Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

Estimating Beverage Preference Based on
Subjective Emotional Reactions and EEG Activity
Kuangzhe Xu∗ , Kenji Katahira∗† , Yoichi Yamazaki∗ , Fan Zhang∗ , Naoki Nishida∗ ,Yuichiro Tamai∗ ,Naoyuki Matsuzaki‡ ,Noriko Nagata∗∗ Kwansei Gakuin University, Hyoko, Japan

E-mail: kyoutetsu.jo@kwansei.ac.jp Tel/Fax: +81-79-565-7874† Waseda University, Tokyo, Japan
E-mail: katahira@waseda.jp Tel/Fax: +81-03-5286-3544‡ Suntory Global Innovation Center Research Group (SIC) Ltd., Kyoto, Japan
E-mail: Naoyuki Matsuzaki@suntory.co.jp Tel/Fax: +81-0774-98-6262
Abstract—In recent years, a new type of value called the Kansei
value has been widely discussed, especially in the manufacturingindustry. Many studies have tried to quantify Kansei values
and found that human psychological and physiological reactions
have certain effects. To further examine the relationships among
psychological reactions, physiological reactions, and Kansei values, the main purpose of this study is to construct a predictivemodel for beverage preference evaluation. However, we foundthat the problem of how to obtain psychological and physiologicalreaction data without affecting people s Kansei value evaluations
has not been solved well. Therefore, this study proposes using
an arm robot instead of a human to perform a series ofactions when drinking, which solves the problem of measuring
psychological and physiological reactions simultaneously. Using
the psychological and physiological reactions collected by thismethod, we completed a predictive model of people s preference
evaluations. Our model s prediction accuracy is higher thanthat of the model using psychological evaluation or physiologicalreaction alone. This result proves again that psychological andphysiological reactions do affect people s preference evaluation
simultaneously. Through the model comparison, we also get theresult that the evaluation of people s preference for beverages
has been formed in tasting the beverages in the mouth.

I. I NTRODUCTION
In recent years, a new type of value called ”Kansei” value
has been widely discussed in the manufacturing industry. It
involves all aspects of human life, including clothing [1],food [2], transportation [3], cosmetics [4], and so on. To
design products better based on Kansei value, it is necessary
to quantify users psychological reactions evoked by products.
There are two types of psychological reactions: conscious andunconscious. Conscious and unconscious psychological reactions each have suitable quantiﬁcation methods. One method
is to use reactions expressed in language [5], measuring
subjective states via subjective evaluation. Another method is
to use psychophysiological reactions that cannot be expressedin language, such as facial expressions and EEG activity [6][7].
These states are obtained by objective measurement.
The mechanisms that form food s Kansei values arecomplex because of the combined inﬂuence of many sensory
modalities. To understand how conscious and unconscious
reactions affect people s Kansei values when eating food,many studies have reported on the relationship between food978-988-14768-9-0/21/$31.00 ©2021 APSIPA

preferences and subjective responses [2][11][12]. These studies were based on subjective evaluations obtained under naturaleating behaviors, and are considered able to correctly measure
the values provided by food in realistic situations. On theother hand, the unconscious state cannot be measured bysubjective evaluation, and its inﬂuence has not been clariﬁed.
The relationship between preferences and unconscious states
has also been studied in the ﬁeld of neuroscience [8][9][10].
However, in such studies, eating methods that have little effecton measurement, such as tube injection, have typically been
selected. Subjective response studies use the most natural
conditions, so the subjective reactions are very close to thereal situation. Still, because there are many external factors,
this method is unable to get accurate objective reactions.
Neuroscientiﬁc methods can obtain more accurate objective
reactions because they exclude external factors. Still, becauseof the excessive deviation between the experimental andnormal conditions, they cannot guarantee accurate subjective
reactions. To sum up, measuring subjective psychological andobjective physiological reactions at the same time without
affecting people s Kansei values is a difﬁcult problem.

