International Journal of Computer Games Technology
Volume 2020 (2020), Article ID 2450651, 19 pages
https://doi.org/10.1155/2020/2450651

Research Article

Method for Selecting an Engagement Index for a Specific Type of Game Using Cognitive Neuroscience

Konrad Biercewicz,¹ Mariusz Borawski,¹ and Jarosław Duda²

¹Department of Multimedia Systems, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology Szczecin, Żołnierska 49, 71-210 Szczecin, Poland
²Maritime University of Szczecin, Dean of Marine Engineering Faculty, Institute of Marine Electrical Engineering and Vessel Automation Department of Automation and Robotics, Wały Chrobrego 1-2, 70-500 Szczecin, Poland

Correspondence should be addressed to Konrad Biercewicz; kbiercewicz@poczta.onet.pl

Received 8 October 2019; Revised 22 July 2020; Accepted 31 July 2020; Published 18 August 2020

Academic Editor: Michael J. Katchabaw

Copyright © 2020 Konrad Biercewicz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The popularity of video games means that methods are needed to assess their content in terms of player satisfaction right from the production stage. For this purpose, the indicators used in EEG studies can be used. This publication presents a method that has been developed to determine whether a person likes an arcade game. To this end, six different indicators to measure consumer involvement in a video game using the EEG were compared, among others. The study was conducted using several different games created in Unity based on the observation () of the respondents. EEG has been used to select the most suitable indices studied.

1. Introduction

Computer games can evoke many emotions in a person who likes a given type of gameplay such as excitement, competition, fun, or relaxation. Game developers are most interested in ensuring that the player reaches the level of engagement in the game to such extent that he/she is performing the current activity for as long as possible. In other words, player involvement is one of the dimensions of gaming experience and can be associated with many concepts such as [1, 2] flow [3, 4], game flow [5], presence [6, 7], immersion [8–10], pleasure [11], motivation [12–14], enjoyment [15], arousal [16], and fun [17]. Therefore, in order to unleash such a state in the recipient, it is necessary to maintain the player’s involvement at a certain level, e.g., by introducing unexpected twists and turns of action, which will encourage him to further explore further areas of the game. Firstly, to assess whether a participant is not discouraged by the game, it is necessary to introduce research, among other things, into the participant’s involvement in the game. In addition, the growing community of video game players is creating a demand among game developers for a better approach to indicating when and at what point the player’s interest is changing. However, before you start to study what elements in the game should be improved, you should examine whether a person likes a particular type of game.

On the face of it, it may seem that evaluating a player’s involvement in a video game is quite an easy task. This is not quite so, as evidenced by the methods provided by the manufacturers in the source code, which, for example, count how many blows the player took and how many times he played a particular level. It is difficult to check in real time, for example, what the player is looking at and what emotions accompany them. An example of such a module is the use of Unity Analytics if the game is created in the Unity engine. This tool helps to understand why people are playing the game or, on the contrary, why they are giving up the game. By understanding the people involved in the game and how they play, you can make improvements to the game.

Using Unity Analytics, you can monitor your game in the following areas [18]:

(i)Onboarding. Do players use mechanisms such as tutorials or starter levels?(ii)Progression. Do players pass the game levels?(iii)Economics. Does the game economy work as expected?(iv)Design Validation. Does the game work properly?(v)Application Validation. Are all application areas used as expected? Are there any items that the players ignore or do not notice?(vi)Earnings. Are earning strategies optimal?

The use of the Analytics Dashboard helps analyze player behavior. The tool allows you to create paths that show how players pass through a linear step sequence. For example, you can create a tutorial sequence that shows the percentage of users that have passed the tutorial steps. Pathways are useful for identifying places in applications where we lose a player.

The sample path shown in Figure 1 shows the player’s progress in a hypothetical game. Each step on the path is the completion of the game level. There is always the possibility of a decrease from level to level; too much decrease after a given level may indicate a problem at this level. The path will not give the reason—it may be due to a problem with the game, error, boredom, or level being too difficult—but it will indicate the area to be explored.

Figure 1: Level progression funnel [18].

The sample path shown in Figure 1 refers to the scientific literature; several studies have used questionnaires which are not entirely a good form of investigating a player’s experience [19]. The problems arise from the formulation and context of these forms [20].

The most common methods used to test a player’s involvement in digital games are the following test methods [21]:

(i)Questionnaire [6]. By asking the appropriate questions in the survey, we can determine the degree of involvement in the individual elements of the game.(ii)Involvement Consisting of Focusing on Attention Using Eye Tracking (ABE) [22–26]. The time during which the respondent looks at the elements on the monitor screen is measured. Concerning the time when the person was not looking at the monitor, it is possible to deduce how much attention the person tested gave to the game.(iii)Electrodermal Activity (EDA). This, also known as skin galvanic reaction (GSR), allows determining the emotions of the tested person based on the measurement of skin conductivity [16, 27, 28].(iv)Examination of Facial Expression. The facial expressions were examined by observation [29].(v)Mouse Clicks and Mouse Movement [30]. Measurements of the number and location of clicks and mouse movements allow determining the level of player involvement during the game.

The above methods are very limited. In a self-reported survey, the researcher relies on the observations of the respondent. The test person may have difficulty in remembering his or her feelings during the whole game. It is difficult to determine the exact time frame within which the growth of interest in the game begins and ends on the basis of this study. Attention-Based Engagement (ABE) depends not only on the player’s engagement but also on the type of game and the situation in the game. During a fight, the player’s focus on the game will be very high, because he/she has to react quickly to the opponent’s actions, while in a game where the player is just wandering around the city, their focus can be lower.

Galvanic skin response (GSR) allows you to define emotions in the first place. However, it is not always that a player’s involvement can be emotional. Certain elements of the game may not generate emotions until some success or failure is achieved.

Mouse clicks and mouse movement are strongly dependent on the scenario of the game itself. They can be useful if you are able to refer to other players. You can tell from them which player is more involved and which one is less involved. For example, during a fight, the mouse movements will depend on the weapon chosen by the player and the way the opponent fights. They may, therefore, be incomparable between opponents.

It is necessary to look for such methods of engagement research that will allow determining the level of engagement at any time in the game, while not being dependent on other factors. An example of such methods is a method of cognitive neuroscience. They are becoming more and more useful because they allow us to get to know the current state of the brain. This task is facilitated by the indices calculated on the basis of the recorded signals. In the literature on the subject, numerous indices of engagement can be found, which will be presented later in this chapter. They allow us to know the level of human involvement in a given activity in a given moment.

