Meta-Learning for Emotion Prediction from EEG while
Listening to Music
Kana Miyamoto

Hiroki Tanaka

Satoshi Nakamura

Nara Institute of Science andTechnology
Center for Advanced Intelligence
Project, RIKEN
Japan
miyamoto.kana.mk4@is.naist.jp

Nara Institute of Science andTechnology
Center for Advanced Intelligence
Project, RIKEN
Japan
hiroki-tan@is.naist.jp

Nara Institute of Science andTechnology
Center for Advanced Intelligence
Project, RIKEN
Japan
s-nakamura@is.naist.jp

ABSTRACT

1

We are studying to realize an emotion induction system that generates music based on emotions predicted in real-time from electroencephalogram (EEG). Since there are individual differences in EEG
while listening to music, a model trained from a single participant’s
data is expected to provide highly accurate emotion prediction.
However, a time-consuming EEG recording is required to avoid
data shortage. We need to reduce the recording time to minimize
the burden on the participants. Therefore, we train a model which
considers the individuality of EEG from multiple participants’ data
and fine-tune it from a small amount of a single target’s data. In thispaper, we propose a method using meta-learning for pre-training.
We compared three methods: two methods using multiple participants’ data (with/without meta-learning) and a method using asingle participant’s data. Our proposed method obtained the lowest
RMSE (valence: 0.244 and arousal: 0.287). We demonstrate the effectiveness to use meta-learning to train an emotion prediction model,
which is a necessary step for constructing the emotion induction
system.

Appropriately inducing emotions is critical in mental health problems. It has been shown that failure of emotion regulation is related
to the development of depressive symptoms [6, 11]. Music is one
stimulus used to induce emotions [27]. The relationship between
music and emotions has been discussed [12, 13, 25]. Valence andarousal are often used to evaluate emotions based on the circumplex model [24]. Such factors as the tempo from which music iscomprised affect valence and arousal [29, 30]. Emotion induction
using music is attractive since music affects emotions. However,people exhibit individual differences in the emotions they feel [15].
Therefore, our previous study proposed an emotion induction system that creates personalized music with a music generator [21].
The music consists of the following parameters: tempo, rhythm,
loudness, pitch, and chord. These parameters are calculated using
continuous values from 0 to 1 for the valence and arousal input tothe music generator. Our previous study showed that generated music induced emotions that are close to the valence and arousal thatare input to the music generator [21]. However, the felt emotion
does not always match the inputs of the music generator. Therefore,
we proposed a feedback loop that predicts emotions and periodically updates the inputs to the music generator [21]. Since themusic changes based on the participants’ emotions, it is expected
to provide personalized emotional induction. The emotion induction system uses EEG for emotion prediction. EEG is the electricalactivity generated from the brain. Recognizing emotions from EEG
is actively being studied [10, 26, 28]. Since the music is made using
predicted emotions from EEG, the prediction accuracy of emotions
is critical in our system.
There are individual differences in EEG [18, 20, 33]. Previous
studies on emotion induction systems trained an independent modelthat was adapted to individuals using only a single participant’s
EEG to eliminate individual differences [8, 21]. The amount of EEG
data obtained is small when the recording time per participant isshortened. It was time-consuming to record the EEG to prevent
data shortages in training the models. If the EEG recording and theuse of the system are the same days, the burden on the participants
will be large because they have to put on the electroencephalographfor a long time. Therefore, the EEG was recorded for the emotion
prediction model on a different day from the day on which thesystem will be used in our previous study [21]. Such a difference
between days might slightly shift the position of the electrodes.
EEG also differs from day to day [5, 19]. Therefore, we will shortthe EEG recording time to train the emotion prediction model and

CCS CONCEPTS• Human-centered computing → Interaction design.