One purpose of this study was to try to solve these problems. To measure psychological and physiological reactions
simultaneously, we used an arm robot instead of a human toperform a series of actions when drinking. In this way, theparticipants did not have to do anything, nor did they have toremain in an extreme environment to complete the drink intake
experiment. Therefore, we think this method can eliminate
errors in measuring brain waves to the greatest extent. Anotherpurpose of our study was to construct a predictive model forbeverage preference evaluation. We used psychological reactions (emotional evaluation) and physiological reactions (EEG
data) collected by our method to build a mathematical model
to predict the preference evaluation of beverages (coffee) andanalyze the psychological and physiological reactions effecton the preference evaluation. We also compared the different
effects of brain waves before and after swallowing and theireffects on preference evaluation.

366

APSIPA-ASC 2021

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

II. R ELATED STUDY
Researchers have made many attempts to measure subjective
psychological and objective physiological reactions. Some ofthem carried out action experiments under normal drinking
conditions, and others carried out fMRI experiments under
extremely unnatural conditions through infusion tubes.
For example, Tanaka et al. [8] studied the effect of ERPs
(event-related potential) on evaluating food image preferences.
In their study, the preference for food images was related to thearea of P300 components, which indicated that ERPs can beused as an index to evaluate people s preference for food.
To analyze how overall preference evaluations of food areformed, Sagara [2] constructed a food Kansei model. Thismodel mainly evaluated intrinsic attributes (such as fragrance)and extrinsic attributes (such as brand effect) to quantitatively
evaluate how the cognition ofdelicious
is generated.
Among many subjects, the subject of beverages has been
widely studied. Some studies examine the impact on Kansei
value by measuring the subjective reactions of the participants.
[11] studied preference evaluations by comparing the taste perception of drinking beverages with the ingredients containedin them [11]. Dalenberg [12] used preference evaluation andemotional factors to predict beverage choice. Dalenberg showsthat a model combining preference evaluation and emotional
factors has better predictive ability than one using preference
evaluation or emotional factors alone. In addition, some people
think that we must consider both subjective and objective
reactions. Samuel [9] and John [10] used tube infusion tomeasure both preference and brain activity when consuming
beverages. Their results showed that beverage preference isrelated to the frontal lobe activity of the brain [9], as well as
the ventral tegmental area and striatum of the brain [10].
III. E XPERIMENT

to evaluate each item and used a visual analysis scale (total
100 points) to evaluate participants beverage preferences.
TABLE I
JAPANESE - E NGLISH QUESTIONNAIRE TRANSLATION
Question Num.
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13

Japanese
気力に満ちた
活動的な
機敏な
エネルギッシュな
無気力な気分ののらない
生気がない
緊張した
落ち着かない気が休まらない
ゆったりした
落ち着いた
くつろいだ

Translation
powerful
activeagile
energetic
lethargic
not feeling like itlifelesstensionrestless
uneasycompatible
calmrelaxedD. Apparatus
In this experiment, a Dobot Magician arm robot was used
to replace the participants arms to complete a series ofhand movements when drinking coffee. This is to avoid any
unnecessary deviation of EEG measurement caused by thedifferent habits of the participants when drinking. We used
Biosemi s Active Two EEG system with a total of 64 electrodechannels and 6 additional channels on participants heads(sampling rate 1024 Hz). To measure laryngeal movement
during swallowing, we set up an ACC sensor near participants
larynxes. As participants swallowed, the sensor recorded theduration of activity (Fig. 1). We set up a joystick to control
the arm robot. Participants only needed to operate the joystick
to complete the intake of drinks. This made their actions
more uniform, reducing physiological reaction errors causedby drinking habits. To avoid the inﬂuence of noise, the wholeexperiment was carried out in a sound-insulated laboratory.

