Emotion Estimation from EEG Signals andExpected Subjective Evaluation
Kana Miyamoto, Hiroki Tanaka, Satoshi Nakamura
Division of Information Science
Nara Institute of Science and Technology
Ikoma-shi, Nara, Japan{miyamoto.kana.mk4, hiroki-tan, s-nakamura}@is.naist.jp

Abstract—We estimated emotions from electroencephalogram(EEG) to construct an emotion induction system using music.
Although a previous study proposed to construct a system fromemotion estimation based on EEG only, the accuracy of emotion
estimation just from EEG might be insufficient and the model was
not tested in leave-one-music-out manner. In this study, we newly
proposed emotion estimation using EEG and the emotions thatare expected to be felt through music. First, we created a music
generator that makes music that induces emotions that resemble
the inputs of emotional values. Next, we recorded EEG and
subjective evaluations of felt emotions while participants listened
to the music created by the music generator. We estimated theemotions by the linear regression of the baseline model andthe convolutional neural network (CNN) of the proposed modelusing recorded EEG and subjective evaluations and investigatedwhether using either all channels (29 EEG channels) or just 14
EEG channels was effective. Finally, we estimated emotions byneural network using the values estimated from the CNN and themusic generator’s inputs. As a result of adapting leave-one-musicout cross-validation, we obtained the lowest RMSE (valence:
0.151, arousal: 0.164) between the actual and estimated emotional
values where the models combined the emotions estimated by
CNN that were trained from 14 channels of EEG and the music
generator’s inputs. For the Wilcoxon signed-rank test result, wefound a significant difference between our proposed model andthe baseline model.
Index Terms—electroencephalogram; emotion estimation;
brain computer interface; human computer interaction

I. I NTRODUCTION
Music induces emotions [1], [2]. It has been used to improvethe mental health such as senior citizens [3]. Researchers
have studied systems that induce emotions using music that
effectively changes emotions. Such systems suffer from two
problems. The first concerns what kind of music to select. The
emotions that are induced when listening to music are very
subjective [4]. The same person may have different emotional
levels depending on the situation. A system needs to selectmusic that is appropriate for the individual and the situation.
The second problem is a method that estimates the emotional
state of participants. The system needs to change the music toadapt to the emotions of the person.
Ehrlich et al. proposed a system that automatically generates
music that is designed to actively induce emotions [5]. They
proposed emotion estimation using the electroencephalogram
978-1-7281-8485-2/21/$31.00 ©2021 IEEE

(EEG) of biological signals to construct their system. EEG
records the brain’s electrical activity and measures time-series
data in multiple channels. Various researches use EEG, suchas a combination of eye-tracking and BCI techniques forcontrolling 3D objects [6] and decoding robot errors using
deep convolutional neural network [7]. EEG is also effective
for emotion estimation [8], [9]. Emotions are expressed in
two-dimensional space [10]. The horizontal dimension is thevalence, representing a range from pleasant to unpleasant. The
vertical dimension is arousal, representing a range from activation to deactivation. Ehrlich et al. proposed emotion estimationin the continuous values of valence and arousal using EEG.
They generated music for participants to perceive the estimatedemotions. The participants recognized their emotions from themusic and induced the desired emotions by themselves. This
study doesn’t passively induce emotions.
We proposed a system that automatically generates musicthat induces emotions passively just by listening [11]. Our
system estimated emotions as continuous values using valence
and arousal by EEG and generated music to induce emotions.
First, based on Ehrlich et al., we created a music generator
that induced emotions. The music generator made music by
inputting the valence and arousal of the emotions to be induced. We evaluated our music generator using crowdsourcing
and concluded that it effectively induced emotions. Second, werecorded EEG and subjective evaluations of the felt emotions
while participants listened to music created by the music
generator and calculated the features for each 1 s of therecorded EEG for training emotion estimation models. We
estimated the emotions by two models (linear regression andconvolutional neural network (CNN)) with the features of 14
channels used by Ehrlich et al. The study obtained the RMSE
between the actual and estimated emotional values by applying
the holdout.
However, there are three problems with our previous study.
The first is model selection. The study calculated the featuresof 14 selected channels instead of all the EEG channels.
Emotion estimation using all the recorded channels must also
be verified. In this paper, we compared linear regression and
CNN using 14 channels that were used by Ehrlich et al. andlinear regression and CNN using all EEG channels.
The second is the verification method. The study acquired
multiple samples from one piece of music and verified them

