Proceeding
Supplementary Issue: Summer Conferences of Sports Science. Costa Blanca Sports Science Events, 25-26 September 2020. Alicante, Spain.

Comparison of EEG biofeedback and visual searchstrategies during e-sports play according to skill level
RYOUSUKE FURUKADO1,3, GOICHI HAGIWARA2, TOMOHIRO ITO3, HIROHISA ISOGAI2,3

1Integrated System Engineering, Faculty of Engineering, Nishinippon Institute of Technology, Fukuoka, Japan
2Department of Sport Science and Health, Faculty of Human Science, Kyushu Sangyo University, Fukuoka, Japan
3Fukuoka Sports Research Consortium, Fukuoka, Japan

ABSTRACT
About 83% of the perceptual information humans receive from the outside world comes through the visual
domain, making the tracking of visual information important for the superior performance of e-sports players.
In addition, it is known that e-sports players’ emotional state during performance affects their results.
However, few studies have examined visual search strategies and electroencephalogram (EEG) findings of
e-sports players while engaged in e-sports. Therefore, the present study aimed to investigate thecharacteristics of visual search activity and EEG feedback during first person shooter (FPS) game play, inwhich the tracking of visual information is important, and to examine the characteristics of different gaming
skill levels. Four skilled and five semi-skilled e-sports participants (mean age ± 19.11, SD = 0.99) participatedin this study. Gazepoint GP3 (Canada) was used to measure eye movements, and the Sports KANSEI(Littlesoftware Inc., Japan) was used to analyse the emotional state of the participants by using EEG data.
The results showed that the skilled e-sports players checked the camera significantly more frequently than
the semi-skilled participants. The results of the area of interest (AOI) analysis showed that skilled e-sports
players glanced at the friendly team information displayed at the top of the game screen more often thansemi-skilled players. Furthermore, it was found that skilled e-sports players had significantly higher average
EEG relaxation values during gameplay than semi-skilled players. Thus, the visual search strategy andemotional state while playing e-sports differed according to gaming skill level.
Keywords: Visual search strategy; Eye tracking; EEG; e-sports; FPS games.
Cite this article as:
Furukado, R., Hagiwara, G., Ito, T., & Isogai, H. (2020). Comparison of EEG biofeedback and visual searchstrategies during e-sports play according to skill level. Journal of Human Sport and Exercise, 15(4proc), S1123S1132. doi:https://doi.org/10.14198/jhse.2020.15.Proc4.13

1

Corresponding author. Integrated System Engineering, Faculty of Engineering, Nishinippon Institute of Technology, 800-0394,
Kanda-machi, Miyako-gun, Fukuoka, Japan.
E-mail: furukado@nishitech.ac.jp
Abstract submitted to: Spring Conferences of Sports Science. Costa Blanca Sports Science Events, 19-20 June 2020. Alicante,
Spain.
JOURNAL OF HUMAN SPORT & EXERCISE ISSN 1988-5202© Faculty of Education. University of Alicantedoi:10.14198/jhse.2020.15.Proc4.13