A. ParticipantsFirst, we conducted a questionnaire survey among participants and selected 32 (24 males and 10 females) who rated
their preference for coffee as do not hate or general. Afterremoving participants who were not completely measured by
EEG data, the actual number of participants was 24 (18 males
and 6 females). Their average age was 21.79 (SD = 1.194).
B. Stimuli
We selected ﬁve kinds of coffee with obvious differences inﬂavor from the market. The participants drank 15ml of eachsample, and the temperature of the drinks was maintained at
4 ◦ C.
C. Questionnaire
We selected evaluation questions from previous studies[14] to evaluate subjective emotions. There were 13 itemsin the questionnaire: powerful (Q1), active (Q2), agile (Q3),energetic (Q4), lethargic (Q5), not feeling like it (Q6), lifeless(Q7), tension (Q8), restless (Q9), uneasy (Q10), compatible(Q11), calm (Q12), and relaxed (Q13). Japanese - English
translation is shown in Table. 1. We used a 4-point Likert scaleFig. 1. Experimental equipment and data collection method

E. Procedure
Participants ﬁrst placed their chins on a jaw stand. After 10
seconds of resting state adjustment (Step 1 in Fig. 2), participants were prompted to start drinking coffee. The participants367Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

drank a random cup of coffee by turning the arm robot scontrol stick (Step 2 in Fig. 2), and tasted the coffee for 10
seconds (Step 3 in Fig. 2). To measure physiological activity
after swallowing, the participants were kept in place for 130
seconds (Step 4 in Fig. 2). After all these steps, the participantsevaluated their current mood and their preference for eachsample (Step 5 in Fig. 2). Finally, participants cleansed theirpalates with 15ml of drinking water to prepare for the next
round of experiments (Step 6 in Fig. 2). The experiment was
conducted for 15 trials.
We used the following method to record the drinking period:
ﬁrst, we calculated the angle at which the liquid ﬂows out of
the cup. When the joystick reached this angle, it sent a trigger
signal to an ADC converter, and we recorded this moment as
the start of drinking. Then, we divided the timing of laryngeal
movement recorded by the ACC sensor by the times before
and after swallowing. Finally, according to these time points,
we extracted the data from the collected EEG data. (see Data
Preprocessing section for detailed period division).
The present experiment with human participants was approved by the Internal Review Board of the Faculty of Letters,
Kwansei Gakuin University.

Tasting period (A period): This is the period from the mouth
stage (ﬁrst part of the swallowing stage) to the pharyngeal
stage (second part of the swallowing stage). The EEG data
corresponding to this period take the interval from drinking
the beverage to before the sensor measures the ﬁrst ﬂuctuationof the larynx (Steps 2 and 3 in Fig. 2).
Post-swallowing period (B period): This is the period fromthe pharyngeal stage to the esophageal stage. The EEG data
corresponding to this period takes the interval from after thesensor measures the ﬁrst ﬂuctuation of the pharynx to before
the second ﬂuctuation of the larynx (Step 4 in Fig. 2).
Difference between the A period and B period (AB period):
Considering that the difference of reaction between the A
period and B period may also be an inﬂuencing factor, we
calculated the variation of brain wave activity in the A period
compared with the B period.
IV. A NALYSIS
A. Processing and analyzing subjective emotion evaluationand EEG data
To make more reasonable use of subjective emotion evaluation and EEG data, we conducted a factor analysis on the
13 self-evaluated items responses and principal component
analysis on EEG data of 24 participants after drinking ﬁve
kinds of beverages.
B. Construction of mathematical model to predict preference
evaluation
As shown in Fig. 3, the distributions of the ﬁve samples preference evaluations were normal. We constructeda Bayesian regression model and choose the best model bymodel comparison. The best model was used to analyze theeffects of emotion and EEG on preference evaluation. We also
compare the EEG data before and after swallowing (A period,
B period, and AB period), to clarify which period was moresuitable to predict preference evaluation. The formulae of themodel are shown in Equations (1) and (2).

Fig. 2. Experimental procedure and different swallowing periods

F. Data preprocessing
We understood the mechanism of human swallowing basedon [13]. First, when swallowing, the hyoid rises, and theepiglottis presses down to close the pharynx to prevent foodfrom entering the trachea. During this period, the larynx rises.
When food enters the esophagus, the hyoid and epiglottis
return to their original positions and open the pharynx. During
this period, the larynx descends. Based on this mechanism,
we hypothesized that laryngeal movement could be used asa criterion to judge whether a participant was swallowing. In
addition, the literature describes four stages of swallowing,each with different movement patterns. They are the cognitive stage, preparatory stage, swallowing stage (this stage isdivided into mouth and pharyngeal stages), and esophageal
stage. To facilitate analysis, we set up a sensor to measurelaryngeal movement near participants larynxes. According
to the measured swallowing timing, we divided the process ofbeverage intake into the following periods (Fig. 2):