using the holdout without considering the type of music. EEG
while listening to the same music was be included in the test
and training data in the holdout. When listening to the same
music, EEG has similar features. This result might lower the
RMSE more than the actual environment used. In this study,
we calculated the RMSE between the actual and estimatedemotional values by applying the leave-one-music-out crossvalidation instead of the holdout.
The third is a further performance improvement. The accuracy of the estimated emotions from EEG is importantin the emotion induction system. However, the accuracy of
emotion estimation just using EEG might be insufficient. If
the estimated emotions differ significantly from the emotions
felt by the participants, the system might generate musicthat struggles to induce the desired emotions. Therefore wechose to use EEG and other information. Previous studies
used EEG and galvanic skin response to estimate emotions[12] and EEG and audio information to predict speech quality[13]. Since the emotion induction system needs very quick
emotion estimation, using minimal biological signals andemotion estimation models is required. We propose an emotion
estimation method that combines EEG for biological signals
and the inputs of a music generator that induces emotions that
resemble the emotions of the inputs. The music generator’s
inputs can be regarded as expected subjective evaluations. We
expect the expected subjective evaluations to complement theemotion estimation by EEG. From the estimated values using
EEG and the values of expected subjective evaluations, weestimated emotions by the neural network.
In summary, this paper investigates the following three
aspect:1) Selection of EEG channels and models.
2) Adaptation of leave-one-music-out cross-validation.
3) Emotion estimation using EEG and expected subjective
evaluations.
II. EEG DATA C OLLECTION
We recorded the EEG and the subjective evaluations of thefelt emotions while participants listened to music produced by
the music generator. In this section, we introduce our stimuli
of generated music and experimental design for recording the
EEG and the subjective evaluations.
A. Stimuli of Generated Music
Ehrlich et al. created a music generator, which helps participants perceive the intended emotions of music [5]. Based on
previous studies, the felt and perceived emotions are different,
but these emotions are the same or the felt emotion often
appears lower than the perceived emotion [14], [15]. Therefore
we created a music generator to induce emotions based on
Ehrlich et al. ’s system. A music generator composes music bycalculating five musical parameters: tempo, rhythm, loudness,
pitch, and mode from the inputs of valence and arousal [16].
We calculated these musical parameters from the inputs ofvalence and arousal between 0 and 1 and generated musicalinstrument digital interface (MIDI) signals, which were sent

Silent (5 s)
Listening to music (20 s)
Evaluation of emotions
using SAM

EEG

re c ording

Display of SAM

(25

s)

Fig. 1. Overview of experimental design: This figure outlines the experimentfor one piece of music. This outline was repeated for 41 pieces of music.