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Furukado, et al. / A study on eye tracking and brainwaves during e-sports gameplay JOURNAL OF HUMAN SPORT & EXERCISEINTRODUCTION
The e-sports market size is growing annually with numerous events taking place globally and is valued at$159.3 billion in 2020. Many colleges and universities already have national e-sports teams, and some offer
scholarships, similar to sports scholarships (Morrison, 2018). A wide variety of game genres exist in e-sports
and can be broadly classified into 3 main categories: multiplayer online battle arena games (MOBA), roleplaying games, and shooting games (Bonny & Castaneda, 2017).
In first-person shooter (FPS) games, targets are meant to move arbitrarily through space depending uponplayers’ subjective points of view, and require constant adaptive actions based on stimulus information
obtained from audio-visual sources and past personal experience. FPS games are characterized by a fasterpace than MOBA games (Alloza et al., 2018), and are also highly competitive, with multiple players working
together to achieve shared goals and perform their respective roles for their teams to win. For instance,
players need to understand information about the maps used in the game, and the properties of thecharacters they control (each weapon’s speed of movement and recoil control). This knowledge is to someextent, acquired naturally through one’s experience of playing the game.
In addition, it is said that during gameplay, one's environment changes in real time, and faster decisionmaking, strategizing, and reaction times are, therefore, required to perform well in the game (Sousa et al.,2020). When confronted with an enemy player, quick and accurate decisions are important since players
have less than a second to decide whether to fire or retreat, and this is called the decision-making ability.
Various aspects of situational decision-making ability in elite athletes have been discussed in studies ofexpertise in open-skill sports, such as soccer and basketball. Kikumasa and Kokubu (2018) found that skilledindividuals' unique visual search strategies contribute to good situational decision-making abilities. Furukado
and Isogai (2020), too, pointed out the importance of visual search strategies that use empirical knowledge
from the sporting context to predict what is likely to occur next in complex and dynamic sporting situations.
Previous research, though limited in number, had also examined visual search strategies during FPS
gameplay. Soderberg et al. (2012) examined the differences between visual search strategies adopted bymilitary personnel and civilians during FPS gameplay and found that military personnel tended to pay lessattention than civilians to the region of interest for tactical objects. To summarize the research to date, manystudies have investigated visual search strategies in real sports such as soccer and basketball, but very few
have investigated these strategies during FPS gameplay. When, where, and how to look, is considered
important not only for FPS games but also for improving gaming skills.
Additionally, psychological skills are also important for the performance of e-sports. Hung et al. (2019)
reported that during e-sports like gameplay, emotions affect players' performance. There is no arguing thatin global tournaments, in which the future of players is decided with huge prizes at stake, there is a lot of
psychological pressure on players. In support of this fact, previous studies have shown that during gameplay,heart rate increases to near sprint status (approximately 190–200) (Valladao et al., 2019). There are also
psychologists, who currently provide mental training guidance to professional e-sports teams. If we canunderstand the players’ specific emotional states during gameplay, we can also use this information for
mental training guidance. Hagiwara, Kawahara, et al. (2020) examined electroencephalogram (EEG)
measurements to understand emotional states during race gameplay. The results showed that the power
percentages of the beta and sensorimotor rhythm (SMR) wave bands obtained from FP1 after race gameplay
had significantly increased compared to the resting period. Campbell et al. (2018) suggested that e-sports
genres should be distinguished with clear distinctions between them, in the same way as different traditional
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sports. In addition, little is known about effective training methods designed to optimize e-sports performance(Martin-Niedecken and Schättin, 2020).
Therefore, the present study aimed to investigate the characteristics of visual search activity and EEG
feedback during FPS gameplay, in which the tracking of visual information is important for examining thecharacteristics of different gaming skill levels. Thus, examining e-sports players’ superior performance bymeasuring their physiological indices during FPS gameplay may lead to the accumulation of knowledge forthe development of research expertise. In addition, this study’s results could be used to pilot a proposal for
an efficient training method to solve the problem of game addiction.
METHODS
Participants
Nine male FPS players participated—5 skilled and 4 semi-skilled, mean (M) age of 19.11, standard deviation(SD) = 0.99—who usually played games for at least 5 hours per day and 6 days a week. The grouping criteria
were based on the match making rate (MMR) of “Tom Clancy’s Rainbow Six: Siege” (Ubisoft, 2015), the FPS
game used in this study. The outline and purpose of the study was explained to the participants, and theirdata and informed consent were obtained. All the participants had normal or corrected-to-normal vision.
Apparatus
The devices and environment used in the experiments are shown in figure 1 and table 1. We used the mouse,mouse pad, and keyboard that the participants generally used. Visual search strategies were measured using
the Gazepoint GP3 HD eye tracker (Gazepoint, Canada). The sampling rate was 150 Hz, and the minimum
degree of resolution ranged from 0.5–1. EEG was measured using a device with a simple band-type EEG
signal collection chip (Neuro Sky Co., Ltd, Japan), and the acquired data were recorded on a tablet device
connected through Bluetooth. The acquired data were evaluated by using Sports KANSEI (Littlesoftware Inc.,
Japan), an application that outputs sensitivity values.

