Fifth International Conference on Software Engineering Research, Management and Applications
Implementation of a 3-Dimensional Game for developing balanced Brainwave
Beom-Soo Shim, Sung-Wook Lee and Jeong-Hoon Shin
Dept. of Computer Information & Communication Engineering,
Catholic University of Daegu, Korea
1. Introduction
Abstract
In a ubiquitous condition, HCI(Human Computer
Interaction) is popularly used to meet users’ needs for
high quality service. In a condition with HCI, there
are a lot of interfaces that are used to satisfy the
needs of the users. As a result of these changes,
various types of effective human computer interface
methods have been developed. In many studies,
researchers have focused on using brain-wave
interface, BCI(Brain-wave computer interaction). BCI
is one of the research areas in HCI. Nowadays,
studies which are related to BCI are under way to
find effective methods of controlling and collecting
brain-wave. Most researches related to BCI are not
centralized and not systematic. BCI research uses
brain wave which is gathered from the subjects’
scalp. The brain wave is affected easily in an
experimental condition. Moreover, subjects do not
control their brain well when they attend the
research. These problems bring about ineffective
results research. In most research to related in HCI
and BCI, that is to say – pattern recognition, the most
important foundation of the research is to set a
standard about subjects’ mindset and attitude in
research. For this factor, in this paper, we propose
the Implementation of a 3-dimensional game for
developing balanced brain wave. Apart from other
kinds of pattern recognition, In BCI, it is difficult to
gather the specific brain wave researchers want.
Researchers do not know effective methods for
gathering the brain wave they want. Subjects also do
not know how to release the brain wave researchers
expect. To solve these kinds of problems, we propose
a novel game for developing balanced brain wave.
The game works by balancing the power of the brainwave values from the left cerebral hemisphere and
right cerebral hemisphere.
Subjects look at a
computer screen. On the screen, there is a game
which is made by our proposed algorithm. In real
time, subjects are able to observe gathered brain
wave data and therefore teach themselves to produce
the appropriate brain wave. To verify the
effectiveness of our proposed system, we analyzed the
difference of brain wave gathered form the left and
right cerebral hemisphere. On the basis of the
balanced left and right cerebral hemisphere analysis,
we propose the Implementation of a 3-Dimensional
game for stably developing balanced brain-wave.
0-7695-2867-8/07 $25.00 © 2007 IEEE
DOI 10.1109/SERA.2007.94
Until recently, controlling computers by human
thought was science fiction, but it’s rapidly becoming
science fact. The last decade has witnessed a rapidlygrowing body of research and technique development
involving detecting human brain responses and
putting these techniques to appropriate uses to help
people with debilitating diseases or who are disabled
in some way. The definition of BCI reflects the
principal reason for proving new augmentative
communication technology to patients with
neuromuscular impairments who are paralyzed or
have other severe movement deficits. Also, Brain
Computer Interaction (BCI) stems from a need for
alternative communication and control options for
individuals with severe disabilities( e.g amyotropic
lateral sclerosis), though its potential uses extend to
rehabilitation of neurological disorders, brain-state
monitoring and game. The most practical and widely
applicable BCI solutions are those based on noninvasive electroencephalogram (EEG) measurements
recorded from the scalp. In general, BCI offers the
possibility of communication for people who are
paralyzed to input letters on a device or give order to
the smallest embedded system. In addition, the BCI in
conjunction with exciting multimedia applications,
e.g., a dexterity discovery, could define a new level of
control possibilities for patients as well to control
information directly from their brains, as reflected in
EEG signals which are recorded non-invasively from
the scalp. But, there are some limits using BCI. Brain
wave has a very weak signal, affected a lot by the
surroundings, and many errors happen according to
the electrode pole stability when brain wave is
gathered, which makes it difficult for the researcher to
gather necessary brain-wave. It is common that a
researcher wants to gather brain-wave in a correct and
an efficient way while avoiding the irrelevant brainwave this can be time-consuming. It is difficult to
gather sable and meaningful brain wave data from
subjects. To solve this problem we propose a 3dimensional game that will help to developing
balanced brain wave. Without brain wave gathering
training, researchers can not gather the brain wave
data which they expect. Also, it is difficult for
subjects to concentrate on BCI research because of
the absence of standards for subjects who are in the
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research. Many studies proposed an efficient way to
get the brain wave researchers expected from
subjects. Some developed an intelligent algorithm for
gathering brain wave from the scalp and introduced
effective method expected to gather brain wave.
