Electrodermal Activity Evaluation of Player Experience in Virtual

Reality Games: A Phasic Component Analysis

Diego Navarro

, Valeria Garro

and Veronica Sundstedt

Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden

Keywords:

Virtual Reality, Psychophysiology, Electrodermography, Phasic Component, Player Experience.

Abstract:

Electrodermal activity (EDA) is considered to be an effective metric for measuring changes in the arousal

level of people. In this paper, the phasic component of EDA data from players is analyzed in relation to their

reported experience from a standardized questionnaire, when interacting with a couple of virtual reality games

that featured two different input devices: the HTC Vive and Leap Motion controllers. Initial results show that

there are no signiﬁcant differences in the phasic component data, despite having signiﬁcant differences in their

respective player experience. Furthermore, no linear correlations are found between the phasic component

data and the evaluated experience variables, with the only exception of negative affect which features a weak

positive correlation. In conclusion, the phasic component of EDA data has here shown a limited correlation

with player experience and should be further explored in combination with other psychophysiological signals.

1 INTRODUCTION

Electrodermal activity (EDA) is considered a very

efﬁcient methodology for measuring the changes

in arousal levels of players when playing video

games (Navarro et al., 2021). Because of this, sev-

eral publications have used EDA measurements in the

assessment of different variables in game research,

such as the emotional responses (Bontchev, 2016;

Moghimi et al., 2017) or the cognitive loads (Buch-

wald et al., 2019) of players.

A predominant area in which EDA data has been

analyzed in previous publications is the assessment

of player experience. Multiple studies have used

EDA data to quantitatively evaluate different experi-

ence variables in game research (Drachen et al., 2010;

Martey et al., 2014; Ang, 2017). However, the ma-

jority of those studies have focused on games played

on regular 2D screens. With the introduction of vir-

tual reality (VR) technologies into the consumer mar-

ket, novel interaction techniques have emerged, af-

fecting the manner in which players experience video

games. Few publications have explored the effects

that these novel interaction techniques may have in

the EDA data from players and their respective expe-

https://orcid.org/0000-0003-1503-8856

https://orcid.org/0000-0002-9527-4594

https://orcid.org/0000-0003-3639-9327

riences (Egan et al., 2016), offering the opportunity

to further explore the relationship that may exist be-

tween these variables.

Therefore, this study presents the following re-

search question: How may the differences in player

experience relate to the variations of their respective

EDA data in VR games?. In particular, this study fo-

cuses on analyzing the phasic component (see Sec-

tion 2) from players’ EDA data, gathered in a pre-

vious experimentation. We hypothesize that there

is a strong relationship between the EDA data from

players and their respective game experience: sig-

niﬁcant differences in the arousal level of players

during gameplay, and consequently signiﬁcant differ-

ences over the phasic component of EDA data, may be

an indicator of signiﬁcant differences in the reported

player experience.

2 BACKGROUND

Electrodermal activity, also known as galvanic skin

response (GSR), is deﬁned as the measurement of the

variations in the electrical conductivity on the skin,

due to Eccrine sweat glands activity (Boucsein, 2012;

Tasooji et al., 2019). EDA measurements focus on

applying an imperceptible amount of electric voltage

on the skin and measure the variation of the speed

in which this voltage travels trough it. When strong

108

Navarro, D., Garro, V. and Sundstedt, V.

Electrodermal Activity Evaluation of Player Experience in Virtual Reality Games: A Phasic Component Analysis.

DOI: 10.5220/0011006100003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 2: HUCAPP, pages

108-116

ISBN: 978-989-758-555-5; ISSN: 2184-4321

emotional reactions are experienced by people (i.e.

increase in their arousal levels), the body increases the

levels of perspiration on the skin and, consequently,

its electrical conductivity. Therefore, increases in the

EDA signal are often associated with increases in the

arousal level of people (Boucsein, 2012).

EDA signals have two main components called

tonic and phasic. The tonic component describes the

slow and gradual changes in the EDA signal over

time, establishing a baseline for the EDA signal called

skin conductance level (SCL). The phasic component

focuses on identifying quick and abrupt changes in

the EDA signal, commonly referred to as a skin con-

ductance response (SCR) (Boucsein, 2012; Navarro

et al., 2021).