Figure 1 Equipment used in the experiment.
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Table 1. Detailed information on equipment used in the experiment.
Manufacturer
ThirdWave (THIRDWAVE Corp.)
OS
Windows 10 Pro64bit
Power supply (PSU)
Owltech AS 700 (700W/Gold+)Motherboard
ASUS H370-A-SI
Processor (CPU)Intel Core i7-9700K
Graphics Card (GPU)
GeForce RTX 2080
Memory (RAM)
DDR4 16GB（8GB×2）
Storage drive
W.D. M.2 SSD 500GB and TOSHIBA HDD 3TB (7200rpm)
Cooling (CPU cooler)
DEEPCOOL / H-GAMMAXX400
Monitor
BenQ ZOWIE XL LCD (24 inch, refresh rate at 144Hz, for gameplay),iiyama XU2292HS-B1A (21.5 inch, for measure eyemovement)
Earphones and Sound card
Sennheiser IE 40Pro(IE40PROBLACK) and Logicool Astro Mixamp Pro(substitutes headset)
TR(MAPTR-002)
Adopted gameIn this study, we used the Rainbow Six Siege, a 5-on-5 competitive FPS game, in which the participants, whowere divided into attacker and defender teams, fought to wipe out enemy positions or defuse bombs inapproximately 3 minutes per round. The number of rounds was set to two. In general, the balance of a gameis determined by the MMR which reflects the real abilities of the opposing team and its friendly team in an
informal match. In the present study, the experimental conditions were standardized by playing 5 versus 5
custom matches with the same subjects to prevent the replacement of enemy and friendly team members
for each participant. In addition, to remove the influence of character differences, we made participants use
the same operator, "Zofia" for attack and "Valkyrie" for defence. Furthermore, the map used in the experiment
was the "Consulate”. Voice chats were allowed only for listening to the voice, but vocalizations were notallowed. Participants were asked to play the game using settings, such as mouse sensitivity and key
mapping, that they normally used when playing the game.
Methods for measuring and assessing visual search strategies
Eye movement data were used during gameplay to measure and assess the visual search strategy.
Participants sat 100 cm away from the gameplay monitor and performed a 9-point calibration. A 60 cm
distance was kept between the eye movement measurement device (GP3 HD) and the participants’ eyes.
The data were analysed only for the time that the participants were “alive” during the gameplay. This was
because if a player controlled by the operator was defeated, the monitor would be viewed from anotherplayer's or surveillance camera's perspective that existed in the map, until the victory or defeat wasdetermined.
This study conducted an area of interest (AOI) analysis to calculate the frequency of gazes in each area. The
settings for AOI analysis were specified for each of the 5 fixed locations displayed on the game screen (Figure
2).
The gaze fixation time per gaze was calculated by dividing the total gaze time by the total number of gazes,
based on the eye movement data during gameplay. In addition, the number of times the player looked at thesurveillance cameras on the map (mainly used by the defenders) and the mobile drone cameras (used by
the attackers), as well as the total number of times the players peeked at the gun’s muzzle (the so-called Aim
Down Sights) were calculated as FPS game-specific indicators.
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Figure 2. An analysis of the area of interest settings.
Methods for measuring and assessing psychological states
EEGs are electrical potentials generated on the scalp as a result of brain activity and are a part of biologicalinformation that continues to occur in humans (Miyata et al., 2004). The disadvantages of EEG include
attenuation of the output signal and low spatial resolution. However, compared to functional magnetic
resonance imaging, magnetoencephalography, and positron emission tomography; EEG does not requirelarge equipment and is inexpensive. Thus, it has the advantage of being easy to use for research. The human
psychological (mental) state can also be reflected by recording electrical signals obtained from electrodes
placed on the scalp. For example, Hagiwara, Akiyama, et al. (2020) measured the EEG of FP1 (10–20
system) located in the left frontal lobe of participants performing the rhythm game and noted an increase in
selective attention levels. In addition, Hagiwara et al. (2019) found that the concentration values during
gameplay were significantly higher compared to the baseline, when the soccer game was performed for 10
minutes.
Therefore, in the present study, we used the same EEG measures as in the studies by Hagiwara et al. (2019);
Hagiwara, Akiyama, et al. (2020). Specifically, the participants wore an EEG device on their heads, andelectrodes were attached to their ears and forehead to obtain data on the intensity of alpha (8–13 Hz) andbeta (13–40 Hz) waves, at 1-second intervals. The EEG data obtained in this way were recorded on a tablet
device using Bluetooth communication, and analysed by the Sports KANSEI, which was finally output as asensitivity value of 0–100. It is possible to evaluate the three psychological states of concentration, relaxation,and zone (SMR wave) according to the size of the sensitivity values calculated by this algorithm (higher
values indicate a higher degree of superiority).