New developments in Brain-Computer Interfaces (BCI) using wireless electroencephalographic (EEG) systems provide recordings and access to neuronal activity, enabling the computer to retrieve and analyze information from brain waves. It has been demonstrated that EEG has the ability to determine the involvement of the user [31–34]. The frequency bands are determined from the EEG signal using the spectral method. More details can be found in Section 2.5.

Using the EEG device, we can determine the preferences of the player, as well as which moment of the game is not very interesting, and we can improve it to make the player fully active in the game. New EEG devices are increasingly being used outside of medicine and are finding more and more new applications.

Using the EEG to measure the commitment of tasks is not a new concept. Pope et al. [35] built a system to control the level of automation of tasks based on whether the operator had increased or decreased his involvement. Freeman et al. [36] extended this system by evaluating the performance of each task with the use of absolute values of commitment. Berka [37] has invented a more accurate and effective method for people to interact with technology, with the ability to develop more productive work environments that increase motivation and productivity. The results suggest that the commitment measured using the EEG reflects information gathering, visual processing, and attention allocation. Smith and Gevins [38] used a flight simulator to study the reactions of the human brain to low-, medium-, and high-difficulty exercises. Studies have shown increased activity of the frontal lobe waves together with decreased activity of parietal lobe alpha waves during demanding tasks. In turn, Yamada [39] measured the activity of theta waves along with blinking of the eye and discovered that children playing video games had higher activity of theta waves during more frequent blinking. These results suggest that interesting tasks cause higher activity of theta waves, while the task inhibits the activity of blinking eyes. Kamzanova et al. [31] compared the sensitivity of a series of EEG engagement indices by examining time-pressured individuals performing tasks of varying degrees of stress to determine which one was most effective. McMahan et al. [32] investigated in the Super Meat Boy game whether there is a connection between engagement and arousal in events of death and general entertainment. The results of their research suggest that by combining engagement data with arousal data, we can establish thresholds indicating when a player has left the flow state. On the other hand, Ewing et al. [33] investigated the sensitivity of EEG power in the (front) theta and (parietal) alpha bands to changing levels of demand for play. Besides, they also conducted a study that assessed the adaptive performance of Tetris in terms of system behavior and user experience. Vourvopoulos et al.’s [34] research focuses on the impact of gaming experience on modulating brain activity, as an attempt to systematically identify elements that contribute to high BCI control and that can be used in the design of a neurogame.

The above author’s research studies [32–34] examine player engagement but focus on topics related to dependencies or BCI. There is no prior research that will show what approach to take in order to determine whether a person likes a particular type of gameplay. In such a case, the relationship between engagement and arousal may prove to be more accurate, because we will be able to present the results as they were presented in people who like or dislike the given type of game.

This article presents the results of the research, which was aimed at developing a method of researching the involvement of a player during an arcade game. Therefore, we can determine which index should be used to determine whether a participant likes this type of gameplay.

2. Materials and Methods

The test procedure is shown in Figure 2. The first step of the test procedure is to formulate the problem. It is presented in Introduction. The second stage of the research procedure is the preparation of a questionnaire. The survey is aimed at learning about the preferences of players—their expectations as to the content of the game, their favorite type of gameplay, and the activities performed in the game. The results of the survey will be the basis for determining the player’s profile.

Figure 2: Proposal for a test procedure.

It took several games to find out what kind of game the player was involved in. The games were created in the Unity engine in the C# language. They were made in such a way that it is possible to record in-game events in order to synchronize them with the registered EEG signals. EEG signals were recorded in a group of 31 people. First, a pilot study was carried out on several persons to verify the correctness of registration.. After the pilot study and the correction of all errors, an appropriate study was carried out.

The recorded signals were used to calculate the EEG indices. Based on the received involvement of the respondent and the respondent’s answer, a comparison was made between the responses and the engagement indices. Based on this selection, it was determined which index should be used for a given type of game.

2.1. Questionnaire

EEG data were collected from 31 healthy subjects (, ), and the average age was 23. The subjects were informed about the course of the study. They then signed consent to participate in the study and seated on a comfortable chair with access to a keyboard and mouse. The next step was to put on a cap and connect the electrodes to the skin of the participant’s head and to a device that recorded data from the participant’s brain. After performing the above activities, the study was started. Before each game, there was information about what the game was going to be about, what goal to achieve, and how to move around in it. Immediately after the end of the survey, each participant was interviewed about their experience with computer games and which type of game is the most popular. During the game, the players were asked to organize the games in terms of their involvement.

The game of each participant was saved in the resolution of using the programmed registration in the game. Each shot on the screen generated a timestamp for EEG data to determine the position of the beginning and end of each section. The screenshots were saved for later reference during the data analysis phase. In addition to EEG, an eye tracker (“The Eye Tribe”) was used in the study to track what was particularly important to the respondent.

2.2. Description of the Games

The games were downloaded from the Unity Asset Store and adapted to the needs of this research in the Unity engine. Before the start of the game, there was a short instruction on how to move and the purpose of the game.

The following games have been created:

(i)2D Shooter [40]. The game was about achieving the best possible result by killing monsters (Figure 3).(ii)Puzzle [41]. The game consisted of arranging a large picture from small fragments with characteristic shapes (Figure 4).(iii)Hexlogic [42]. The task was to indicate the excess number of figures (Figure 5).(iv)3D Shooter [43]. Just like in the 2D shooter game, the game consisted of achieving the best possible result by killing creatures (Figure 6).(v)Tower Defense [44]. The task was to stop further waves of enemies by building defense towers (Figure 7).(vi)Flying Mushroom [45]. The task was to fly the mushroom to the indicated location (Figure 8).(vii)Racing Game. The game consisted of driving as many meters as possible avoiding obstacles (Figure 9).(viii)Ball Control [46]. The task was to avoid obstacles and achieve the highest possible result (Figure 10).

Figure 3: The screenshot shows a 2D shooter computer game platform.

Figure 4: Screenshot showing a computer game of puzzling jigsaw puzzles.

Figure 5: Screenshot of a hexlogic computer game.

Figure 6: Screenshot of a 3D shooter computer game.

Figure 7: Screenshot of a tower defense computer game.

Figure 8: Screenshot of a flying mushroom computer game.

Figure 9: Screenshot of a computer game of time racing.

Figure 10: Screenshot showing a computer game of ball control with advertisement #Zaparkuj telefon i jedź (“#Park the phone and go”).

Each game lasted a minute. During each game, the start and end time of the game was recorded and then saved to an Excel file.