However, there are some limits in the ways. So, we
proposed the implementation of a 3-Dimensional
game for which can train subjects’ to use their brain
in a stable and controlled way.
Moreover, most of the research focuses on a
standard method to make subjects concentrate on the
experiment which works independently and also
depends on the platform that is used. The trend
research way of making subjects concentrate on the
experiment is inefficient and non systematic. There
are no standard for research environment.
Consequently, our proposed training game suggests a
standard way to control a subjects’ concentration.
In BCI research, it is important for subjects to
being gathered for accuracy and reliability of the
brain wave. Being gathered in BCI research, limits
encountered are the analysis of brain wave. Our
proposed training game could improve gathering
expected and useful brain wave.
The brain wave is affected easily in an external
condition. It is necessary for researchers to set the
standard concentration condition method which other
researchers agree to use. In order to agree with the
standard concentration condition, we propose the
implementation of 3-Dimensional game for
developing balanced brain wave. Presented training
game is the performance of the real-time BCI game
“Training Brain balance” when played by normal
subjects. The design of the Training Brain balance
game was split into two parts. The first part is the
analysis of left and right cerebral hemisphere’s power
value. The training game works through the analysis
of left and right cerebral hemisphere power value.
The second part is analysis of brain wave device
which makes it possible subject for to see the power
values easily in real time.
The game also contains visual and sound effects so
that subjects can concentrate on producing brain wave
easily. The goals of the proposed game focus on
gathering the stable and meaningful brain wave. In
chapter 2, we introduce some studies which are
related to the brain wave training device and some
game that use brain wave. In chapter 3, we will
explain our brain wave training device and 3D game
using brain wave we proposed. In chapter 4, we will
introduce the experiment result and its further study.
2.1 Brain Computer Interface based on the
Steady-State VEP for Immersive Gaming
Control. [1] Lalor E, Kelly S.P., Finucane C.,
Burke R., Reilly R.B., McDarby G.
2.1.1 Objective
In this paper the authors wish to address the
application of the SSVEP(Steady State Visual Evoked
Potential)-based BCI design to a real-time gaming
framework. It is proposed that performance on the
BCI game detailed below will be sensitive to
neurological
disorders
such
as
Attention
Deficit/Hyperactivity Disorder and thus may aid in its
rehabilitation. Presented here is the performance of
the real-time BCI game “MindBalance” when played
by normal subjects.
2.1.2 Configuration
MindBalance – the Game : The object of the
MindBalance game is to control the balance of an
animated character on a tightrope using only the
player’s EEG. A checkerboard is positioned on either
side of character. A screen-shot of the game can be
seen in Figure 1.
Figure 1. The character loses balance
during the game
Signal Processing and the C# Engine: In order to
carry out this study, a programming engine and
platform were required, capable of rendering detailed
3-D graphics while processing continuous EEG data
to control a sprite within the game at the same time.
This was accomplished using a combined graphics,
signal processing and network communications
engine implemented in C#.
2. Related Works
2.1.3 Experimental Results
HCI using brain wave is a popular topic of research.
Also, it is a popular topic for game using BCI. We
gathered some related issues about training and
feedback game using BCI. The following subsections
describe these related studies.
Results of the study indicate that successful binary
control using Steady State Visual Evoked Potentials is
possible in an uncontrolled environment and is
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resilient to any ill effects potentially incurred by a
rich detailed visual environment as in the
MindBalance game. The authors also propose to
extend the results of the preliminary trials of this
study to covert visual attention, in which subjects
direct attention to one of two bilateral stimuli without
eye movement.
2.2 Learning to Control Brain Rhythms :
Making A Brain-computer Interface Possible
[2] J. A. Pineda, D. S.Silverman,A.Vankov,and
J.Hestenes.