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Procedure
The experimental procedure is shown in Figure 3. First, participants were required to wear an EEG device
and underwent a 2-minute EEG measurement in an open-eyed state to determine their baseline level ofarousal, prior to gameplay. They then performed a calibration for setting up the eye movement measurement
device. After synchronizing the EEG and the eye movement device, gameplay was performed. These
processes were repeated nine times in one cycle to measure the participants’ data.

Figure 3. Procedure for the experiment.
Analysis
During gameplay, an unpaired t-test was conducted on the obtained eye movement data to characterize thevisual search strategies of FPS players having different skill levels. In addition, a paired t-test was employed
to compare the means of concentration, relaxation, and zone, during FPS gameplay with a 2-minute baseline
measurement. Subsequently, an unpaired t-test was used to compare the means of concentration, relaxation,and zone during gameplay with different skill levels. IBM SPSS Statistics 27.0 software was used for all
statistical analyses. A standardized measure was calculated of the effect size, which does not vary withsample size. The Cohen's d index (d) was used as the effect size. Gazepoint Analysis (Gazepoint, Canada)
was also used to analyse eye movement data.
RESULTSAt the outset, we used eye movement data during FPS gameplay to examine the differences in visual searchstrategies between different skill levels. The AOI analysis revealed a significant difference in the number oftimes participants gazed at the “Our team” area during FPS games (t(7) = 2.97, p < .05, d = 1.88). The skilledgroup (M = 3.75, SD = 2.87) gazed at the “Our team” area significantly more often than the semi-skilled group(M = 0, SD = 0) (Table 2). The number of counts of camera checks during FPS gameplay were alsosignificantly higher in the skilled (M = 7.00, SD = 2.94) than the semi-skilled group (M = 1.40, SD = 1.14)
S1128| 2020 | Proc4 | VOLUME 15© 2020 University of AlicanteFurukado, et al. / A study on eye tracking and brainwaves during e-sports gameplay JOURNAL OF HUMAN SPORT & EXERCISE(t(3.72) = 3.60, p < .05, d = 2.11) (Table 2). As shown in Table 2, there were no differences in visual searchstrategies between the two skill levels for the other items.
Table 2. Comparison of eye movement data during FPS gameplay.
Skilled(n=4)
Semi-Skilled(n=5)
M
SD
M
SD
HP
2.00
1.83
1.60
2.51
Time left
2.75
2.06
0.60
1.34
Lower part of the screen
16.00
11.22
18.00
22.99
Our team
3.75
2.87
0.00
0.00
Enemy team
1.25
0.96
0.00
0.00
Count of camera checks
7.00
2.94
1.40
1.14
Count of aim down sights
26.25
4.03
19.40
10.33
Gazing time per session
0.30
0.07
0.27
0.04

t value

95% CI

0.27
1.90
-0.16
2.97 *
2.61
3.60 *
1.24
0.93[-3.16, 3.96][-0.53, 4.83][-31.93, 27.93][0.77, 6.73][-0.27, 2.77][1.15, 10.06][-6.23, 19.93][-0.05, 0.12]

Note: Signification p < .05.

Subsequently, when the averages of attention, relaxation, and zone extracted from the EEGs during FPS
gameplay were compared with game skill levels, they tended to be significantly different only in terms ofrelaxation (t(5.18) = 2.64, p < .05, d = 5.75).
The level of relaxation during FPS gameplay tended to be significantly higher in the skilled (M = 3.75, SD =2.87) than the semi-skilled group (M = 3.75, SD = 2.87) (Figure 4).

Figure 4. Comparison of the degree of relaxation during FPS gameplay between different skill levels (error
bar: standard deviation) for each site.
Furthermore, a corresponding t-test that compared EEG data during the 2-minute baseline measurement
and FPS gameplay found a significantly different trend only in the zone for the skilled group (t(3) = - 4.05, p< .05, d = 3.74). The degree of zone during FPS gameplay in the skilled group (M = 56.23, SD = 6.62) tended
to be significantly higher than that at baseline (M = 48.66, SD = 6.44) (Figure 5).