2.3. Electrodes Used in the Examination

The cap with 21 electrodes placed in AF3, AF4, F3, F4, F7, F8, FC5, FC6, P7, P8, T7, T8, O1, O2, P3, C3, Pz, Fz, Cz, FPz, and P4 was used (see Figure 11). The channels have been distributed according to the 10-10 system, the international EEG electrode distribution system [47]. The electrodes required a dampened socket to improve conductivity. The sampling frequency was 500 Hz.

Figure 11: Sensor location on a headset.

2.4. Game Survey

The participants answered a series of questions evaluating previous experiences with video games and other personal characteristics. The participants were asked to submit their favorite type of game: arcade (27 people) and logical (4 people). The participants were also asked if they would qualify as “recreational players”; the answer was positive.

The last questions concerned the game itself and, more specifically, which elements of the game should be improved according to them, as well as in which situations they believe that their involvement grew and decreased.

2.5. Analysis of Data

All data were analyzed using MATLAB R2019a and Statistica. Events such as blinking of the eyes, head movements, or body movements may cause undesired EEG registration data. Most EEG analyses require the removal of such events in order to identify medical problems. However, this is not a problem to analyze the gameplay. Such events are common in everyday play [48].

The EEG measured on the scalp corresponds to a recording at frequencies of 0.5 to 30 Hz. Four basic bands are recognized in this range [49]:

(i)Delta (0.5-4 Hz). The brain waves of the delta are generated in the deepest meditation and sleep. Delta waves suspend external consciousness and are a source of empathy. In this state, treatment is stimulated and regeneration, which is why deep restorative sleep is so important for the healing process.(ii)Theta (4-8 Hz). Theta waves occur most frequently in sleep but are also dominant in deep meditation. Theta waves are noticed during learning or remembering.(iii)Alpha (8-12 Hz). Alpha activity is best seen in the back regions of the brain and is typical for relaxation. It occurs when closing the eyes.(iv)Beta (12-30 Hz). Beta activity can be divided into low-activity waves (12-15 Hz), medium-activity waves (15-20 Hz), and high-activity waves (18-30 Hz). The average range of beta activity is associated with increased energy, anxiety, performance, and concentration. The most visible is in the leading regions.

The EEG spectral signal was analyzed using a Fast Fourier Transform (FFT) and an overlapping 3-second time frame with a 1-second jump at the relevant alpha, beta, and theta frequencies listed in Table 1.

Table 1: Representation of frequency bands (alpha, beta, and theta).

Bandwidth
Frequency (Hz)

Theta
4-7.9

Alpha2
7-13

Alpha3
8-13

Alpha4
8-10.9

Alpha5
11-13.9

Beta3
13-25

Beta4
13-22

Beta5
14-19.9

Beta6
20-29.9

Measurement of the level of engagement is one of the elements determining the player’s experience while playing a computer game. In particular, it can be used to determine the player’s preferences for the same type of game.

For this purpose, the exposure indices used to calculate the exposure level are those presented in Table 2.

(i)Index 1 [32]. has been calculated for each participant using the following electrodes: AF3, AF4, F3, F4, F7, F8, FC5, FC6, P7, P8, T7, T8, O1, and O2.(ii)Index 2 [32]. was calculated using the average registration value from electrodes placed on the frontal lobe of theta (F3, F4, FC5, and FC6) and divided by the average registration value from electrodes placed on the parietal lobe of alpha (P7, P8).(iii)Index 3 [32]. Theta was calculated using the average registration value from the electrodes placed on the frontal lobe of theta: AF3, AF4, F3, F4, F7, F8, FC5, and FC6.(iv)Indices 4 and 5 [31]. was calculated using the average registration value of the following electrodes: F3, F4, F7, F8, Cz, P3, Pz, and P4.(v)Index 6 [50]. was calculated using the average registration value from the following electrodes: P3, C3, Pz, Fz, Cz, and FPz.

Table 2: Description of the indices used in the test.

Index number
Formula
Counting method

Index 1

Average registration value of all electrodes on the head

Index 2

Average registration value from electrodes placed on the frontal lobe of theta and parietal lobe of alpha

Index 3
Theta
The average value of registration from electrodes placed on the frontal lobe of theta

Index 4

Average registration value from electrodes: F3, F4, F7, F8, Cz, P3, Pz, and P4

Index 5

Average registration value from electrodes: F3, F4, F7, F8, Cz, P3, Pz, and P4

Index 6

Average registration value from electrodes: P3, C3, Pz, Fz, Cz, and FPz

The variance is then analyzed to see if there is a material significant difference between the calculated exposure indices. For this purpose, the ANOVA statistical test was used. Before comparing population averages, the Shapiro-Wilk statistical test was used to check whether the examined features have a similar distribution to normal. The study of degradability normality can de facto be disregarded because, with sufficiently large samples (), a breach of the normality assumption should not cause serious problems; this means that parametric procedures can be used even if the data are not normally distributed [51]. The number of samples in the tests performed is greater than 100, so that the data distribution can be ignored [51]. If any differences were detected, the post hoc (postfact) HSD Tukey test was used. The post hoc tests are carried out as a further step in the analysis of variance since the analysis of variance itself only tells whether differences in the compared averages exist or not. We do not know which groups have these differences. A significant -factor only indicates the validity (or otherwise) of rejecting a zero hypothesis. If we reject it, we have to find out whether all the averages are different or just some.

After selecting which groups differ from each other, the average of the whole game was calculated. This allowed each index to select three games for the time when the average commitment was highest. After comparing the first positions with the opinion of the respondent, the index that corresponded most closely to the opinion of the respondent was selected. Where two different indices indicated the same game, the results of a post hoc analysis were considered. Where they did not appear in the table, the second or third position to compare with the opinion of the respondents was taken into account.

3. Results

Favorite games, which were mentioned by the respondents, are listed in Table 3. Indices with the greatest commitment were assigned to each researched person and to which game this occurred (Table 4). More detailed data can be found in Table 5.

Table 3: Respondent’s opinion on which game he/she thinks is the best.