368

Fig. 3. Distribution of preference evaluations of ﬁve beverage samples

Proceedings, APSIPA Annual Summit and Conference 2021y ∼ N ormal(µ, σ)
µ=α+

n
∑

14-17 December 2021, Tokyo, Japan(1)

βn Vn + rstimulus + rparticipant , n ∈ 1...8 (2)n=1

In Equation 1, y is the result of preference evaluation, and µ
and σ are the mean and standard dispersion. In Equation 2,
βs represents the ﬁxed effect and the participants individual
differences (rparticipant ) and individual differences in sample
stimuli (rstimulus ) as random effects. n ∈ 1...8 means thatthe model uses up to 8 independent variables. This is because
EEG produces too many principal components (see result (b)).
Therefore, we carried out a single regression analysis on eachprincipal component in advance and selected the best fourparameters in A, B, and AB periods by model comparison(using WAIC as a judging standard). After adding the four
subjective emotion factors (see result (a)), we got the eight
independent variables used in the A, B, and AB period models.
The structure of the models are as follows:
M 1p = V 1p
M 2p = V 1p + V 2p
M 3p = V 1p + V 2p + V 3p...
M 8p = V 1p + V 2p + ... + V 8p
M 1p is a model with one independent parameter, M 2p
is a model with two independent parameters, and so on.
Finally,M 8p is a model with eight independent parameters.
V is the independent variable, p is the index of the period (A,B, or AB period). Finally, we calculated each models WAIC,
and the optimal model was selected.
We used RStan [15-17] for Bayesian parameter estimations.
The uniform prior was used for ﬁxed effects, and we used aweakly informative prior (gamma with α = 10, β = 10). We
used RStan s default settings for MCMC sampling. For eachmodel, there were four chains. Each chain had 1000 warm-up
steps, 2000 iterations, and a thin factor of 1. Thus, there werea total of 4000 MCMC samples for each model.
To verify whether MCMC samplings had converged, we
checked R̂ values. R̂ values for all coefﬁcients were less than
1.1, which is a typically used criterion, and we considered that
our MCMC sampling had converged.
We used the highest density interval (HDI) as a method todetermine the signiﬁcance of our estimation results. If the 95%
HDI did not contain 0, we considered the estimation result
signiﬁcant. The HDI indicates which points of a distribution
are most credible. Thus, the HDI speciﬁes an interval thatspans most of the distribution, such that every point inside
the interval has higher credibility than any point outside theinterval.
V. R ESULTS
A. Subjective emotion
We used factor analysis to analyze the participants subjective emotion evaluations and selected the best number offactors according to Kaiser Guttman s standard (results shownin Table II). We obtained four main factors. Because the ﬁrst
factor was related to activity, the second factor was related
to relaxation, the third factor was related to anxiety, and thefourth factor was related to low activity, we called them active,
relaxation, anxiety, and negative factors.
TABLE II
FACTOR ANALYSIS RESULTS OF SUBJECTIVE EMOTIONS
Index
Q1.powerful
Q4.energetic
Q2.active
Q3.agileQ8.tension
Q11.compatible
Q12.calm
Q13.relaxed
Q9.restless
Q10.uneasy
Q7.lifelessQ5.lethargic
Q6.not feeling like it