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Figure 5 Comparing the average degree of zone at baseline and while playing FPS games (error bar:standard deviation) for each site.
DISCUSSION
Differences in visual search strategies during FPS gameplay
Although previous studies had rarely discussed specific visual search strategies during FPS gameplay, ourAOI analysis showed that skilled players gazed more often at the “Our team” area fixed in the upper left
corner of the monitor (refer to Figure 2), implying that the skilled group collected information about the life,
death, and hit points of friendly team members about once every 60 seconds during gameplay. It is also likelythat the reason why skilled groups with high game skills checked the camera more often, was to gain anadvantage in the battlefield by gathering information. For example, in soccer, elite athletes with high passing
accuracy have been reported to make many body and head movements (scanning) that actively andtemporarily turns their faces away from the ball for gathering as much information as possible before receiving
the ball (Jordet et al., 2013). Conversely, in FPS games, players can gather information by scanning thescreen with their eyes without moving their heads and bodies, as in football. Therefore, it is likely that gameplayers with higher skill levels, frequently checked the in-game camera footage to understand the enemy's
position and the key points of attack and defence. It is hoped that future research will contribute to thedevelopment of research proficiency by investigating the characteristics of visual search strategies in specificsituations, such as while looking at the camera, listening to enemy footsteps, etc., during FPS gameplay.
Differences in EEG during FPS gameplay
The EEG data measured in the FP1 region were converted into sensitivity values using the Sports KANSEI
algorithm to compare psychological states during FPS gameplay with different levels of gaming skills.
Comparisons of the means of concentration, relaxation, and zone during FPS gameplay at different gameskill levels showed that only mean relaxation was significantly higher in the skilled than in the semi-skilledgroup. Previous studies have already shown that heart rate increases during e-sports play (Valladao et al.,2019; Sousa et al., 2020). FPS games, in particular, are a game genre where the peak heart rate can fluctuate
greatly, but it is possible that skilled players may have been able to relax while playing, thereby increasing
their effective field of view, and consequently improving their performance.

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In addition, the skilled group’s mean sensitivity values for the zone were higher during FPS gameplay thanat baseline. The zone state is a type of beta wave called the SMR wave, which is the most balanced between
relaxation and concentration. In addition to SMR waves (12–15 Hz), there are high alpha waves (10–12 Hz)
and low beta waves (15–18 Hz) in the brain waves of the mid-awake zone. If the SMR wave is the apex of
the balance between relaxation and concentration; then, the more relaxed and less aroused the person is,
the higher the alpha wave, whereas the more focused and more aroused the person is, the lower the beta
wave. As mentioned earlier, on an average, the skilled group was more relaxed during gameplay than thesemi-skilled group, suggesting that the optimal EEG band for FPS game performance can exist when thehigh alpha and SMR waves are in a predominant state. However, since the optimal EEG band may varydepending on the game genre, more research is needed to determine the optimal band. In addition,
longitudinal experiments on the same subjects will help to improve the accuracy of the sensitivity values.
In this study, we conducted an experiment with 9 FPS game players who belonged to a vocational schooland engaged in daily gameplay. However, to generalize our research findings, we need to expand the sample
size and collect data from a wide range of game skill levels. First, we shall collect gold standard data from esports players who are professional athletes to allow for more detailed comparisons. There is also a need to
discuss the control group’s settings, as those who have never played FPS games before will need to listen
to the rules first, and then practice and check the keystrokes and other aspects of the game, before they canplay properly. For this reason, it is necessary to establish a common set of rules for setting the appropriate
skill level for the control group.
CONCLUSION
In conclusion, this study aimed to examine the visual search strategies and EEG activities of FPS players
during gameplay in e-sports. A comparison of visual search strategies during FPS gameplay based on gameskill levels indicated that skilled players collected more information for situational judgment than semi-skilledplayers, as they gazed at fixed areas of the game screen and camera images more often than semi-skilledplayers. An examination of the EEG activity revealed that the skilled participants played the game in a morerelaxed state than the semi-skilled ones. Furthermore, the skilled players’ mean for the zone during gameplay
was higher than at baseline, indicating a better balance between relaxation and concentration during FPS
gameplay under psychological pressure. Future research is expected to examine the characteristics ofvarious states during FPS gameplay.
ACKNOWLEDGEMENTS
We would like to thank Editage (www.editage.com) for English language editing.
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