Examining the number
1st place
2nd place
3rd place

1
Flying mushroom
3D shooter
Ball control

2
Puzzle
Tower defense
Other

3
Racing game
Other

4
Tower defense
3D shooter
2D shooter

5
3D shooter
Ball control
2D shooter

6
Puzzle
Hexlogic
Ball control

7
Flying mushroom
Other

8
2D shooter
Ball control
3D shooter

9
Racing game
Flying mushroom
Ball control

10
I was not interested in anything

11
Ball control
Tower defense
2D shooter

12
Flying mushroom
Other

13
Flying mushroom
Other

14
Flying mushroom
Other

15
Racing game
Puzzle
Hexlogic

16
3D shooter
Flying mushroom
Other

17
Flying mushroom
3D shooter
Racing game

18
Flying mushroom
Tower defense
Racing game

19
3D shooter
Puzzle
Other

20
3D shooter
Puzzle
Other

21
3D shooter
2D shooter
Puzzle

22
3D shooter
2D shooter
Puzzle

23
Racing game
Tower defense
Puzzle

24
Racing game
3D shooter
2D shooter

25
3D shooter
Flying mushroom
Racing game

26
3D shooter
Racing game
Puzzle

27
3D shooter
Hexlogic
Flying mushroom

28
Tower defense
3D shooter
Puzzle

29
Flying mushroom
2D shooter
Racing game

30
3D shooter
2D shooter
Other

31
Flying mushroom
Tower defense
Puzzle

Table 4: The table shows the distinguished games in which the involvement index was highest.

Examining number
Index 1
Index 2
Index 3
Index 4
Index 5
Index 6

Game
Game
Game
Game
Game
Game

1
prof1_0
prof4
prof1_1
prof5
prof5
prof1_1

2
prof1_1
prof3
prof4
prof3
prof1_1
prof4

3
prof1_1
prof5
prof4
prof6
prof4
prof2

4
prof1_1
prof1_1
prof1_1
prof1_1
prof4
prof1_1

5
prof1_1
prof2
prof5
prof1_2
prof1_1
prof6

6
prof2
prof1_1
prof1_1
prof4
prof5
prof2

7
prof6
prof4
prof4
prof6
prof6
prof4

8
prof1_1
prof1_0
prof3
prof1_1
prof1_1
prof5

9
prof4
prof5
prof5
prof6
prof4
prof4

10
prof1_1
prof1_1
prof5
prof1_1
prof4
prof1_2

11
prof5
prof4
prof4
prof5
prof2
prof3

12
prof1_2
prof4
prof4
prof1_2
prof1_1
prof1_2

13
prof1_0
prof4
prof1_1
prof5
prof5
prof1_1

14
prof3
prof4
prof4
prof3
prof1_2
prof1_1

15
prof4
prof5
prof2
prof1_2
prof4
prof4

16
prof1_1
prof2
prof3
prof1_1
prof4
prof4

17
prof5
prof5
prof5
prof1_2
prof6
prof1_2

18
prof1_2
prof1_0
prof4
prof1_2
prof1_1
prof5

19
prof1_1
prof2
prof6
prof6
prof4
prof6

20
prof4
prof5
prof5
prof4
prof1_1
prof1_2

21
prof4
prof2
prof2
prof5
prof1_1
prof5

22
prof1_1
prof2
prof4
prof6
prof6
prof6

23
prof1_2
prof5
prof4
prof3
prof1_2
prof1_2

24
prof6
prof5
prof5
prof5
prof1_2
prof1_1

25
prof1_2
prof1_1
prof4
prof5
prof5
prof5

26
prof1_1
prof2
prof2
prof6
prof6
prof5

27
prof1_2
prof1_1
prof1_1
prof5
prof1_2
prof1_2

28
prof1_1
prof1_1
prof1_1
prof5
prof5
prof1_1

29
prof1_2
prof4
prof4
prof5
prof1_2
prof1_1

30
prof1_1
prof1_1
prof3
prof1_1
prof1_1
prof5

31
prof6
prof4
prof4
prof1_2
prof5
prof1_2

prof1_0: 2D shooter; prof1_1: puzzle; prof1_2: hexlogic; prof2: 3D shooter; prof3: tower defense; prof4: flying mushroom; prof5: racing game; prof6: ball control.

Table 5: The table shows at which game the engagement index was highest.

Examining number

Index 1
Index 2
Index 3

Place
Mean
Std
Game
Mean
Std
Game
Mean
Std
Game

1
1
1.3939
0.1108
prof1_0
0.5771
0.1089
prof4
4.8238
0.3323
prof1_1

2
1.3915
0.1170
prof3
0.5446
0.1079
prof1_1
4.7891
0.3452
prof4

3
1.3910
0.1169
prof6
0.5367
0.1114
prof5
4.7650
0.3531
prof1_0

2
1
1.2741
0.0974
prof1_1
0.5506
0.1264
prof3
4.8002
0.4985
prof4

2
1.2736
0.1005
prof3
0.5497
0.1337
prof4
4.7719
0.5057
prof1_1

3
1.2736
0.0988
prof1_2
0.5491
0.1253
prof6
4.7669
0.5051
prof1_2

3
1
1.2984
0.1060
prof1_1
0.5875
0.1109
prof5
5.0370
0.5387
prof4

2
1.2984
0.1060
prof1_1
0.5810
0.1127
prof2
4.9798
0.5069
prof5

3
1.2983
0.1059
prof6
0.5793
0.1130
prof1_0
4.9580
0.4984
prof2

4
1
1.3223
0.1204
prof1_1
0.5577
0.0748
prof1_1
4.5023
0.3074
prof1_1

2
1.3086
0.0754
prof4
0.5550
0.0719
prof3
4.4917
0.2896
prof2

3
1.3058
0.1413
prof5
0.5542
0.0715
prof1_2
4.4904
0.2912
prof1_0

5
1
1.2725
0.1256
prof1_1
0.5711
0.0951
prof2
4.9084
0.5123
prof5

2
1.2569
0.1164
prof1_2
0.5692
0.0934
prof1_0
4.9022
0.5264
prof2

3
1.2541
0.1176
prof6
0.5678
0.0928
prof3
4.8939
0.5219
prof1_0

6
1
1.3830
0.1039
prof2
0.4907
0.1151
prof1_1
4.7602
0.4777
prof1_1

2
1.3777
0.1244
prof5
0.4882
0.1160
prof4
4.7254
0.4585
prof1_0

3
1.3730
0.1178
prof1_2
0.4847
0.1110
prof1_0
4.7203
0.4542
prof3

7
1
1.3725
0.1253
prof6
0.5299
0.1003
prof4
4.9861
0.5093
prof4

2
1.3716
0.1249
prof1_2
0.5071
0.1052
prof2
4.7367
0.4541
prof1_1

3
1.3712
0.1289
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0.5061
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8
1
1.3415
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prof1_1
0.5070
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prof1_0
4.8350
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prof3

2
1.3411
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prof1_2
0.5068
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prof3
4.8346
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prof2

3
1.3407
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prof6
0.5061
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prof2
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prof1_0

9
1
1.2701
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prof4
0.6678
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prof5
5.3371
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prof5