KEYWORDS
Electroencephalogram; emotion induction; emotion prediction; metalearning
ACM Reference Format:
Kana Miyamoto, Hiroki Tanaka, and Satoshi Nakamura. 2021. Meta-Learning
for Emotion Prediction from EEG while Listening to Music. In Companion
Publication of the 2021 International Conference on Multimodal Interaction(ICMI ’21 Companion), October 18–22, 2021, Montréal, QC, Canada. ACM,
New York, NY, USA, 5 pages. https://doi.org/10.1145/3461615.3486569

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INTRODUCTIONICMI ’21 Companion, October 18–22, 2021, Montréal, QC, Canada

use the emotion induction system on the same day. Unfortunately,with just a small amount of EEG data, it might not be possible totrain the model [31]. Transfer learning adapts a pre-trained modelto another domain [23]. Highly accurate results can be obtained
from a small amount of data with transfer learning. However, insome studies, the pre-training model treated the EEG of multiple
participants as one piece of data [14, 21]. We believe that training
cannot take into account the personal character of EEG because
no individual has been recognized. In this paper, we apply modelagnostic meta-learning (MAML) [9] that makes distinctions among
participants.
MAML acquires parameters from tasks and adapts them to atarget task with a small amount of data. Comparing with otherexisting meta-learning approaches, MAML does not expand thenumber of learned parameters nor place constraints on the modelarchitecture [9]. We consider that use of MAML is expected to
be useful in prediction using EEG and complex model structures.
MAML has actually been used in EEG-based prediction [1, 4, 16].
In addition, MAML has been used in predicting emotions from EEG
using the DEAP and SEED datasets [7]. These datasets containnot only sound but also video. The type of stimulus influences responses in biological signals [2]. In our emotion induction system,
the stimulus is limited to music. Therefore, we need to investigate
the effectiveness of MAML in predicting emotion using EEG while
listening to music. In this paper, we compared three training methods. The first uses multiple participants’ EEG with MAML. We
propose to fine-tune the model trained with MAML using a single
target’s EEG. The second uses multiple participants’ EEG without
MAML. We fine-tune the model trained without MAML using asingle target’s EEG. This method might not be able to grasp EEG
individuality. The third uses a single participant’s EEG. We train
the model using only a single target’s EEG. This method might lacktraining data. We assumed that MAML trains parameters that areeasily adaptable to many people from EEG while listening to music,
and we hypothesized the method with MAML can reduce the errorof emotion prediction compared to the other two methods.

2

EEG EMOTION PREDICTION FROM MUSIC

Miyamoto ,Tanaka and Nakamura

evaluated the valence and arousal on a 9-point scale between 0 and
1 after listening to the music.

2.2

Features

We obtained the EEG while the participants listened to 20 sec pieces
of music. We cut the EEG into 1 sec slices. Then the EEG was divided
into five frequency bands using bandpass filters. The logarithm of
the variance of the EEG waveform that passed through the filter
was calculated as features. 20 features were extracted from one
piece of music. Although CGX Quick-30 can obtain 29 ch of EEG,
emotion prediction using 14 ch was more accurate than using all the
EEG likes in our previous study [22]. We calculated 70 dimensionalfeatures using 14 ch and made 6 × 6 × 5 matrices (Figure 1), whichtake into account the positional relationship of the EEG and thefollowing five frequency bands: theta (4-7 Hz), alpha (8-13 Hz), lowbeta (14-21 Hz), high beta (22-29 Hz), and gamma (30-45 Hz). We
used zero to fill the element of the matrices where there were no
EEG channels.

2.3

Model structure

We used a convolutional neural network (CNN) as an emotion
prediction model. CNN is commonly used in EEG research as wellas in image recognition [17, 32]. We used a CNN for training byconsidering the location of the electrodes. As shown in Figure 1,
a CNN consists of a convolution layer (2×2 size, 1 stride), a batchnormalization layer, a ReLU layer, a convolution layer (2×2 size, 1
stride), a batch normalization layer, a ReLU layer, a convolution
layer (2×2 size, 1 stride), a batch normalization layer, a ReLU layer, afully connected layer (output dimensionality of 2), and a regression
output layer. We used the SGD optimizer. The number of filters inall the convolution layers was eight.

3

TRAINING METHODSIn this section, we present the three types of training methods:
the proposed method and two baseline methods. The EEG can beused are for 20 participants. We trained 20 models with different
data of the target participant in each method. We used a fewer
hyperparameter search to reduce time required to train models.

In this section, we describe the dataset, features and model structure.