3 RELATED WORK

Several publications have explored EDA data and

their potential relationship with multiple variables

from the player experience. An initial study by Ravaja

et al. (2006) focused on analyzing the changes of

the phasic component data during gameplay, com-

bining data from electromyography, heart rate, and

EDA; and showing a strong positive relationship be-

tween phasic increases of arousal and in-game re-

wards. A further study from Drachen et al. (2010)

aimed to generate a quantitative understanding of

player experience by analyzing its potential correla-

tion with EDA and heart rate data. A set of ﬁrst-

person shooting games were used in this paper, show-

ing signiﬁcant correlations between EDA data and

reported player experience, despite having limited

covariance between the physiological metrics. An-

other study using ﬁrst-person shooting games was

presented by Nacke et al. (2010), exploring the ef-

fects of turning on and off the sound effects and mu-

sic on the arousal responses from players. The results

showed that changes in sonic stimuli had little effect

on players’ EDA data, despite showing signiﬁcant ef-

fects over reported player experience. In (Klarkowski

et al., 2016, 2018) challenge, and the relationship

with EDA signals, were analyzed. Despite discrepan-

cies with extant literature, results from these studies

show a directly proportional relation between player

arousal and game challenge. Few articles, however,

have explored the variation of EDA data in VR games.

One such study compared the effects of VR and non-

VR environments in players’ EDA and heart rate

data (Egan et al., 2016), ﬁnding signiﬁcant differences

between the gathered physiological metrics.

The analysis presented in this paper is based on

additional EDA data gathered in an earlier experi-

ment that evaluated player performance and experi-

ence when manipulating objects in two virtual reality

games (a pentomino puzzle and a ball throwing task)

with two different interaction devices: the HTC Vive

controller and the Leap Motion Controller. More de-

tails on the earlier experiment design and procedure

that is the basis for the EDA data gathering can be

found in (Navarro and Sundstedt, 2019). The per-

formance evaluation was carried out by analyzing the

amount of piece grabs require to complete the puzzle

in the pentomino game, and the number of throws re-

quired to hit all targets in the ball throwing game. Ad-

ditionally, completion times were included in this part

of the analysis. The experience evaluation was done

through a set of three questionnaires, two applied af-

ter completing the games with each respective inter-

action device, and one at the end of the experiment.

The questionnaires where a modiﬁed version of the

Game Experience Questionnaire (GEQ), a standard-

ized survey to evaluate player experience (IJsselsteijn

et al., 2013).

As an outcome of the previous work, the HTC

Vive was reported to offer an improved overall sub-

jective experience. The performance was reported to

decrease when using the Leap Motion controller and

gesturing with the hands was not perceived as reliable

as when using the HTC Vive for input control. How-

ever, the earlier work also showed potential in terms

of positive responses for both controllers, in particular

relating to enjoyment. Since there was a previous sig-

niﬁcant difference reported in the player performance

it was considered relevant to also analyze the gathered

EDA data collected during the experiment in further

detail.

4 METHODOLOGY

A statistical evaluation between player EDA signals

and the reported experience results was established as

the main methodology for this study. Speciﬁcally, an

analysis of the phasic component of the EDA data and

its potential relationship with player experience vari-

ables.

4.1 Phasic Component Analysis

For the phasic component analysis, the metric peaks

per minute was used. Peaks per minute highlight the

ratio in which EDA signal peaks occurred within the

time required by players to complete each game. This

metric aims to compare how the different input de-

vices featured in each game might have affected the

arousal levels of players and, subsequently, their re-

Electrodermal Activity Evaluation of Player Experience in Virtual Reality Games: A Phasic Component Analysis

109

spective perceived experience results. The process

used to calculate the peaks per minute (P

min

) is shown

in Equation 1, where P represents the total number of

peaks (automatically detected by iMotions) between

the initial exposure time (t

in ms) and the completion

time (t

in ms), over the completion time divided by

6 ∗ 10

ms.

min

∑

6∗10

(1)

Peaks per minute were calculated for each game

(pentomino and ball throwing), and for each input de-

vice (HTC Vive controller and the Leap Motion con-

troller). A third measurement was done over the entire

exposure with each input modality, adding the peaks

from both games together.

4.2 Statistical Analysis

Several statistical analyses were carried out with the

calculated peaks per minute, and the reported player

experience results from the GEQ. First, the ANOVA

assumptions were tested for all the data sets using the

Shapiro-Wilk test for normality, and the Levene’s test

for homoscedasticity. Only the data sets that satisﬁed

the ANOVA assumptions were later compared using

a paired t-test, while all others were compared by us-

ing a Wilcoxon signed-rank test. Lastly, to evaluate

any potential correlation that might exist between the

calculated peaks per minute and the variables tested

in the GEQ, a Pearson correlation coefﬁcient was cal-

culated with the data from the pentomino game, the

ball throwing game, and the overall experience.

