 2 (c). These suggest that we could create the music generator
to induce emotions.

4

In our proposed music generation feedback system based on participant emotions, valence and arousal between 0 and 1 must be
estimated using EEG. Ehrlich et al. estimated emotions based onlinear discriminant analysis (LDA) and the sigmoid function using
the logarithm of variances of EEG signals while listening to music created by the music generator [3]. We used the same featuresand linear regression, which resembles Ehrlich et al.’s research. In
addition, we proposed CNN and a CNN with transfer learning.

4.1

(d)

(f)

Figure 2: Color map of perceived and felt emotions. (a) The
perceived valence evaluation. (b) The perceived arousal evaluation. (c) The felt valence evaluation. (d) The felt arousal
evaluation. (e) The felt valence evaluation using recreatedthe music generator. (f) The felt arousal evaluation using
recreated the music generator.

Participants

20 healthy undergraduate and graduate students (10 males, 10 females) participated in this experiment, which was approved by the
ethics committee of Nara Institute of Science and Technology.

4.2

Experimental design

The participants sat in front of a desk on which a monitor was placed
and wore earphones. Before putting on the electroencephalograph,they listened to sample music (15 s) {val, aro}={0,0}; {0,1}; {0.5,0.5};{1,0}; {1,1}.
The experimental procedure is as follows. First, they silently
gazed at a cross in the center of the monitor for 5 s. Next, theylistened to music for 20 s while continuing to gaze at the cross. The
screen changed after they listened to the music, and the participantsevaluated the valence and the arousal of the emotion they felt by listening to music. SAM was used as the evaluation method, and each
response was given in nine steps. The participant did this practicefor two pieces of music (20 s) {val, aro}={0.125,0.25}; {0.875,0.75}. The
participants put on a CGX Quick-30 electroencephalograph after
the practice. We recorded their EEG and the subjective evaluationsof 41 pieces of music (20 s) with the same method as in the practice.

4.3
(b)

EMOTION ESTIMATOR BY EEG

EEG preprocessing

We did preprocessing for each participant based on the following
procedure.
(1) We removed the data that caused problems, including musicthat wasn’t played. (2) We selected the data of the following 14
channels: AF3, AF4, F3, F4, F7, F8, FC5, FC6, T7, T8, P7, P8, O1,
O2. (3) The EEG signals were downsampled from 1000Hz to 200Hz.
(4) The silent state of 3 to 5 s was divided into two epochs of 1 s
data. We also divided the 0 to 20 s of music listening state into 20
epochs of 1 s data. (5) We designed 2nd order zero phase Chebyshev

IIR bandpass filters that pass the following values: theta (4-7 Hz),alpha (8-13 Hz), low beta (14-21 Hz), high beta (22-29 Hz), gamma(30-45 Hz). (6) We divided the EEG signals into five frequency bandsby the designed filters and calculated the 𝑓 = 𝑙𝑜𝑔(𝑣𝑎𝑟 (𝐸𝐸𝐺𝑑𝑎𝑡𝑎)),which is the logarithm of the waveform variance for each bit ofdata. (7) We subtracted the average of the logarithm of the silent
waveform variance from the logarithmic waveform variance while
they listened to the music.

4.4

Regression

We validated three models such as linear regression, CNN, and CNN
with transfer learning using 70 features (14 channels×5 frequency
bands) obtained in the preprocessing by MATLAB. We used a holdout method this time. In the case of linear regression, the train
and test data are 9:1. In the case of CNN and CNN with transfer
learning, the train, validation, and test data are 8:1:1. The test data
is identical to compare the three models.

last three layers (Figure 3 dotted box) were trained with transfer
learning. We decided the parameters: initial learning rate of 0.0005,
batch size of 20. Using Bayesian optimization, the parameters wereselected from learning rate factor for weight and biases of the fully
connected layer (2 to 40).
4.4.4 Result. The linear regression, CNN, and CNN with transfer
learning results are shown in Table 1. The result of the CNN using
DEAP dataset was RMSE (valence: 0.2498, arousal: 0.2383). We
obtained the lowest RMSE (valence: 0.1807, arousal: 0.1945) between
the felt and estimated emotional values when we used the CNN
with transfer learning for both the valence and the arousal. For the
Wilcoxon signed-rank test result, we found a significant difference
between the linear regression and CNN with transfer learning forboth valence and arousal (p<0.05).
Table 1: RMSE of felt and estimated emotions. Bold fonts
indicate the lowest RMSE of the three models.

4.4.1 Linear regression. Linear regression is the baseline model
selected by previous research [3]. The features were input in vector
format and the data was normalized.