3.1
2.1

DatasetWe used a dataset created in our previous study on emotion induction systems [21]. It contains EEG and subjective evaluations while
listening to 20 sec music from 20 participants (10 males, 10 females).
For this recording, completely new music that was made for theemotion induction system was used. We made a music generator
based on a previous study [8]. The music generator makes musicthat induced emotions using valence and arousal of inputs andcalculating five musical parameters. We found from crowdsourcingthat the music generator is effective for making music that induces
emotions that resemble the inputs [21]. The electroencephalograph
was CGX Quick-30. EEG was recorded using 41 pieces of music
made by the music generator. However, the data of several pieces
of music from a few participants were removed due to defects inthe experiment. The emotion while listening to music was evaluated using a self-assessment mannequin (SAM) [3]. ParticipantsMultiple participants’ EEG with MAML

This is our proposed method using multiple participants’ EEG with
MAML. We used Algorithm 1 whose training procedure is shownat the top of Figure 2. We trained our models using EEG for 10
participants from the dataset with MAML. In addition, the parameters were tuned using EEG from nine participants. The obtained
pre-training model was fine-tuned with the EEG of a single target
participant. We explain pre-training and fine-tuning respectively.
First, we describe the pre-training. The model’s parameters 𝜃
was initialized. We sampled 10 tasks and selected D𝑖 = {𝑥 ( 𝑗) , 𝑦 ( 𝑗) }which are EEG and labels for 20 pieces of music from each task.
′
The model 𝜃𝑖 was trained using 𝐷𝑖 , loss L T 𝑖 and learning rate 𝛼:′𝜃𝑖 = 𝜃 − 𝛼∇𝜃 L T 𝑖 (𝑓𝜃 ).
The EEG and labels for the remaining 21 pieces of music of each′′task was set as D𝑖 = {𝑥 ( 𝑗) , 𝑦 ( 𝑗) }. However, some participants’ D𝑖

Meta-Learning for Emotion Prediction from EEG while Listening to Music

AF3
F7 F3
FC5

AF4
F4 F8
FC6

T7

T8
P7

O1

O2

P8

Placement of EEG channels

6

AF3 AF4 0 0
0 0 AF3AF4
AF3 AF4 0 0 0 0
0 0 0 0 0AF3
AF4
0 0 0 F80
F3 0F4AF4
0 0 F7
0F7AF3F7 0 0 F3F3 F4F4 0 F8F8
F3 F4F4
F8 0
FC5
FC6
0 0 0 FC6
F7F7 0 0 FC5F3F80
FC5 0 0 FC6 0 0
0 FC5
FC6
T7 0 0 0 0 FC6
0 0 0 T8
0 0 FC5
T7 0 0 0 0 0 T8
T8
T7
T7
0 0 0 0 T8
T7 P7
0P7 0 0 0 T80 P8
P7 0 0 0 0 0 P8P8
Gamma
P7 0 0 O1
0 O2
0 P8
0 0
P7 0 0 O1
0 O2
0 P8
0 0 High beta
O1O2
O2 0 0 0 0
Low beta
0 0 0 0 O1
0 0 O1 O2 0 0
Alpha
Theta
6
MatrixICMI ’21 Companion, October 18–22, 2021, Montréal, QC, Canada