4.3 Software Tools

To capture the participants’ EDA data, the iMotions

platform (iMotions, 2021) and the Shimmer3 GSR+

sensor were used. IMotions recorded and processed

the raw data captured by the GSR sensor, allowing us

to directly export the phasic component data. These

phasic data were later imported, processed, and ana-

lyzed using a script developed in Python (version 3.8).

The script used three main libraries to carry out the

analyses: Pandas (Pandas, 2021) was used to read,

store, and manipulate data frames; ScyPy (Virtanen

et al., 2020) was used to carry out the statistical sig-

niﬁcance analyses; and Matplotlib (Matplotlib, 2021)

was used to plot the calculated data.

4.4 Ethical Considerations

The EDA data can be considered a sensitive metric

since it allows to identify when and how much the

arousal levels of a person change, when exposed to

a speciﬁc stimulus. Therefore, this study made the

identity of the players conﬁdential, and made no di-

rect links between the participants and the gathered

EDA data. The study was submitted to a regional

ethics board in Sweden for its evaluation, and was

granted ethical approval (dnr: 2018/624).

5 RESULTS

A total of 20 participants volunteered for the experi-

ment. However, only data from 18 of them were ana-

lyzed and reported in this study. The EDA data from

participants 15 and 20 were affected by noise at the

end of the exposure with the Leap Motion controller,

which generated incomplete data sets when exported

from iMotions. Therefore, data from these partici-

pants were excluded from the analysis.

5.1 Peaks per Minute

For each game, two different peaks per minute data

sets were calculated, one for each input device.

Moreover, we computed also two additional peaks

per minute data sets for the entire stimuli exposure,

adding together the peaks per minute from the pen-

tomino and the ball throwing games for each interac-

tion device. An overview of the distribution of the

calculated peaks per minute is shown in Figure 1.

5.1.1 Pentomino Game

Both peaks per minute data sets for the pentomino

games passed the normality and homoscedasticity

tests, allowing the use of the paired sample t-test to

compare the data. Results from the test showed that

there was not a statistically signiﬁcant difference in

the peaks per minute from players when playing the

pentomino game:

t − test

(1,n=18)

= 0.89, p > 0.05

5.1.2 Ball Throwing Game

The peaks per minute data set for the Leap Mo-

tion passed the normality test, while the one for the

HTC Vive controller failed it. Therefore, a Wilcoxon

signed-rank test was used to compare these data sets,

showing that there was not a statistically signiﬁcant

difference in the peaks per minute from players when

playing the ball game:

W SR

(1,n=18)

= 48, p > 0.05

HUCAPP 2022 - 6th International Conference on Human Computer Interaction Theory and Applications

110

Figure 1: Calculated peaks per minute for the pentomino game, ball throwing game, and the entire stimuli exposure, between

the HTC Vive controller and the Leap Motion controller.

5.1.3 Entire Stimuli Exposure

The entire stimuli exposure data sets added the peaks

per minute from both games that used the same in-

put device. Both data sets that were calculated this

way passed the normality and homoscedasticity tests

and were compared using a t-test. Results from the

test showed that there was not a statistically signiﬁ-

cant difference in the players’ peaks per minute when

using the HTC Vive and the Leap Motion:

t − test

(1,n=18)

= 0.10, p > 0.05

5.2 GEQ Results

Six different data sets (one per input device) were

created from the answers gathered in the GEQ: two

for the pentomino game experience, two for the ball

throwing game experience, and two for the overall ex-

perience reported by players.

In the GEQ, two different sets of variables were

used to evaluate player experience. The ﬁrst set was

used to evaluate the experience when playing each of

the games, exploring the perceived competence, level

of challenge, tension, positive affect, and negative af-

fect from players. The second set explored the over-

all experience of using each input device in terms of

the perceived enjoyment, ease of use, sense of control,

and preference from players[CITE].

5.2.1 Pentomino Game

All experience variables failed the normality test

for the pentomino game. Therefore, the Wilcoxon

signed-rank test was used to evaluate the perceived

player experience. An overview of the distributions

from the results obtained in the GEQ for the pen-

tomino game is shown in Figure 2 . These results

from the test showed that there was a statistically sig-

niﬁcant difference among all evaluated variables, with

the exception of the perceived negative affect:

• Competence: W SR

(1,n=18)

= 1, p < 0.05

• Level of challenge: W SR

(1,n=18)

= 3.5, p < 0.05

• Tension: W SR

(1,n=18)

= 8, p < 0.05

• Positive affect: W SR

(1,n=18)

= 10.5, p < 0.05

• Negative affect: W SR

(1,n=18)