Fac 1
.944
.906
.881
.665
.352
-.030
.052
.203
.032
.017
.009
-.193
.059

Fac 2
-.024
.125
.228
-.195
-.336
.900
.898
.657
-.046
-.072
.057
.360
-.215

Fac 3
-.234
.128
.286
-.074
.146
-.033
.076
-.350
.865
.836
-.056
.312
.332

Fac 4
.083
-.210
-.186
.278
.267
.122
.000
.199
.028
.071
.889
.645
.341

B. EEG data
We analyzed EEG frequencies during the A, B, and AB
periods. Through this frequency analysis, we obtained thefrequency characteristics from time-series data via the Welch
method and calculated the representative frequency bands.
They are θ wave, α wave (lower α1 wave, Lower α2 wave andupper α wave), β wave and γ wave. The power calculation ofeach band refers to Dopplmayr s [18] results. We obtained thedifferences in EEG activity between each period (A period, B
period, and AB period) and the calm state through this processing method. Then, we used principal component analysis
to extract EEG features in each period, and the contribution
ratio was 80%. To construct a more convenient model, we ﬁrst
selected the four most representative components from the A,
B, and AB periods using WAIC as a judging standard (seeFig. 4). In the A period, A13 (lower α2 wave), A6 (θ wave),
A11 (lower α1 wave) and A18 (upper α wave) were selected.
During B period, B8 (lower α2 wave), B13 (β wave), B15(γ wave), and B4 (θ wave) were selected. During AB period,
ab12 (lower α2 wave), ab18 (β wave), ab21 (γ wave), andab16 (upper α) were selected. Each circle in the picture is a
top view of the head, with the face above and the back of
the head below. Red areas indicate positive brain activity, andblue areas indicate negative brain activity. Many yellow areas
indicate that the brain has a wide range of activities, and thatthe reaction involves the whole brain.
Notably, we considered the ﬁrst component of the lower
α2 wave the best principal component in all periods. On thewhole, more than half (58.3%) of the most representative
principal components selected occurred in the ﬁrst component (many yellow areas). According to [19], most of thesebrain activities originate from the basal ganglia. Most of theactivities in this area are closely related to emotion. At thesame time, the reaction of the frontal lobe (A6, A18, B4)
was also intriguing. This part of the activity is more closely369Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

related to cognitive processing [20]. These outcomes veriﬁed
the results of [9], indicating that frontal lobe reaction is related
to beverage preference evaluation.

Fig. 5. Pearson correlations of all independent variables

Fig. 4. Four most representative principal components by EEG model in A,
B, and AB periods. WAIC used.

C. Prediction model of preference evaluation
Before analysis, we checked the Pearson correlations of
all independent variables. As shown in Fig. 5, there wasno obvious correlation between the four subjective emotion
factors and the principal components of EEG.
As described in Analysis subsection B, we used four
emotional factors from the results (a) and 12 principal EEG
components (4 in each period) selected from the results (b) toconstruct the model according to M1 to M8. We also calculated
the WAIC values for each model (see Fig. 6). The WAICs
tended to be ﬂat from M2 onward, and reached a minimum at
M6. This means that M6 is the best model.
Table III summarizes the detailed structure of the bestmodels in M2 to M8. We found that all models except M2 werean emotional factor + EEG principal components in Period
A. Therefore, a model using both subjective emotion andobjective physiological reactions is better than a model using
subjective emotion or physiological reactions alone. Moreover,models constructed using EEG as their principal component in
Period A were better than those constructed based on principal
EEG components in the B or AB periods. This indicates that
it is very likely that the preference evaluation of drinks is

Fig. 6. Best WAIC values, models M1 to M8

formed during the tasting period ( Period A). The best model,
M6, is composed of active factors (Fac1), relaxation factors(Fac2), anxiety factors (Fac3), A6 (related to frontal lobe), A13(related to basal ganglia), and A18 (related to frontal lobe).
The activity distribution of brain wave reaction is shown in
Fig. 7.

370

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, JapanTABLE IIIS TRUCTURE OF THE BEST MODEL AMONG DIFFERENT MODEL TYPES
Model typesubjective
emotion
(M2)
M3
M4
M5
M6
M7
M8

Best model

Waic

Fac1+Fac3

967.193

Fac1+Fac3+A13
Fac1+Fac2+Fac3+A13
Fac1+Fac2+Fac3+A6+A13
Fac1+Fac2+Fac3+A6+ A13+ A18
Fac1+Fac2+Fac3+Fac4+ A6+ A13+ A18
Fac1+Fac2+Fac3+Fac4+A6+A11+A13+A18