2
1.2669
0.1080
prof6
0.6661
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prof2
5.3148
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prof2

3
1.2663
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prof1_1
0.6661
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prof1_1
5.2975
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prof1_1

10
1
1.2778
0.0893
prof1_1
0.4622
0.0878
prof1_1
4.5582
0.3510
prof5

2
1.2674
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prof4
0.4557
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prof1_0
4.5532
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prof1_1

3
1.2671
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prof3
0.4551
0.0793
prof3
4.5172
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prof1_0

11
1
1.3664
0.1249
prof5
0.6665
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prof4
6.0602
0.7416
prof4

2
1.3628
0.1273
prof1_2
0.6448
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prof1_1
5.9635
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prof1_1

3
1.3613
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prof6
0.6270
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prof5
5.7526
0.8399
prof5

12
1
1.2783
0.1185
prof1_2
0.5877
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prof4
5.1959
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prof4

2
1.2777
0.1196
prof6
0.5664
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prof5
4.9896
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prof5

3
1.2763
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prof3
0.5614
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prof1_1
4.9659
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prof1_1

13
1
1.3939
0.0612
prof1_0
0.5771
0.0685
prof4
4.8238
0.2783
prof1_1

2
1.3915
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prof3
0.5446
0.0663
prof1_1
4.7891
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prof4

3
1.3910
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prof6
0.5367
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prof5
4.7650
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prof1_0

14
1
1.2853
0.1046
prof3
0.5631
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prof4
4.7660
0.4829
prof4

2
1.2849
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prof6
0.5596
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prof2
4.6753
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prof2

3
1.2835
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prof1_2
0.5593
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prof1_0
4.6739
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prof1_2

15
1
1.3021
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prof4
0.5203
0.0996
prof5
4.7767
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prof2

2
1.2380
0.1566
prof5
0.5167
0.0990
prof2
4.7546
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prof5

3
1.2258
0.1421
prof1_2
0.5107
0.0888
prof1_1
4.7519
0.5167
prof1_0

16
1
1.2423
0.1070
prof1_1
0.5739
0.0971
prof2
4.7264
0.4814
prof3

2
1.2423
0.1070
prof1_2
0.5737
0.0970
prof5
4.7236
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prof2

3
1.2422
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prof4
0.5695
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prof3
4.7213
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prof1_0

17
1
1.2430
0.0921
prof5
0.5288
0.0852
prof5
4.7155
0.4200
prof5

2
1.2429
0.0899
prof1_2
0.5179
0.0823
prof2
4.6865
0.4163
prof2

3
1.2424
0.0898
prof6
0.5125
0.0778
prof1_2
4.6715
0.4113
prof1_0

18
1
1.4281
0.1378
prof1_2
0.5532
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prof1_0
4.7321
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prof4

2
1.4270
0.1378
prof1_1
0.5527
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prof1_2
4.6583
0.4871
prof1_0

3
1.4270
0.1378
prof6
0.5526
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prof3
4.6561
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prof1_2

19
1
1.1832
0.0925
prof1_1
0.4885
0.0951
prof2
4.4860
0.3320
prof6

2
1.1832
0.0925
prof1_2
0.4875
0.0964
prof1_0
4.4857
0.3305
prof1_0

3
1.1826
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prof6
0.4874
0.0970
prof6
4.4847
0.3329
prof3

20
1
1.3625
0.1730
prof4
0.5214
0.1405
prof5
4.9589
0.7446
prof5

2
1.3621
0.1750
prof1_1
0.5137
0.1275
prof1_1
4.9212
0.7097
prof4

3
1.3284
0.1486
prof1_2
0.5135
0.1262
prof4
4.9134
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prof1_1

21
1
1.3028
0.0927
prof4
0.5638
0.1199
prof2
4.7399
0.5710
prof2

2
1.2918
0.0904
prof1_1
0.5604
0.1154
prof1_0
4.7392
0.5607
prof3

3
1.2911
0.0899
prof1_2
0.5599
0.1198
prof5
4.7372
0.5556
prof1_1

22
1
1.2583
0.1075
prof1_1
0.5481
0.1066
prof2
4.8554
0.4468
prof4

2
1.2583
0.1075
prof1_2
0.5466
0.1022
prof1_0
4.8347
0.5118
prof1_0

3
1.2580
0.1072
prof6
0.5454
0.1005
prof3
4.8323
0.5126
prof3

23
1
1.1481
0.1034
prof1_2
0.4769
0.0936
prof5
5.0241
0.4799
prof4

2
1.1468
0.1040
prof6
0.4752
0.0786
prof4
4.8410
0.5040
prof1_1

3
1.1461
0.1053
prof3
0.4640
0.0928
prof2
4.8364
0.5332
prof5

24
1
1.2417
0.0796
prof6
0.5838
0.1269
prof5
4.9556
0.5460
prof5

2
1.2415
0.0781
prof1_2
0.5607
0.1139
prof2
4.9414
0.5930
prof4

3
1.2400
0.0809
prof3
0.5590
0.1135
prof1_0
4.9362
0.5417
prof1_1

25
1
1.4218
0.1146
prof1_2
0.5910
0.0991
prof1_1
5.0299
0.4832
prof4

2
1.4198
0.1170
prof6
0.5884
0.0994
prof4
5.0196
0.4496
prof1_1

3
1.4187
0.1237
prof3
0.5757
0.0975
prof3
4.9003
0.4604
prof3

26
1
1.3732
0.1040
prof1_1
0.5623
0.1315
prof2
4.7500
0.4765
prof2

2
1.3732
0.1040
prof1_2
0.5576
0.1238
prof1_0
4.7404
0.4582
prof1_0

3
1.3717
0.1249
prof5
0.5553
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prof5
4.7322
0.4507
prof3