= 8, p > 0.05

5.2.2 Ball Throwing Game

All experience variables failed the normality test for

the ball throwing game. The only exception occurred

with the perceived competence, which also passed the

homoscedasticity test. Given this, a t-test was used

to evaluate the perceived competence, while all other

variables were evaluated with the Wilcoxon signed-

rank test. An overview of the distributions from the

results obtained in the GEQ for the ball throwing

game is shown in Figure 3. Results from these tests

showed that there was a statistically signiﬁcant differ-

ence between all evaluated experience variables:

• Competence: t −test

(1,n=18)

= 3.05, p < 0.05

• Level of challenge: W SR

(1,n=18)

= 9, p < 0.05

• Tension: W SR

(1,n=18)

= 20.5, p < 0.05

• Positive affect: W SR

(1,n=18)

= 12, p < 0.05

• Negative affect: W SR

(1,n=18)

= 11, p < 0.05

5.2.3 Overall Experience

An overview of the distributions from the results ob-

tained in the GEQ for the overall experience is shown

in Figure 4. The variables used to assess the overall

Electrodermal Activity Evaluation of Player Experience in Virtual Reality Games: A Phasic Component Analysis

111

Figure 2: Results of each variable evaluated with the GEQ

in the pentomino game, for the HTC Vive controller and the

Leap Motion controller.

Figure 3: Results of each variable evaluated with the GEQ

in the ball game, for the HTC Vive controller and the Leap

Motion controller.

HUCAPP 2022 - 6th International Conference on Human Computer Interaction Theory and Applications

112

experience failed the normality test, and were evalu-

ated using the Wilcoxon signed-rank test. The results

of these tests showed that all variables had a statisti-

cal signiﬁcant difference, with the exception of enjoy-

ment:

• Enjoyment: W SR

(1,n=18)

= 9, p > 0.05

• Ease of use: W SR

(1,n=18)

= 3.5, p < 0.05

• Sense of control: W SR

(1,n=18)

= 4, p < 0.05

• Preference: W SR

(1,n=18)

= 18.5, p < 0.05

5.3 Correlation Analysis

The correlation analysis evaluated the potential linear

correlation between the peaks per minute and GEQ

Scores. Results from this analysis were classiﬁed into

ﬁve different categories, based on the results obtained

in the Pearson coefﬁcient (ρ) (Shevlyakov and Oja,

2016):

• A strong positive correlation when ρ was greater

than 0.8.

• A weak positive correlation when ρ was greater

than 0.4 but lesser than 0.8.

• No correlation when ρ was between the values of

0.4 and -0.4.

• A weak negative correlation when ρ was lesser

than -0.4 but greater than -0.8.

• A strong negative correlation when ρ was lesser

than -0.8.

Lastly, this analysis should be understood as ex-

ploratory and no correction for multiple comparisons

was performed on it.

5.3.1 Pentomino Game

For the HTC Vive controller, only the negative affect

showed a weak positive correlation with the peaks per

minute. All other GEQ variables showed no correla-

tion:

• Competence: ρ

(1,n=18)

= 0.017, p > 0.05

• Challenge: ρ

(1,n=18)

= −0.039, p > 0.05

• Tension: ρ

(1,n=18)

= 0.048, p > 0.05

• Positive affect: ρ

(1,n=18)

= −0.26, p > 0.05

• Negative affect: ρ

(1,n=18)

= 0.516, p < 0.05

For the Leap Motion controller, all GEQ variables

showed no correlation with the peaks per minute:

• Competence: ρ

(1,n=18)

= −0.057, p > 0.05

• Challenge: ρ

(1,n=18)

= −0.202, p > 0.05

• Tension: ρ

(1,n=18)

= −0.280, p > 0.05

• Positive affect: ρ

(1,n=18)

= −0.278, p > 0.05

• Negative affect: ρ

(1,n=18)

= −0.104, p > 0.05

Figure 4: Box plots from the results of each evaluated vari-

able with the GEQ for the overall player experience with

each input device.

5.3.2 Ball Throwing Game

For the HTC Vive controller, the variables of chal-

lenge, tension, and negative affect showed a weak

Electrodermal Activity Evaluation of Player Experience in Virtual Reality Games: A Phasic Component Analysis

113

positive correlation with the peaks per minute. All

other GEQ variables showed no correlation:

• Competence: ρ

(1,n=18)

= −0.259, p > 0.05

• Challenge: ρ

(1,n=18)

= 0.487, p < 0.05

• Tension: ρ

(1,n=18)

= 0.568, p < 0.05

• Positive affect: ρ

(1,n=18)

= 0.063, p > 0.05

• Negative affect: ρ

(1,n=18)