to DAW software that generated music by piano, violin, andcello. After evaluating the music generator using crowdsourcing, we concluded that it effectively induced emotions. In thisstudy, we used the same stimuli as our previous study [11].
B. Participants
Twenty healthy undergraduate and graduate students (10males, 10 females) participated in this experiment, which wasapproved by the ethics committee of Nara Institute of Scienceand Technology.
C. Design
The experimental procedure is shown in Fig. 1. The participants silently gazed at a cross in the center of the monitor
for 5 s. Then they listened to music for 20 s and gazed at
the cross. The screen changed after they listened to the music
for the evaluation of their felt emotions. They evaluated thevalence and arousal of the felt emotions in nine steps between
0 and 1 with a self-assessment mannequin (SAM) [17].
The participants sat in front of a desk on which a monitor
was placed. Before putting on the electroencephalograph, they
wore earphones and listened to five pieces of music sample(15 s) {val, aro}={0,0}; {0,1}; {0.5,0.5}; {1,0}; {1,1}. They
practiced the experiment with two pieces of music (20 s) {val,aro}={0.125,0.25}; {0.875,0.75}. After the practice, we put a
CGX Quick-30 electroencephalograph on them and recorded
their EEG and their subjective evaluations of the 41 pieces ofmusic (20 s). The 41 pieces of music were chosen uniformly
from the coordinates of valence and arousal.
D. Preprocessing
We used MATLAB (R2019a) and EEGLAB [18] for preprocessing, which consisted of the following seven steps: 1)
We removed the data that caused problems such as the music
wasn’t played; 2) The EEG signals were downsampled from
1000Hz to 200Hz; 3) The silent state of 2 to 5 s was divided
into three epochs of 1 s data; 4) The music-listening stateof 0 to 20 s was divided into 20 epochs of 1 s data; 5) We
designed 2nd order zero phase Chebyshev IIR bandpass filtersthat pass theta (4-7 Hz), alpha (8-13 Hz), low beta (14-21Hz), high beta (22-29 Hz), and gamma (30-45 Hz); 6) EEG
signals were divided into five frequency bands by the designed

Placement of the EEG channels

Matrix of 14 channels

Matrix of 29 channels

Fig. 2. Matrix based on placement of EEG channels: We used two matrices to estimate valence and arousal in CNN.

filters to calculate the f = log(var(EEGdata)), which is thelogarithm of the waveform variance for each bit of data; 7) The
average of the logarithm of the silent state waveform variance
was subtracted from the logarithmic waveform variance during
the music-listening state for each type of music.

Our model trained with 29 channels has 145 features (29channels×5 frequency bands). The features were input invector format and the data were normalized using z-scorenormalization. In linear regression, the models that estimate
valence and arousal are different.

III. E MOTION E STIMATION

4) CNN: CNN has feature extraction capabilities. CNN isalso used in studies on EEG [19], [20]. We used CNN for
training that takes into account the positional relationshipsof the EEG channels. We mapped the matrix for each of
the five frequency bands (Fig. 2). The placement was basedon the method of previous studies [21], [22]. We used zero
to fill the element where there were no EEG channels. We
prepared 8×9×5 matrix. As shown in Fig. 4, the CNN
consists of a convolution layer (2×2 size, 1 stride), a batchnormalization layer, a ReLU layer, a convolution layer (2×2size, 1 stride), a batch normalization layer, a ReLU layer, adropout layer (dropout rate of 0.5), a fully connected layer(output dimensionality of 2), and a regression output layer.
We used the Adam optimizer. The learning rate was 0.001.
The batch size was 64. The learning epoch was 100. In CNN,
valence and arousal were estimated from one model.

We verified the three proposed emotion estimations using
the preprocessing results. We used MATLAB (R2019b) for
training the emotion estimation models.
A. Selection of EEG channels and models
We verified the models for estimating emotions only using
EEG. We prepared two types of channels: just 14 channels
and all 29 channels. We prepared two types of models: linear
regression and CNN. By combining the channels and models,
we examined four models: linear regression using 14 channels,
CNN using 14 channels, linear regression using 29 channels,
and CNN using 29 channels. We evaluated the models using
the leave-one-music-out cross-validation.
1) Emotion estimation using 14 channels: Ehrlich et al. [5]acquired 14 channels using Emotiv EPOC: AF3, AF4, F3, F4,
F7, F8, FC5, FC6, T7, T8, P7, P8, O1, and O2. We used CGX
Quick-30, acquired 29 channels, and selected 14 from the 29
channels we recorded.
2) Emotion estimation using 29 channels: We acquired 29
channels using CGX Quick-30: Fp1, Fp2, AF3, AF4, F7, F8,
F3, Fz, F4, FC5, FC6, T7, T8, C3, Cz, C4, CP5, CP6, P7, P8,
P3, Pz, P4, PO7, PO8, PO3, PO4, O1, and O2. We used all
29 channels to train the emotion estimation models.
3) Linear regression: Ehrlich et al. estimated emotions
based on linear discriminant analysis (LDA) and the sigmoid
function using the logarithm of the EEG variances. Linear
regression is a similar model of Ehrlich et al. We used linear
regression as the baseline model. Our model trained with 14
channels has 70 features (14 channels×5 frequency bands).