27
1
1.3557
0.1023
prof1_2
0.6389
0.0874
prof1_1
4.6823
0.4899
prof1_1

2
1.3546
0.1055
prof3
0.6196
0.0713
prof4
4.6518
0.4172
prof5

3
1.3546
0.1053
prof1_0
0.6166
0.0939
prof5
4.6159
0.4044
prof2

28
1
1.3015
0.1300
prof1_1
0.5502
0.1066
prof1_1
4.8910
0.5148
prof1_1

2
1.2848
0.1277
prof1_2
0.5491
0.1066
prof5
4.8438
0.5042
prof2

3
1.2827
0.1295
prof3
0.5444
0.1123
prof2
4.8410
0.4996
prof1_0

29
1
1.3764
0.1139
prof1_2
0.6071
0.0777
prof4
5.1324
0.5021
prof4

2
1.3759
0.1135
prof1_1
0.5606
0.0928
prof1_2
4.9583
0.5014
prof5

3
1.3741
0.1169
prof6
0.5602
0.0929
prof1_0
4.9030
0.4791
prof2

30
1
1.1402
0.0762
prof1_1
0.5803
0.0934
prof1_1
4.8808
0.4266
prof3

2
1.1293
0.0743
prof4
0.5650
0.1013
prof4
4.8769
0.4203
prof6

3
1.1116
0.0833
prof1_2
0.5637
0.0979
prof2
4.8741
0.4160
prof1_2

31
1
1.3262
0.1255
prof6
0.6185
0.1855
prof4
5.2500
0.7078
prof4

2
1.3256
0.1346
prof1_1
0.5956
0.1418
prof2
5.1171
0.5887
prof1_1

3
1.3253
0.1255
prof1_2
0.5938
0.1362
prof1_0
5.0460
0.6261
prof1_0

1
1
1.3939
0.1107
prof1_0
0.5771
0.0820
prof4
4.8238
0.0985
prof1_1

2
1.3915
0.1130
prof3
0.5446
0.0799
prof1_1
4.7891
0.0862
prof4

3
1.3910
0.1174
prof6
0.5367
0.0830
prof5
4.7650
0.0858
prof1_0

2
1
1.2741
0.1163
prof1_1
0.5506
0.0912
prof3
4.8002
0.1021
prof4

2
1.2736
0.1192
prof3
0.5497
0.0917
prof4
4.7719
0.0987
prof1_1

3
1.2736
0.1140
prof1_2
0.5491
0.0914
prof6
4.7669
0.0986
prof1_2

3
1
1.2984
0.1090
prof1_1
0.5875
0.0784
prof5
5.0370
0.0860
prof4

2
1.2984
0.1097
prof1_1
0.5810
0.0767
prof2
4.9798
0.0833
prof5

3
1.2983
0.1097
prof6
0.5793
0.0775
prof1_0
4.9580
0.0854
prof2

4
1
1.3223
0.1006
prof1_1
0.5577
0.0790
prof1_1
4.5023
0.1067
prof1_1

2
1.3086
0.1174
prof4
0.5550
0.0804
prof3
4.4917
0.1082
prof2

3
1.3058
0.1174
prof5
0.5542
0.0843
prof1_2
4.4904
0.1082
prof1_0

5
1
1.2725
0.1127
prof1_1
0.5711
0.0855
prof2
4.9084
0.0849
prof5

2
1.2569
0.1125
prof1_2
0.5692
0.0769
prof1_0
4.9022
0.0846
prof2

3
1.2541
0.1135
prof6
0.5678
0.0807
prof3
4.8939
0.0860
prof1_0

6
1
1.3830
0.1117
prof2
0.4907
0.0915
prof1_1
4.7602
0.1070
prof1_1

2
1.3777
0.1123
prof5
0.4882
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prof4
4.7254
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prof1_0

3
1.3730
0.1033
prof1_2
0.4847
0.0836
prof1_0
4.7203
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prof3

7
1
1.3725
0.1253
prof6
0.5299
0.0734
prof4
4.9861
0.1368
prof4

2
1.3716
0.1264
prof1_2
0.5071
0.0740
prof2
4.7367
0.1099
prof1_1

3
1.3712
0.1246
prof3
0.5061
0.0769
prof3
4.6896
0.0991
prof5

8
1
1.3415
0.1132
prof1_1
0.5070
0.0799
prof1_0
4.8350
0.1143
prof3

2
1.3411
0.1136
prof1_2
0.5068
0.0797
prof3
4.8346
0.1087
prof2

3
1.3407
0.1135
prof6
0.5061
0.0805
prof2
4.8306
0.1011
prof1_0

9
1
1.2701
0.0971
prof4
0.6678
0.0721
prof5
5.3371
0.1272
prof5

2
1.2669
0.0974
prof6
0.6661
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prof2
5.3148
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prof2

3
1.2663
0.0980
prof1_1
0.6661
0.0676
prof1_1
5.2975
0.1047
prof1_1

10
1
1.2778
0.0897
prof1_1
0.4622
0.0749
prof1_1
4.5582
0.0951
prof5

2
1.2674
0.0925
prof4
0.4557
0.0681
prof1_0
4.5532
0.0971
prof1_1

3
1.2671
0.0926
prof3
0.4551
0.0672
prof3
4.5172
0.0959
prof1_0

11
1
1.3664
0.0833
prof5
0.6665
0.0714
prof4
6.0602
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prof4

2
1.3628
0.0693
prof1_2
0.6448
0.0724
prof1_1
5.9635
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prof1_1

3
1.3613
0.0660
prof6
0.6270
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prof5
5.7526
0.0867
prof5

12
1
1.2783
0.1047
prof1_2
0.5877
0.0759
prof4
5.1959
0.0980
prof4

2
1.2777
0.1050
prof6
0.5664
0.0771
prof5
4.9896
0.0975
prof5

3
1.2763
0.1049
prof3
0.5614
0.0766
prof1_1
4.9659
0.0981
prof1_1

13
1
1.3939
0.1067
prof1_0
0.5771
0.0602
prof4
4.8238
0.0724
prof1_1

2
1.3915
0.0950
prof3
0.5446
0.0544
prof1_1
4.7891
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prof4

3
1.3910
0.0957
prof6
0.5367
0.0537
prof5
4.7650
0.0721
prof1_0

14
1
1.2853
0.0864
prof3
0.5631
0.0672
prof4
4.7660
0.0900
prof4

2
1.2849
0.0864
prof6
0.5596
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prof2
4.6753
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prof2

3
1.2835
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prof1_2
0.5593
0.0673
prof1_0
4.6739
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prof1_2

15
1
1.3021
0.1018
prof4
0.5203
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prof5
4.7767
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prof2

2
1.2380
0.1039
prof5
0.5167
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prof2
4.7546
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prof5

3
1.2258
0.1023
prof1_2
0.5107
0.0711
prof1_1
4.7519
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prof1_0

16
1
1.2423
0.0913
prof1_1
0.5739
0.0835
prof2
4.7264
0.1004
prof3

2
1.2423
0.0913
prof1_2
0.5737
0.0696
prof5
4.7236
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prof2

3
1.2422
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prof4
0.5695
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prof3
4.7213
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prof1_0

17
1
1.2430
0.0994
prof5
0.5288
0.0703
prof5
4.7155
0.0912
prof5

2
1.2429
0.0991
prof1_2
0.5179
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prof2
4.6865
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prof2