Par.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
mean
std

4.4.2 CNN. The features calculated from the logarithm of the
waveform variance were treated as 6×6×5 images (Figure 3). This sequence was adapted from Jinpeng et al. [8]. As in Figure 3, the CNN
consists of a convolution layer (2×2 size, 1 stride), a batch normalization layer, a ReLU layer, a dropout layer, a fully connected layer(output dimensionality of 100), a dropout layer, a fully connected
layer, and a regression output layer. We decided the parameters:
initial learning rate of 0.001 and batch size of 20. Using Bayesian
optimization [15], the parameters were selected from dropout (0 to0.2), the number of filters (16 to 256).
4.4.3 CNN with transfer learning. We applied the same reprocessing method to the DEAP dataset [7]. The DEAP dataset recorded
the EEG and subjective emotions while the participants watchedmusic videos. Although we didn’t use video in our experiment, we
performed transfer learning using the DEAP dataset because weused music. First, our CNN was trained by the DEAP dataset. We
decided the parameters: initial learning rate of 0.001 and batch sizeof 2000. Using Bayesian optimization, the parameters were selectedfrom dropout (0 to 0.2), the number of filters (16 to 256). Next, the

AF3
AF4
AF3
AF3 AF4AF4AF4F8AF3 F4AF4
F7F7 F3 AF3
F4F4F8F8
F7
F3F3
F7F7
F4F4F8F8
F3F3
FC6
FC5
FC6
FC5
FC6
FC5
FC6
FC5
FC6
FC5
T8
T7
T8
T7
T8
T7
T8
T7
T8
T7
P8P8
P7P7
P8
P7
P8P8
P7
O2
O1
P7
O2
O1
O2
O1
O2
O1
O1 O2

Features of five frequency bands foreach EEG channels

O1 O2

Regression
output layer

Fully connectedlayer

Dropout layer

Fully connectedlayer

Dropout layer

ReLU layer

Batch normalization
layer

AF3 AF4AF3 AF4AF3 AF4F7
F3 AF3F4 AF4F8F7F3 AF3F4 AF4 F8
F7
F3 F4
F8
F7
F3 FC6
F4
F8
FC5
F7
F3 F4
F8
FC5
FC6
FC5
FC6
FC5
FC6
T7
T8
FC5
FC6
T7
T8
T7
T8
T7
T8
P7
P8
P7 T7
P8 T8
P7
P8
P7 O1 O2
P8
P7 O1 O2
P8
O1 O2
O1 O2

Convolution layer

Transformation
Input

Figure 3: Proposed CNN (with/without transfer learning)

5

Linearval
aro
0.1887 0.2254
0.2517 0.1872
0.2033 0.2240
0.1008 0.1253
0.2506 0.2221
0.2199 0.2548
0.2896 0.3108
0.1952 0.2505
0.1980 0.2239
0.0617 0.1341
0.1265 0.1660
0.1849 0.2253
0.1785 0.2356
0.1507 0.1699
0.2211 0.1675
0.0866 0.1426
0.3437 0.2912
0.1221 0.1358
0.0664 0.1935
0.3503 0.3094
0.1895 0.2098
0.0826 0.0567

CNN
val
aro
0.1764 0.2319
0.2299 0.1837
0.2088 0.2119
0.0939 0.1322
0.2354 0.2087
0.2206 0.2400
0.2942 0.2684
0.1957 0.2547
0.2105 0.2194
0.0633 0.1292
0.1299 0.1798
0.2028 0.2218
0.1731 0.2298
0.1635 0.1640
0.2286 0.2084
0.0923 0.1423
0.3149 0.2865
0.1214 0.1220
0.0736 0.1711
0.3407 0.3140
0.1885 0.2060
0.0775 0.0533

CNN TLval
aro
0.1636 0.2171
0.2352 0.1745
0.1980 0.1904
0.0839 0.1206
0.2332 0.2017
0.2081 0.2442
0.2779 0.2546
0.2028 0.2566
0.2073 0.2021
0.0581 0.1195
0.1209 0.1575
0.1834 0.2115
0.1601 0.2116
0.1691 0.1702
0.2148 0.1758
0.0766 0.1350
0.3155 0.2869
0.1088 0.1032
0.0712 0.1684
0.3261 0.2891
0.1807 0.1945
0.0777 0.0538

CONCLUSION

We proposed the system that expresses emotions as continuous values and generates music from EEG. We created the music generatorof the system to induce emotion using support vector regression.
We also confirmed CNN with transfer learning was more effective than linear regression for emotion estimation from EEG while
listening to generated music.
In order to construct the feedback system, we will improve models for emotion estimation and validate them using the leave-oneout whether they can respond to music participants have neverlistened to. Furthermore, we will construct a feedback system thatcombines the music generator and the emotion estimator and compare the emotion estimated from the EEG when using the systemwith the desired emotion.

6

ACKNOWLEDGMENTS

This work was supported by JST CREST Grant Number JPMJCR19A5,
Japan.

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