B. Adaptation of leave-one-music-out cross-validation
From the preprocessing, we acquired 20 samples from apiece of music (Fig. 3). We assume that adjacent samples have
similar features. If the holdout is applied without considering
the type of music, adjacent samples are included in the test andtraining data. The emotion induction system needs to estimate
emotions from the EEG when listening to unknown music.
Since the holdout might show higher accuracy than emotion
estimation in actual environments, we apply the leave-onemusic-out cross-validation to test one piece of music that is
excluded from the training data.

20 sMusic 1
1

2

3

...

Music 2
...

20

1

2

3

...

20

Music 41
1

2

3

...

20

1s

Leave-one-music-out cross-validation

Holdout

Training data...

Training data...

Test data...

Test data...

These data don't include
the same music

These data include
the same music

Fig. 3. Overview of the leave-one-music-out cross-validation and the holdout.

C. Emotion estimation using EEG and expected subjective
evaluations
The accuracy of the emotion estimation from EEG might
only be insufficient. One solution to this problem is estimating
emotions by adding information other than EEG. We used
EEG and the music generator’s inputs. After evaluating themusic made by our music generator, we found that it induced
emotions similar to the inputs of valence and arousal. Accordingly, we regarded the inputs of the music generator as
expected subjective evaluations and assumed that the inputs
can be used for emotion estimation. We estimated emotions
using emotions estimated from EEG and the inputs to themusic generator. Using the music generator’s inputs, we expectthat the influence on the final estimated emotions will be
reduced even if the estimated values by EEG are incorrect.
We prepared four inputs of emotions estimated from EEG
and those of the music generator. As shown in Fig. 4, theneural network consists of a fully connected layer (output
dimensionality of 8), a ReLU layer, a dropout layer (dropout
rate of 0.2), a fully connected layer (output dimensionality of2), and a regression output layer. We used the Adam optimizer.
The learning rate was 0.001. The batch size was 64. The
learning epoch was 100. We evaluated the models using leaveone-music-out cross-validation.
IV. R ESULT
A. Selection of EEG channels and models
We estimated the emotions only using EEG from fourmodels: linear regression using 14 channels, CNN using 14
channels, linear regression using 29 channels, and CNN using
29 channels. Table I shows the results of the comparisonof the felt and estimated emotions. We obtained the lowest
RMSE (valence: 0.214, arousal: 0.239) by CNN using 14
channels. For the Wilcoxon signed-rank test result, we founda significant difference between CNN using 14 channels andthe linear regression using 14 channels of the baseline model
for both valence and arousal (p<0.05).

TABLE IRMSE OF FELT AND ESTIMATED EMOTIONS USING FOUR MODELS . B OLD
INDICATES LOWEST RMSE.
Par.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
mean
std

Linear regression
14 channels
29 channels
val
aro
val
aro
0.285 0.330 0.290 0.341
0.307 0.234 0.328 0.246
0.276 0.286 0.290 0.279
0.118 0.164 0.131 0.191
0.334 0.283 0.350 0.316
0.244 0.285 0.259 0.313
0.403 0.410 0.423 0.424
0.259 0.308 0.288 0.323
0.213 0.290 0.224 0.302
0.074 0.166 0.083 0.193
0.165 0.233 0.171 0.243
0.216 0.291 0.211 0.307
0.218 0.318 0.224 0.333
0.193 0.227 0.195 0.249
0.252 0.239 0.253 0.233
0.081 0.165 0.090 0.182
0.464 0.379 0.496 0.399
0.178 0.189 0.177 0.192
0.077 0.222 0.082 0.231
0.384 0.367 0.421 0.386
0.237 0.269 0.249 0.284
0.108 0.071 0.116 0.072