Conv

BN

ReLU

Conv

BN

ReLU

Conv

BN

ReLU

FC

ssion
Regre

Figure 1: Structure of CNN

3.2
Algorithm 1 MAML for our emotion prediction
Require: 𝑝 (T ): distribution over tasks
Require: 𝛼, 𝛽: learning rate
randomly initialize 𝜃for each 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛 do
Sample training tasks T𝑖 ∼ 𝑝 (T )for each T𝑖 do
Select data of 20 pieces of music D𝑖 = {𝑥 ( 𝑗) , 𝑦 ( 𝑗) } from T𝑖
Evaluate ∇𝜃 L T 𝑖 (𝑓𝜃 ) using D𝑖 and L T 𝑖′
Update parameters: 𝜃𝑖 = 𝜃 − 𝛼∇𝜃 L T 𝑖 (𝑓𝜃 )′Select data of about 21 pieces of music D𝑖 = {𝑥 ( 𝑗) , 𝑦 ( 𝑗) }from T𝑖
end forÍ′
Update 𝜃 ← 𝜃 − 𝛽∇𝜃 T𝑖 ∼𝑝 ( T) L T 𝑖 (𝑓𝜃 ′ ) using each D𝑖 and𝑖LT𝑖
end forwere set to EEG and labels of 19 or 20 pieces of music due to missing′′
data in the dataset. We evaluated updated 𝜃𝑖 using D𝑖 . After all thetasks finished these calculations, we updated 𝜃 to minimize the loss
L T 𝑖 of all the tasks using learning rate 𝛽:𝜃 ← 𝜃 − 𝛽∇𝜃ÕL T 𝑖 (𝑓𝜃 ′ ).
𝑖T𝑖 ∼𝑝 ( T)The trained parameters were evaluated using the EEG of nine
participants with validation data, and the parameters of the learning
rate were tuned. The sets of hyperparameters were 𝛼 ∈ {10−1, 10−2 }and 𝛽 = 10−1 . The model was trained until validation loss did notdecrease for 5 consecutive iterations. The model trained at the bestlearning rate was used as the pre-trained model.
Next, we fine-tuned the pre-trained model to adapt it to a single target participant. The EEG of 20 pieces of music was fixed
as training data. We performed 5-fold cross-validation using thetraining data. The sets of hyperparameters were 𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔 𝑟𝑎𝑡𝑒 ∈{10−1, 10−2 }. The model was trained until validation loss did notdecrease for 5 consecutive iterations. The EEG of remaining about
21 pieces of music was used as test data. Finally, the five models
were evaluated using test data.
The same training data, validation data, and test data were usedin the proposed method and two baseline methods.

Multiple participants’ EEG without MAML

This is a baseline method using multiple participants’ EEG without
MAML. Its training procedure is shown in the middle of Figure 2.
In the proposed method, participants were regarded as tasks, andtraining was performed for each task. In this method, multiple participants are regarded as one large amount of data. The parameters
were calculated with a batch size of 1024 using the data. The trainedmodel was evaluated using EEG for nine participants of validation
data, and the parameters of the learning rate were tuned. The setsof hyperparameters were 𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔 𝑟𝑎𝑡𝑒 ∈ {10−1, 10−2 }. The model
was trained until validation loss did not decrease for 5 consecutive
iterations. The model trained at the best learning rate was used as
the pre-trained model, which was fine-tuned to adapt to the target
task. The fine-tuning method is identical as the proposed method.

3.3

Single participant’s EEGThis is a baseline method using a single target participant’s EEG.
Its training procedure is shown at the bottom of Figure 2. Thismethod has no pre-training. The model was trained from initial
parameters 𝜃 by the same procedure as in the fine-tuning of the proposed method. The sets of hyperparameters were 𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔 𝑟𝑎𝑡𝑒 ∈{10−1, 10−2 }.

4

RESULTSThe emotion prediction results using the proposed method and thetwo baseline methods are shown in Table 1, which shows the RMSE
between the measured values of the dataset and the predicted valuesof each target participant. The model with the lowest RMSE wasthe one of the proposed method (valence: 0.244 and arousal: 0.287).
For the Wilcoxon signed-rank test result, we found a significant
difference between the multiple participants’ EEG with MAML andwithout MAML for both valence (p<.001) and arousal (p<.001). We
found a significant difference between multiple participants’ EEG
with MAML and a single participant’s EEG for both valence (p<.001)
and arousal (p<.001). We also found a significant difference between
multiple participants’ EEG without MAML and a single participant’s
EEG for both valence (p<.001) and arousal (p<.001). For emotion
prediction with a small amount of a target’s EEG, the prediction
accuracy was higher when using the EEG of multiple participants
than when using the EEG of a single participant in considerationof individuality. When the EEG of multiple participants was used,
the prediction accuracy was higher when using MAML than when
not using it.