2.2.1 Objective
The authors hypothesize that a more efficient method
for training subjects to control mu rhythm, while still
meeting most of game conditions, involves visual
representation of the signal. Hence, the goal of their
study is to examine the mu rhythm and to determine
the effects on learning while using a complex visual
representation of the brain signal. In this case, the
signal was mapped to navigational movements (i.e.,
left or right) within a 3-dimensional (3-D) first person
shooter video game. Unlike the simple visual
feedback conditions used by previous studies, this
study involved training in a stimulus-rich, realistic,
and motivationally engaging environment.
Figure 2. A view of the 3-D first-person
shooter game. Subjects controlled the
movement of the scenery either to the left by
producing low mu or to the right by
producing high mu.
2.2.3 Experimental Results
The results of this study indicate that subjects learn to
control levels of mu very quickly, but especially when
this learning involves producing similar mu levels
(whether high or low) over each hemisphere. They are
able to maintain that level of control across ten
training sessions. In contrast, subjects show an almost
linear increase in learning across training sessions
when a difference over each hemisphere in mu levels
is necessary to achieve control.
2.2.2 Configuration
Subjects were placed in a soundproof chamber and
asked to look straight ahead at a computer monitor
that displayed a high-resolution 3-D first-person
shooter video game, as shown in Fig. 2. During the
free running period, subjects were asked to explore
the game by pressing the “s” key on the keyboard to
move forward and the “x” key to move back. Right
and left movements were controlled by “high” and
“low” mu respectively.
At the end of the free-running period, subjects began
either the left- or right- movement period. Subjects
were instructed not to touch the keyboard (thus
keeping the environment on the screen stationary), but
to attempt to rotate it left or right by producing “low”
or “high” mu respectively.
For a left movement, the subject was told to focus on
rotating the environment only to the left(Thus making
counter-clockwise circles). Similarly, for a right
movement, the subject was told to focus on rotating
the environment only to the right (thus making
clockwise circles). When the subject completed the
three periods of training, the session for that day
ended.
2.3 Quantifying Mental Relaxation with EEG
for use in Computer Games [3] T. A. Lin, L. R.
John.
2.3.1 Objective
The aim of this study was to investigate methods for
the implementation of EEG based measurement of
mental relaxation, and to demonstrate the potential of
the interface with a simple game, where a simulated
ball is controlled to move left or right based on
player’s mental relaxation level. The Bulk of this
research concerns investigating what frequency
components of the EEG signals or the combinations
of the frequency components best measures the user’s
mental relaxation level, as well as how viable it is to
be measured in real-time, so that it could be
implemented in a game.
2.3.2 Configuration
The EEG data gathered was large (+500 timewindows per test, per subject), thus parametric
methods were used for analyzing the data. A three-
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the Modular EEG device. The game application was
also designed to be easily extended if other
programmers wanted to create different neuro
feedback games.
way ANOVA with repeated measures on three factors
(tests, indices, and time-windows) was performed to
analyze the data in MATLAB, in order to compare the
difference between the three factors in the EEG data.
Turkey HSD post-hoc tests were carried out on the
selected significant factors. For all statistical analysis,
a significance level of 0.05 was chosen.
The game was implemented in JAVA, using open
source libraries from Brainathon, and Sun’s
COMMAPI library for the serial connection from
EEG device to the PC.
A simulated ball moves left or right along a horizontal
axis controlled by the player’s EEG signals relating to
level of mental relaxation. Two players can compete
simultaneously to determine who is more relaxed by
comparing the relative position of the ball. The
controllability of the ball was assessed by subject’s
perception of relaxation. See Figure 3 for a screenshot
of the game interface.
2.4.2 Configuration
The Brainathlon game consists of three consecutive
mini-games called courses. Each course monitors and
rewards activity in configurable brainwave ranges.
The game can be played by one player or by two
players simultaneously. When played in two-player
mode, the player who wins the most events will win
the game.
When the game is played by two players, the screen is
split in half vertically with player 1’s game board on
the left and player 2’s on the right, as shown in Figure
4.1. When a single player is playing, only one game
board appears on the screen, as shown in Figure 4.2.
During all of the courses, a small display of current
brainwave activity in the target frequency band is
displayed at the bottom of the screen. This display
provides additional feedback to the players and assists
them in identifying increases and decreases in brain
activity.