CNN
14 channels
29 channels
val
aro
val
aro
0.261 0.282 0.261 0.283
0.287 0.217 0.292 0.227
0.247 0.229 0.253 0.246
0.104 0.140 0.116 0.153
0.312 0.250 0.324 0.252
0.222 0.266 0.236 0.276
0.353 0.372 0.362 0.379
0.217 0.277 0.228 0.291
0.192 0.254 0.200 0.265
0.073 0.152 0.076 0.153
0.151 0.217 0.160 0.220
0.195 0.263 0.199 0.260
0.208 0.302 0.216 0.307
0.174 0.193 0.175 0.202
0.228 0.210 0.232 0.210
0.067 0.148 0.085 0.160
0.411 0.369 0.417 0.372
0.152 0.152 0.155 0.159
0.067 0.190 0.072 0.197
0.365 0.299 0.363 0.329
0.214 0.239 0.221 0.247
0.099 0.068 0.098 0.068

B. Adaptation of leave-one-music-out cross-validation
We found that CNN using 14 channels of EEG had thelowest RMSE of the four models that only used EEG. We
used CNN with 14 channels and compared emotion estimationby considering the type of music using leave-one-music-out
cross-validation and emotion estimation without considering
the type of music using k-fold cross-validation. In this paper,
we applied k-fold cross-validation instead of the holdout toimprove the generalizability. The amount of test data forthe leave-one-music-out cross-validation and the k-fold crossvalidation was identical. Tables II shows the results of thecomparison of the felt and estimated emotions. We obtained
the lower RMSE (valence: 0.202, arousal: 0.226) with the
k-fold cross-validation than RMSE (valence: 0.214, arousal:

Regression
output layer

Fully connected
layer

ReLU layer

Dropout layer

Matrix of EEG

EEG and expected subjective evaluation
Fully connected
layer

Regression
output layer

Fully connectedlayer

Dropout layer

ReLU layer

Batch normalization
layer

Convolution layer

ReLU layer

Batch normalization
layer

Convolution layer

CNN using EEG only

Expected subjective evaluation

Fig. 4. Neural network using EEG and expected subjective evaluations: This neural network estimates valence and arousal from four inputs. Four inputs arevalence and arousal only estimated from EEG and valence and arousal of the music generator’s inputs.

TABLE IIRMSE OF FELT AND ESTIMATED EMOTIONS APPLYINGLEAVE - ONE - MUSIC - OUT CROSS - VALIDATION AND K - FOLDCROSS - VALIDATION . B OLD FONTS INDICATE THE LOWEST RMSE.

TABLE IIIRMSE OF FELT AND ESTIMATED EMOTIONS USING EEG AND EXPECTEDSUBJECTIVE EVALUATIONS . B OLD FONTS INDICATE THE LOWEST RMSE.
Par.

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

leave-one-music-out
val
aro
0.261
0.282
0.287
0.217
0.247
0.229
0.104
0.140
0.312
0.250
0.222
0.266
0.353
0.372
0.217
0.277
0.192
0.254
0.073
0.152
0.151
0.217
0.195
0.263
0.208
0.302
0.174
0.193
0.228
0.210
0.067
0.148
0.411
0.369
0.152
0.152
0.067
0.190
0.365
0.299
0.214
0.239
0.099
0.068

k-fold
val
0.240
0.255
0.247
0.105
0.285
0.211
0.331
0.209
0.188
0.068
0.141
0.188
0.189
0.167
0.209
0.087
0.374
0.145
0.072
0.328
0.202
0.086

aro
0.268
0.210
0.234
0.140
0.239
0.251
0.340
0.252
0.237
0.150
0.201
0.240
0.270
0.193
0.209
0.154
0.320
0.144
0.182
0.293
0.226
0.057

0.239) with the leave-one-music-out. For the Wilcoxon signedrank test result, we found a significant difference between andthe k-fold cross-validation and the leave-one-music-out crossvalidation for both the valence and arousal (p<0.05).