= 0.683, p < 0.05

In the case of the Leap motion controller, all

GEQ variables show no correlation with the peaks per

minute:

• Competence: ρ

(1,n=18)

= −0.329, p > 0.05

• Challenge: ρ

(1,n=18)

= 0.346, p > 0.05

• Tension: ρ

(1,n=18)

= −0.125, p > 0.05

• Positive affect: ρ

(1,n=18)

= −0.233, p > 0.05

• Negative affect: ρ

(1,n=18)

= 0.230, p > 0.05

5.3.3 Overall Experience

For both, the HTC Vive controller and the Leap Mo-

tion controller, all GEQ variables evaluated in the

overall experience showed no correlation with the cal-

culated peaks per minute for the entire stimuli expo-

sure. The results for the HTC Vive were:

• Enjoyment: ρ

(1,n=18)

= −0.094, p > 0.05

• Ease of use: ρ

(1,n=18)

= −0.127, p > 0.05

• Sense of control: ρ

(1,n=18)

= −0.001, p > 0.05

• Preference: ρ

(1,n=18)

= −0.154, p > 0.05

Similarly, the results obtained for the Leap Motion

controller were:

• Enjoyment: ρ

(1,n=18)

= −0.203, p > 0.05

• Ease of use: ρ

(1,n=18)

= −0.137, p > 0.05

• Sense of control: ρ

(1,n=18)

= −0.073, p > 0.05

• Preference: ρ

(1,n=18)

= −0.228, p > 0.05

6 DISCUSSION

The results from this study showed that there were

no signiﬁcant statistical differences in the peaks per

minute within the players in the evaluated games.

However, the reported player experience did show

a signiﬁcant statistical difference in the majority of

variables evaluated with the GEQ. In addition to this,

no strong correlation between the peaks per minute

and the evaluated experience variables was found in

the analysis.

These results show an initial discrepancy with our

proposed hypothesis. Nevertheless, this is not enough

evidence to propose its rejection with certainty. This

study should be interpreted as a work in progress and

reports only on the initial results from the phasic com-

ponent analysis. Previous publications that showed a

correlation between player experience and EDA data

focused on analyzing the tonic component (Drachen

et al., 2010), suggesting that variations in the skin

conductance level may display a stronger relationship

with the differences in the player experience, than the

variations of the peaks per minute.

Despite this, the results obtained in the phasic

component analysis showed an interesting consis-

tency with previous publications that proposed alike

methodologies, showing that positive correlations

have only been found between EDA data and negative

affects of player experience (Drachen et al., 2010).

Furthermore, the peaks per minute seemed to be af-

fected by the different game genres. An initial evalu-

ation of the peaks per minute showed statistically sig-

niﬁcant differences between the pentomino and ball

throwing games for both, the HTC Vive controller

(W SR

(1,n=18)

= 0.0, p < 0.05) and the Leap Motion

controller (t − test

(1,n=18)

= 9.41, p < 0.05). Analyz-

ing the changes that different game genres may gen-

erate in players’ phasic component data is beyond the

scope established for this paper, but the results ob-

tained for the peaks per minute suggest that this could

be a potential relationship to further explore.

It has been suggested that the correlation between

experience variables and EDA data can be limited,

since the game genres and experimental approach can

affect the measurements (Martey et al., 2014; Egan

et al., 2016). Therefore, the use of multiple method-

ologies for relating player experience with EDA data

is recommended (Navarro et al., 2021).

7 CONCLUSION AND FUTURE

WORK

This article has presented an initial analysis of the

phasic component of EDA data gathered from play-

ers and evaluated its potential relationship with their

respective player experience. Initial results show that

there were no statistically signiﬁcant differences in

the calculated peaks per minute within players, while

having statistically signiﬁcant differences in their re-

spective experience; suggesting that phasic compo-

nent data captured during gameplay does not vary sig-

niﬁcantly in terms of the player experience. However,

a weak positive linear correlation was evidenced be-

tween the peaks per minute and the negative affect of

HUCAPP 2022 - 6th International Conference on Human Computer Interaction Theory and Applications

114

players, which is consistent with the results disclosed

in other publications.

Future work should focus on continuing this eval-

uation, expanding over the analysis of the tonic com-

ponent of EDA data. In addition to this, other evalu-

ation metrics, such as heart rates, should also be con-

sidered when analyzing the potential relationships be-

tween player physiology and player experience, due

to the limited correlation that may exist with EDA

data alone.

ACKNOWLEDGEMENTS

This work has been ﬁnanced partly by KK-stiftelsen

Sweden, through the ViaTecH Synergy Project (con-

tract 20170056).

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