Figure 3. A screenshot of the game
interface
2.3.3 Experimental Results
This is the first study that attempted to measure
mental relaxation state using one channel (Fp1-Fp2)
EEG for game implementation. The EEG results
indicate that the sum alpha + theta, and sum of alpha
+ beta + theta are good indices for the measurement
of neurological relaxation. Game testing also reflects
that these indices have the capability to measure the
basic level of relaxation in at least half of the players.
Figure 4.1. Two-player mode
2.4 BRAINATHLON: Enhancing Brainwave
Control Through Brain-Controlled Game
Play [4] Palke, Amy.
2.4.1 Objective
The goal of this project was to create a different type
of application for the Modular EEG device. In the
spirit of Brain ball, a competitive neuro feedback
game was designed and developed. In addition to a
software game, the aim was to build a reusable library
of EEG acquisition and analysis components that
could be used to build other applications for use with
Figure 4.2. One-player mode
Each course has an accompanying XML
configuration file where users can set the frequency
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ranges, time limits, and other course variables. The
configuration is read into the application at runtime,
so changing the game to suit different players’
training interests and skill levels is easy.
As shown in figure 5, we constructed a game for brain
training by comparing the power value of left and
right cerebral hemisphere. Subjects can train the way
they use their brain stably with the help of this game.
The merit of this algorithm is it will help researchers
gather brain wave data which is stable and balanced.
The proposed game gathers raw data from the
subject’s scalp. After gathering the brain wave, we
process raw data of the left and right cerebral
hemisphere using FFT filtering. The value of FFT
level can be used to play games or end games by
comparing the balance of the left and right cerebral
hemisphere. Through this process, subjects can
practice producing appropriate brain wave by playing
this game systematically. The proposed game
includes a real time analysis device which shows the
power value of the left and right cerebral hemisphere.
It is possible for subjects to practice creating brain
wave and therefore use their brain wave to control a
function. The brain game is played by aproposed
algorithm which will be presented in the indicator. it
makes it easy for the subjects to understand the brain
wave data, that is being collected, in real time.
2.4.3 Experimental Results
The data from one-player and two-player games was
also analyzed separately to determine if either mode
was more effective in increasing alpha activity.
Although the player's average alpha amplitude was
higher for those who played alone, both modes
resulted in increased alpha activity with repeated
game play.
3. Implementation of a 3-Dimensional
Game for developing balanced Brain wave
In our research, we proposed the implementation of a
3-dimensional game for developing balanced brain
wave. It is an analysis game which analyzes raw brain
wave gathered. By comparing the power value of the
left and right cerebral hemisphere, the game is played.
The proposed game, which was used in an
experimental environment by the researchers, was
designed carefully to provide data needed. The
resource of the proposed game is in the analysis of the
power value of the left and right cerebral hemisphere
which is made from FFT (Fast Fourier Transform)
algorithm. It can be used to develop subjects’ ability
to control the left and right cerebral hemisphere in a
stable way. Through this training, it is possible that
the subject’s will be able to provide reliable and
meaningful brain wave for the researchers. The
subjects consist of 5 men and 5 women from Dae-Gu
Catholic University.
3.1 Entire system
The 3-dimensional game is used by comparing the
power value of left and right cerebral hemisphere.
Figure 6. Falling the rope by break balance
left and right cerebral hemisphere
3.2 Algorithm of the system
The proposed game works by using the FFT power
value of the left and right cerebral hemisphere which
is under in the selective environment. In order for
subjects to release brain wave related to BCI research,
subjects need to practice controlling their mind as
much as possible. We use sound and visual effect, and
the character in the game acts in a funny way to make
subjects concentrate better. We set the game like this:
if the subjects releases 120% more power than the
average value of normal brain wave, the character in
the game can move forward on the rope. 120% power
value is the threshold for the character to move
forward. In case when the brain wave is not balanced
in power value of the left and right cerebral
hemisphere, the character will fall from the rope.
Figure 5. Move forward by balanced power
value of left and right cerebral hemisphere in
proposed game 3D game
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Considering the error of measuring equipment, we set
a rule that its balance is broken if the power value of
the left and right cerebral hemisphere is more than 10
units. The proposed algorithm enables the subjects to
practice using and controlling brain wave by playing
this game.