3
1.2424
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prof6
0.5125
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prof1_2
4.6715
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prof1_0

18
1
1.4281
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prof1_2
0.5532
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prof1_0
4.7321
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prof4

2
1.4270
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prof1_1
0.5527
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prof1_2
4.6583
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prof1_0

3
1.4270
0.1099
prof6
0.5526
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prof3
4.6561
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prof1_2

19
1
1.1832
0.1020
prof1_1
0.4885
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prof2
4.4860
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prof6

2
1.1832
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prof1_2
0.4875
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prof1_0
4.4857
0.1014
prof1_0

3
1.1826
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prof6
0.4874
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prof6
4.4847
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prof3

20
1
1.3625
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prof4
0.5214
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prof5
4.9589
0.0990
prof5

2
1.3621
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prof1_1
0.5137
0.1080
prof1_1
4.9212
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prof4

3
1.3284
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prof1_2
0.5135
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prof4
4.9134
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prof1_1

21
1
1.3028
0.1010
prof4
0.5638
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prof2
4.7399
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prof2

2
1.2918
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prof1_1
0.5604
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prof1_0
4.7392
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prof3

3
1.2911
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prof1_2
0.5599
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prof5
4.7372
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prof1_1

22
1
1.2583
0.0930
prof1_1
0.5481
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prof2
4.8554
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prof4

2
1.2583
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prof1_2
0.5466
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prof1_0
4.8347
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prof1_0

3
1.2580
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prof6
0.5454
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prof3
4.8323
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prof3

23
1
1.1481
0.0768
prof1_2
0.4769
0.0846
prof5
5.0241
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prof4

2
1.1468
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prof6
0.4752
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prof4
4.8410
0.1193
prof1_1

3
1.1461
0.0772
prof3
0.4640
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prof2
4.8364
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prof5

24
1
1.2417
0.1043
prof6
0.5838
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prof5
4.9556
0.0917
prof5

2
1.2415
0.1008
prof1_2
0.5607
0.0776
prof2
4.9414
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prof4

3
1.2400
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prof3
0.5590
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prof1_0
4.9362
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prof1_1

25
1
1.4218
0.1487
prof1_2
0.5910
0.0857
prof1_1
5.0299
0.1155
prof4

2
1.4198
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prof6
0.5884
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prof4
5.0196
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prof1_1

3
1.4187
0.1333
prof3
0.5757
0.0805
prof3
4.9003
0.1098
prof3

26
1
1.3732
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prof1_1
0.5623
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prof2
4.7500
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prof2

2
1.3732
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prof1_2
0.5576
0.0810
prof1_0
4.7404
0.1044
prof1_0

3
1.3717
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prof5
0.5553
0.0802
prof5
4.7322
0.1044
prof3

27
1
1.3557
0.0958
prof1_2
0.6389
0.0752
prof1_1
4.6823
0.0814
prof1_1

2
1.3546
0.0996
prof3
0.6196
0.0764
prof4
4.6518
0.0828
prof5

3
1.3546
0.0998
prof1_0
0.6166
0.0762
prof5
4.6159
0.0826
prof2

28
1
1.3015
0.0969
prof1_1
0.5502
0.0958
prof1_1
4.8910
0.0929
prof1_1

2
1.2848
0.0930
prof1_2
0.5491
0.0845
prof5
4.8438
0.0901
prof2

3
1.2827
0.0954
prof3
0.5444
0.0809
prof2
4.8410
0.0907
prof1_0

29
1
1.3764
0.1202
prof1_2
0.6071
0.0818
prof4
5.1324
0.0909
prof4

2
1.3759
0.1155
prof1_1
0.5606
0.0816
prof1_2
4.9583
0.0912
prof5

3
1.3741
0.1151
prof6
0.5602
0.0822
prof1_0
4.9030
0.0917
prof2

30
1
1.1402
0.0659
prof1_1
0.5803
0.0581
prof1_1
4.8808
0.0976
prof3

2
1.1293
0.0674
prof4
0.5650
0.0573
prof4
4.8769
0.0954
prof6

3
1.1116
0.0668
prof1_2
0.5637
0.0591
prof2
4.8741
0.0955
prof1_2

31
1
1.3262
0.1232
prof6
0.6185
0.1117
prof4
5.2500
0.0766
prof4

2
1.3256
0.1227
prof1_1
0.5956
0.1014
prof2
5.1171
0.0763
prof1_1

3
1.3253
0.1240
prof1_2
0.5938
0.1014
prof1_0
5.0460
0.0780
prof1_0

In Table 6, there are presented averages of indices between which there are no significant differences. The post hoc HSD Tukey test had to be applied to each patient because the ANOVA test (Table 7; for other subjects, the results were very similar) had to be rejected. This is because the average square of error (error) is the variance we expect and the average square (MS) is the variance in our dataset. We can see that the variance is much greater than the one we would expect, so the value of , the probability, is very low. In this case, as well as for other respondents, the value of indicates that the alternative hypothesis H1 should be assumed; i.e., there are differences between exposure indices. When two indices are in Table 6, choosing an engagement index is practically meaningless because there are no significant differences between their averages.

Table 6: The table shows the indices between which there are no significant differences in a particular game.

prof1_0
prof1_1
prof1_2
prof2_0
prof3_0
prof4_0
prof5_0
prof6_0

1
1.6
1.6, 4.5
1.6
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6
1.6

2
4.5
4.5
4.5
5.4
5.4
5.1, 5.4
4.5
4.5

3
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5

4
None
None
None
None
None
None
None
None

5
4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.5, 1.6, 4.5, 5.6
1.6, 4.5
4.5

6
None
None
None
None
4.5
1.6, 4.5
None
None

7
1.6
1.6
1.6
1.6
1.6
1.4, 1.5, 1.6, 4.5
1.6
1.6

8
1.6
1.6
1.6
1.6, 4.5
1.6, 4.5
1.6, 4.5
None
1.6

9
None
None
4.5
1.6, 4.5
1.5, 1.6, 4.5
1.5, 1.6, 4.5
1.6, 4.5
None

10
None
None
None
None
None
None
None
None

11
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5

12
None
1.6, 1.5
1.6
1.6, 1.5, 4.5
1.6, 1.5, 4.5
1.6, 1.5, 1.4, 4.5
1.6
1.6

13
None
None
None
None
None
None
None
None

14
None
None
None
None
None
1.6, 4.5
None
None

15
4.5
4.5
4.5
1.5, 4.5
1.5, 4.5
1.5, 1.6, 4.5
1.5, 4.5
4.5

16
None
None
None
1.5
1.5
1.5, 4.5
1.5
None

17
None
None
None
4.5
4.5
1.5
4.5
None

18
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.5, 1.5, 4.5
1.6, 4.5
1.6, 4.5