ICMI ’21 Companion, October 18–22, 2021, Montréal, QC, Canada

Miyamoto ,Tanaka and Nakamura

Training data
10 participantsMulti participants' EEG Initialization
𝜃with MAML

Test data

5-fold cross-validation
20 pieces of music

Validation data
9 participants

TargetTest

Fine-tuning𝜃"#Update 𝜃
Validation

5-fold cross-validation
20 pieces of music

Multi participants' EEG Initialization
𝜃without MAML

Test data
TargetTest

Fine-tuning

Validation
Test data

5-fold cross-validation
20 pieces of music
Initialization
Single participant’s EEG𝜃TargetTest

Figure 2: Three types of training methods
Table 1: RMSE of felt and predicted emotions using EEG.
Bold fonts indicate the 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

Single par.
val
aro
0.371
0.378
0.427
0.408
0.357
0.456
0.306
0.326
0.374
0.373
0.387
0.456
0.478
0.470
0.380
0.380
0.378
0.356
0.377
0.264
0.289
0.328
0.346
0.323
0.314
0.374
0.380
0.377
0.360
0.362
0.258
0.318
0.511
0.468
0.382
0.383
0.233
0.323
0.424
0.405
0.367
0.377
0.066
0.056

Without MAML
val
aro
0.310
0.374
0.407
0.334
0.307
0.361
0.153
0.169
0.379
0.314
0.364
0.394
0.449
0.454
0.331
0.328
0.301
0.380
0.116
0.205
0.249
0.314
0.260
0.276
0.266
0.360
0.285
0.290
0.289
0.297
0.231
0.251
0.452
0.406
0.240
0.238
0.202
0.292
0.415
0.385
0.300
0.321
0.092
0.071

With MAML
val
aro
0.300
0.348
0.281
0.283
0.274
0.329
0.141
0.167
0.332
0.271
0.276
0.350
0.361
0.387
0.283
0.292
0.235
0.360
0.103
0.197
0.193
0.276
0.204
0.252
0.220
0.321
0.213
0.264
0.253
0.297
0.120
0.196
0.424
0.392
0.191
0.189
0.106
0.246
0.375
0.317
0.244 0.287
0.090
0.066

In addition, we investigated the performance of the pre-training
models and the effectiveness of fine-tuning. We evaluated the pretraining models for multiple participants’ EEG with MAML andmultiple participants’ EEG without MAML. The RMSE was calculated from the measured values of the dataset and the values
predicted by the pre-trained models. As a result, we obtained meanof RMSE for 20 participants (valence: 0.248 and arousal: 0.299) forthe method with MAML and (valence: 0.310 and arousal: 0.337) forthe method without MAML. We confirmed that the RMSE tended
to be lowered by fine-tuning between with and without fine-tuningin the method using MAML, there were significant differences only

valence (valece: p=.0228 and arousal: p=.911). We also confirmedthat the RMSE tended to be lowered by fine-tuning between withand without fine-tuning in the method not using MAML, there
were no significant differences (valece: p=.100 and arousal: p=.654).
A previous study showed that the accuracy improves as the amountof data used for fine-tuning increases [7]. We will investigate therelationship between the amount of data used for fine-tuning andthe RMSE in our future work.

5

CONCLUSIONSIn this paper, we compared three methods for emotion prediction
using EEG while listening to music: two methods using multiple
participants’ data (with/without meta-learning) and a method using
a single participant’s data. The prediction accuracy was higher
when using the EEG of multiple participants than when using
the EEG of a single target considering individuality. This suggeststhat the amount of data was insufficient only with the target’s
EEG. In addition, perhaps the pre-training data contained EEG that
resembled those of the target. When predicting emotions using
EEG of multiple participants, the method with MAML was effective.
This suggests that by treating each participant as a single task,
training addressed the individual nature of EEG. Therefore, using
MAML is effective for training an emotion prediction model, which
is necessary for constructing an emotion induction system usingmusic.
Our future task is to validate the effectiveness of MAML witha larger number of participants. We will also induce emotions byapplying models trained by MAML to our emotion induction system.
We will investigate how recording EEG and training the modelon the same day as when using the system affects the quality ofemotional induction.

ACKNOWLEDGMENTS
This work was supported by JST CREST Grant Number JPMJCR19A5,
Japan, and RIKEN Junior Research Associate Program.

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