Figure 7 shows processing FFT algorithm of 3-D
game.
values from the left and right cerebral hemispheres
stably. Figure 8 presents the architecture of the
proposed training device which consists of three
indicators. These indicators display brain wave
power.
Figure 7. Algorithm of processing FFT in 3D
game
Figure 8. Brain wave training device
Among the three indicators, the left one presents the
power of brain wave that comes from the left cerebral
hemisphere, which the subjects can see easily in real
time. The right one presents the power of brain wave
that comes from the right cerebral hemisphere. The
one in the middle presents the value (power value of
left brain wave – power value of right brain wave). As
in the picture shown above, with balanced brain-wave
value the indicator will indicates ‘0 value’ in middle
indicator. If it moves to the left, the power value of
the left brain wave is stronger than the right brain
wave, which means that the left cerebral hemisphere
is more activated. If it moves to right, the power value
of the right brain wave is stronger than left brain
wave, which means that the right cerebral hemisphere
activated. If subjects use either the left cerebral
hemisphere or the right one more during the training,
the balance of left and right cerebral hemisphere will
be broken. Subjects will be able to notice this
immediately by looking at screen with this training
device. Then subjects will try to adjust the balance of
the left and right cerebral hemisphere. The subject
will be able to keep balance. The interaction of
training device leads on improving the power value of
the left and right cerebral hemispheres stably.
3.3 Features of system
The important goal of our proposed 3D game is to
train subjects to practice using brain wave
systematically. Our 3D game provides more reliable
brain wave. Our proposed 3D game improves data
analyzed of a subjects’ brain wave by comparing the
power value of left and right cerebral hemisphere.
The results of experimental value are processed using
FFT in real time. Among the processed value of FFT,
the value of 2~8 Hz is θ (theta) value and the value
of 12~20 Hz is SMR(lowβ (beta)) + middle- β
(beta). They can be displayed as (SMR + middlebeta)/ (theta value) which is processed in each
channel. The power value of the left cerebral
hemisphere is input from channels 1 and 3 and the
power value of the right cerebral hemisphere is input
in channels 2 and 4. We call (SMR + middle-beta) /
(theta value) the degree of concentration. It is used for
the threshold value for moving forward on the rope
and comparison value of the left and right cerebral
hemisphere’s is used to control balance. In Figure 7
the entire process of analyzing brain wave data by
using the FFT filtering is presented. Also, the degree
of concentration can eliminate unnecessary band and
provide the necessary the power value for researchers.
By using this system, it is possible for us to study in a
systematical way using trained subjects in BCI
research. This processed brain wave data makes it
possible for researchers to apply it to the related BCI
research and to a lot of meaningful applications in a
ubiquitous system.
4. Experimental Evaluation
Our proposed 3D game is played by the power of
brain wave which is more than the threshold value
and by the analysis of left and right brain wave. This
3D game improves subjects’ ability to use their own
brain wave. To end the 3D game, subjects should
release stable and balanced brain wave. The
programming language visual C++ was used in the
development of this game.
3.4 Real-time brain wave analysis device
As mentioned, we also proposed a real time brain
wave analysis device. It is a training device that
improves subjects’ ability to release balanced power
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Figure 11. Results of play the 3D game
The first time, subjects play the game it is easy for
them. But, at second training, subjects have trouble
when subjects play the game. Subjects release a high
power brain wave so it is possible to move forward.
But, the left and right cerebral hemisphere balance is
broken by untrained subjects using brain wave. It
ends the game because the character falls off the rope.
However, when subjects try to play the game more
and more, the results are better than each previous
trail. In the final trail, we can find out that subjects
release balanced and stable brain wave. In order to
increase the difficulty of the game, researcher can set
the value of threshold and balanced level. The result
of the research is illustrated in Figure 12 and Figure
13. Through this research we can see that it is
possible for the human’s brain wave to develop with
brain wave training. Moreover, we adopted the use of
real time system to allow subjects to notice the value
of the brain wave according to the change of time and
situation. Figure 12 presents training results of
elapsed time before the end of the game.
Figure 9. System development environment
using visual c++
Measuring brain wave equipment is QEEG-4 system
(lxe3204). QEEQ-4 is made of a junction box, plate
electrode and paste. Figure 10 shows specification of
equipment.