19
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5

20
4.5
1.6, 4.5
4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
4.5

21
4.5
4.5
4.5
4.5
4.5
1.6, 4.5
4.5
4.5

22
4.5
4.5
4.5
4.5
4.5
1.5, 4.5
4.5
4.5

23
1.5
1.5
1.5
1.5
1.5
1.5, 4.5
1.5
1.5

24
4.5
4.5
4.5
4.5
4.5
1.4, 1.5, 1.6, 4.5
4.5
4.5

25
1.6, 4.5
None
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5

26
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.6

27
None
1.6, 4.5
None
1.6, 4.5
1.6
1.6, 4.5
1.6, 4.5
None

28
None
4.5
None
4.5
4.5
1.5, 4.5
4.5
None

29
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5

30
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5

31
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 4.5
1.6, 1.5, 1.4, 4.5, 4.6, 5.6
1.6, 4.5
1.6, 4.5

Table 7: The results of the ANOVA test for the first person tested are presented.

(a)

prof1_0
prof1_1
prof1_2

MS

MS

MS

1
Result
595.07
20466.41
0.00
240.56
9215.93
0.00
547.85
18081.51
0.00

Error
0.03

0.03

0.03

(b)

prof2
prof3
prof4
prof5

MS

MS

MS

MS

Result
293.44
9887.26
0.00
280.22
9308.00
0.00
76.98
2915.87
0.00
375.33
13666.71
0.00

Error
0.03

0.03

0.03

0.03

(c)

prof6

MS

Result
556.23
18491.81
0.00

Error
0.03

Before the ANOVA test, normal distribution was tested (Figure 12). An example is shown because the results are similar to the other games and the people surveyed. was obtained in all cases (Table 8), which indicates the lack of normality of degradation. Nevertheless, parametric procedures can be used because the sample count is more than 100 [51]. In all tests, a materiality level of was established.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 12: (a–f) The results of the Shapiro-Wilk test of decomposition normality. Table 8 presents the results obtained for the specified game.

Table 8: The results of the study of normality of the Shapiro-Wilk test distribution for one tested game and one specific game are presented.

Zmn1 (Figure 12(a))
Zmn2 (Figure 12(b))
Zmn3 (Figure 12(c))
Zmn4 (Figure 12(d))
Zmn5 (Figure 12(e))
Zmn6 (Figure 12(f))

0.96215
0.90103
0.91376
0.96143
0.97850
0.95185

0.00137
0.00000
0.00000
0.00119
0.04216
0.00020

Zmn1: index 1; Zmn2: index 2; Zmn3: index 3; Zmn4: index 4; Zmn5: index 5; Zmn6: index 6.

Analysis has shown that index 2 best reflects a player’s commitment, which translates into a player’s preferences. The number of situations which differed from the opinion of the examined person was 9, of which 1 opinion should be rejected because the examined person (no. 10) was not interested in the game at all; for 5 opinions, the game differed, but the type of game itself was accepted, and the remaining 3 opinions did not agree either with the game which was liked or with the type of game.

4. Discussion

The main objective of the study was to develop a method that would allow determining player preferences based on engagement. It was helpful to evaluate the different indices of engagement when playing different types of computer games, which had a time of 1 minute. As a result, it was determined which index most closely reflects the correct engagement in the game. MATLAB software and survey data were used for the analysis. Among the most important results, we can mention the following:

(1)Table 9 shows the number of opinions for skill games that matched the index(2)Table 10 shows the persons in whom index 2 indicated another game, but it was still an arcade game(3)Among the respondents, there were people whose favorite game was a puzzle or tower defense (Table 11). Index 2, in this case, did not show arcade games. Therefore, we can presume that it is also able to detect cases where players do not like arcade games. We cannot state this clearly, because the attempt is too small for a bolder statement

Table 9: The number of opinions that matched the index on the most engaging arcade game.

Index 1
Index 2
Index 3
Index 4
Index 5
Index 6

Number of persons
0
18
9
1
0
0

Table 10: The number of the respondents, in which index 2 indicated an arcade game different from the respondent’s opinion in the 1st place.

Person no.
Index

7
prof4

11
prof4

17
prof5

18
prof1_0

20
prof5

prof1_0: 2D shooter; prof4: flying mushroom; prof5: racing game.

Table 11: Comparison of games that were indicated by the respondent and by index 2.

Person no.
Game
Index 2

2
Puzzle
Tower defense

4
Tower defense
Puzzle

6
Puzzle
Puzzle

28
Tower defense
Puzzle

In order to carry out a test that will allow us to determine whether a person likes arcade games, we should use the test procedure included in Figure 13. The first stage of the test procedure is the preparation of 3 different types of games. The second stage is to conduct the EEG test using the appropriate electrode configuration, i.e., F3, F4, FC5, FC6, P7, and P8, to calculate the exposure index. The test must last at least 1 minute. The last step is to analyze the data obtained. Calculate the average engagement during the whole duration of a particular game, and then sort the received values from the highest to the lowest. If the highest value is of an arcade game, it means that the player likes this type of game.

Figure 13: Test procedure to determine if a person likes to play arcade games.

In order to carry out a test that will allow us to determine whether a person likes arcade games, we should use the test procedure included in Figure 13. These conclusions should be understood in the context of certain restrictions. First of all, in the study, most people like arcade games; only 5 people said they liked a different type of game. As a result, we cannot fully determine whether there will be any conflict in the case of other types of games. At this moment, it was examined which index we should use for arcade games. The next step will be to prepare other types of games in order to establish the credibility of the selected index to ensure that the current scores have no anomaly due to the current number of people who do not like arcade games.

5. Conclusion

The results of research aimed at selecting the appropriate index of engagement using EEG to determine whether a person likes arcade games are presented. In this achievement, it was decided to define the profile of the player on the basis of the order from the most engaging to the least engaging game. Based on their opinions, the optimal indicator is index 2 (theta/alpha), because it best represents the opinion of the respondents.

It should be taken into account that these findings are based on a single type of game and that further research will be needed in order to extend the results of the methodological approach to assessing which type of game is of greatest interest not only by analyzing the player’s involvement but also by adding further indices from other categories such as concentration. Nevertheless, these results confirm the view that index 2 is a strong indicator of enjoyment for some type of games, and this shows real promise for future research with a larger more diverse set of participants and possibly a different set of games.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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