964.345963.678963.913963.545965.064966.389

Fig. 8. Results predicted by M6 and actual participant responses

VI. D ISCUSSION

Fig. 7. Best model s EEG activity

Table IV shows the results of the signiﬁcant effects of theemotional factors and principal EEG components on preference evaluation as analyzed by M6. We considered an effectsigniﬁcant if the 95% HDI interval did not contain zero. The
results showed that the higher the activity factor was, thehigher the preference evaluation was; the higher the anxiety
factor was, the lower the preference evaluation was. We foundno signiﬁcant effects between principal EEG components andpreference evaluation.
TABLE IVS IGNIFICANT EFFECT RESULTS (M6)
Predictor
Fac1
Fac3

Mean
8.902
-7.74095%HDI
6.190 ∽ 11.409-10.499 ∽ -4.899

Finally, we used M6 to predict participants preference
evaluations. As shown in Fig. 8, the x axis is participants
actual responses, and the y axis is the results predictedby M6. The more consistent the predicted results are withthe actual answers, the more concentrated the ﬁnal results
will be on a diagonal line at an angle of 45 degrees. Our
results show that M6 basically accurately predicts participantspreference evaluations. However, some lower-level evaluations
were still not predicted successfully. We think this is because
the experimental sample size was insufﬁcient. If we increase
the number of experimental samples, this problem should be
solved.

This study used subjective emotion and objective physiological reactions (EEG) to build a Bayesian mathematicalmodel to predict beverage preference evaluations. We also
compared EEG data before and after swallowing in threedifferent periods to help predict preference evaluation. Then,
we used the best model to analyze the inﬂuence of various
related factors on preference evaluation. First, we obtained
four factors related to emotion through factor analysis. Then,
we used principal component analysis to extract the principal
EEG components in each period, and the contribution ratio was
80%. To construct the model more conveniently, we selected
the best four principal components from the tasting period,
the post-swallowing period, and the difference between the
two. We selected EEG components for each period based on
WAIC and positive brain activity. Notably, the best parameterselected by model comparison was the ﬁrst component oflower α2 wave regardless of the period. More than half of themost representative principal components selected occurred in
the ﬁrst component (many yellow areas). This kind of brain
activity is derived from the basal ganglia, and most of thiskind of brain activity is related to emotion. This result showsthat people s preference for drinks is more subjective. At thesame time, the reaction with the frontal lobe (A6, A18, B4) isalso very interesting because the reaction of the frontal lobe isrelated to the brain s cognitive processing. This result meansthat evaluating beverage preference also involves cognition
about beverage information.
The optimal models from M3 to M8 were combinations ofemotional factors and principal EEG components. All principal
EEG components were reactions to the tasting period (Period
A). This result shows that a model constructed using bothsubjective evaluation and objective physiological reaction isbetter than using subjective evaluation or objective physiological reaction alone. The effect of reaction in Period A on
preference evaluation is better than that in B and AB periods.
This result shows that people s preferences for drinks are371

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

formed during the early stages of the cognitive activity of
tasting.
Our model comparison results show that M6 is the bestmodel. M6 is composed of active factors, relaxation factors,
anxiety factors, A6 (related to the frontal lobe), A13 (related
to the basal ganglia) and A18 (related to the frontal lobe).
Although A6, A13, and A18 are related to emotion andcognition, only active and anxiety factors showed signiﬁcanteffects on preference evaluation. This result indicates that theevaluation of beverage preference is primarily on subjective
judgement.
VII. C ONCLUSIONSIn this study, we measured psychological and physiologicalreactions at the same time without affecting people s Kansei
values by using an arm robot instead of a human to perform
a series of drinking actions. At the same time, we usedsubjective emotional and objective physiological reactions
collected via this method to build a predictive model forbeverage preference evaluation and analyzed the correlations
among various parameters. The relationship between brain
wave and preference evaluation was the same as in previous
studies. This result proves that the data collected using ourmeasurement method is highly reliable. At the same time, ourpredictive model also shows that the model s predictive ability
using subjective emotion and physiological reaction is greaterthan that of models using only subjective emotion. This result
proves that both psychological and physiological reactions
inﬂuence evaluation results, reﬂecting people s Kansei values.
Next, we will adjust the parameters to continue optimizing
the model and increase the sample size to check the models credibility.

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