Figure 10. Specification of brain wave
measuring equipment
4.1 Results
The proposed game system analyses the power value
of the left and right cerebral hemispheres. It is
possible for subjects to release balanced and stable
brain wave of from the data left and right cerebral
hemisphere. It gives us useful brain wave from raw
brain wave. In Figure 11, we present the result of
training 10 participants in brain wave research. In
Figure 11, we see the optimized result that human’s
brain wave can be controlled with brain wave
training. Based on the graphical result, we can find
out that the brain wave training has many possibilities
in BCI. After brain wave training, most subjects
release stable and reliable brain wave.
Figure 12. Average elapsed time (success)
Figure 13 presents number of failures, which is when
the character falls off the rope, therefore ending the
game.
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6. References
[1] Lalor E, Kelly S.P., Finucane C., Burke R., Reilly R.B.,
McDarby G.: Brain Computer Interface based on the
Steady-state VEP for Immersive Gaming Control,
Biomedizinsche Tecknik, (2004)pp. 63-64
[2] J. A. Pineda, D. S.Silverman,A.Vankov,andJ.Hestenes.:
Learning to Control Brain Rhythms: Making a BrainComputer Interface Possible, IEEE, (2003)pp. 181
[3] T. A. Lin, L. R. John.: Quantifying Mental Relaxation
with EEG for use in Computer Games, International
Conference on Internet Computing, (2006)pp. 409-415
[4] Palke, Amy.: Brainathlon, Enhancing brainwave control
through brain-controlled game play, Master thesis, Mills
College, (2004)
[5] Kulman, W.N.: EEG feedback training: enhancement of
somatosensory cortical activity. Electroencephalography
and Clinical Neurophysiology, (1978)pp. 290-294
[6] Vankov, A. Instantaneous evaluation of EEG rhythms
by variable epoch frequency decomposition. Society for
Neuroscience Abstracts, (2000)pp. 840.2
[7] Mu¨ ller, K.-R., Anderson, C., Birch, B.: Linear and
nonlinear methods for brain computer interfaces, IEEE
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Engineering, (2003)pp. 165–169
[8] Harel, D., Carmel, L., Lancet, D.: Towards an odor
communication system, Computational Biology and
Chemistry 27, (2003)pp.121–133.
[9] Peters, B.O., Pfurtscheller, G., Flyvbjerg, H.: Automatic
differentiation of multichannel eeg signals, IEEE
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[10] Delorme, A., Makeig, S. EEGLAB: an open source
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[11] Ming, C., Xiaorong, G., Shangkai, G., and Dingfeng,
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[12] Wolpaw, J.R., Birbaumer, N., McFarland, D.J.,
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Figure 13. Average elapsed times (fail)
As a result of this research, we know that most
subjects improve their ability to use brain wave
stably. We know the proposed 3D game and training
device system provides researchers with more
capable, versatile, stable, and reliable data collected
form trained subjects than trained subjects.
5. Conclusions
The BCI is emerging in important research because it
is easy to use in any special environment. This
research will help to improve services for the disabled
as it can be operated 24 hour a day. Brain wave
signals are very weak and are affected by their
surroundings a lot. Besides a lot of errors come out
according to the electrode stability. Accordingly, we
developed brain wave training device and made a 3D
game using brain wave to produce balanced and
stable brain wave of the left and right cerebral
hemisphere By using this game consistently users will
produce brain wave signal as a means of HCI more
accurately through this training.
In this paper, we proposed and implemented a 3dimensional game for developing balanced brain
wave. In this experiment we found the fact that the
possibility of winning the game increases as the brain
wave training continues and the time to finish the
game shortens. Through this we know that a human’s
brain wave can be developed with brain wave training
and it is possible to use it in the field of HCI. Subjects
can perform very complicated functions if it is
performed consistently. We tried to develop very
effective brain wave learning devices and make very
interesting games. The result implies that using our
proposed 3D game can increase the reliability and
stability of gathered brain wave and improve subjects
using brain wave. This study will be used in many
application researches and applied to the real life. The
future BCI research will be focused on high quality of
life with the development of various BCI techniques.
The paralyzed patients and workers who work in a
particular environment can control some robotic
machines like controlling their own body by using our
proposed 3D game training, which can ease the
complaints made by researchers in BCI.
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