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

CNN
val
aro
0.261
0.282
0.287
0.217
0.247
0.229
0.104
0.140
0.312
0.250
0.222
0.266
0.353
0.372
0.217
0.277
0.192
0.254
0.073
0.152
0.151
0.217
0.195
0.263
0.208
0.302
0.174
0.193
0.228
0.210
0.067
0.148
0.411
0.369
0.152
0.152
0.067
0.190
0.365
0.299
0.214
0.239
0.099
0.068

Music gen.
val
aro
0.140
0.125
0.174
0.216
0.140
0.140
0.266
0.301
0.192
0.152
0.293
0.122
0.122
0.149
0.402
0.244
0.238
0.128
0.256
0.360
0.232
0.186
0.305
0.253
0.177
0.201
0.277
0.244
0.175
0.306
0.293
0.244
0.146
0.137
0.220
0.250
0.253
0.213
0.347
0.284
0.232
0.213
0.075
0.070

Comb.
val
aro
0.124
0.150
0.180
0.144
0.137
0.130
0.097
0.128
0.205
0.143
0.223
0.169
0.143
0.188
0.228
0.197
0.157
0.128
0.053
0.122
0.115
0.157
0.166
0.220
0.135
0.222
0.172
0.134
0.143
0.177
0.068
0.137
0.155
0.169
0.102
0.139
0.057
0.134
0.367
0.282
0.151
0.164
0.071
0.041

method and the linear regression using 14 channels of thebaseline model for both the valence and arousal (p<0.05).
We show the plots of the actual and estimated emotions of
the valence and arousal of participant 1 in Fig. 5. There wasa positive correlation between the actual emotions and thoseestimated by EEG and the expected subjective evaluations.

C. Emotion estimation using EEG and expected subjective
evaluations

V. C ONCLUSION

We estimated the emotions from CNN using 14 channels
and the music generator’s inputs. Table III shows the resultsof the comparison of the felt and estimated emotions. CNN is
CNN using 14 channels of EEG only, Music gen. is the inputsof the music generator, and Comb. is the neural network thatcombines CNN and Music gen. We obtained the lowest RMSE
(valence: 0.151, arousal: 0.164) by the EEG and expectedsubjective evaluations. For the Wilcoxon signed-rank test
result, we found a significant difference between our proposed

We proposed an emotion estimation method that combines
EEG and inputs of a music generator. We first estimated theemotions using EEG only and obtained the lowest RMSE
by CNN using 14 channels. We assume the CNN that takes
into account the positional relationship of channels is more
effective than linear regression. Our result also indicates theeffectiveness of selecting just some channels instead of allof them. We also estimated emotions by applying two crossvalidation methods. The RMSE was higher when the leave-

R EFERENCES

Actual and estimated emotions of valence
Pearson’s correlation coefficient (r=0.841, p<0.05)

Actual and estimated emotions of arousal
Pearson’s correlation coefficient (r=0.809, p<0.05)
Fig. 5. Plots of the actual and estimated emotions of participant 1: The
emotions were estimated by EEG and expected subjective evaluations.

one-music-out cross-validation was applied than when the kfold cross-validation was applied, although we assume that theleave-one-music-out cross-validation is similar to the actual
environment. We then estimated the emotions by a model
combining values estimated from EEG and inputs of the music
generator. The RMSE by the model was the lowest of all
our experiments. We consider that the inputs of the music
generator compensated for the results of emotion estimationfrom EEG.
In the future, we will construct an emotion induction systemthat combines the music generator and our proposed methodof emotion estimation and compare the estimated emotions
when using the system with desired emotions.
ACKNOWLEDGMENT
This work was supported by JST CREST Grant Number
JPMJCR19A5, Japan.

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