Engineering, 2013, 5, 58-62
doi:10.4236/eng.2013.55B012 Published Online May 2013 (http://www.scirp.org/journal/eng)
The Design of the Fuzzy Inference System for the
Determination of Attention
Hye Jin Kim1, Sun K. Yoo2*
1Graduate School of Biomedical Engineering, Yonsei University, Seoul, Korea
2Department of Medical Engineering, College of Medicine, Yonsei University, Seoul, Korea
Email: email@example.com, *firstname.lastname@example.org
In this study, by using the response speed and the number of errors resulting from the children’s concentration test
through the fuzzy inferenc e system and comparing it to the theta which is one of the EEG’s parameter to find the level
of concentration. Targeting 21(Male 12, Female 9) healthy children between the ages of 10 - 14, the test was conducted
one time with a duration of 14 minutes. For the first 5 minutes the children were listening to the Bach’s Air on a G
string having a steady state and the next 9 minutes the children were subjected to the external stimuli audiogenic stimu-
lation that induces attention co ncen tration. When the number 3 wa s heard, ch ildren w ere subjected to press down on the
spacebar to check the response speed and the number of errors. By conducting computerized neurocognitive function
test to compare the theta wave related to the concentration with th e response speed and the number of errors that deter-
mines the attention con centration through the fuzzy system, the data from 15 children out of 21 have shown the results
for the concentration. In order to check the concentration level, a fuzzy inference system which was designed by the
user could be used.
Keywords: Attention; Computerized Neuro-physiological Test; Fuzzy system
The modern society that is represented by knowledge •
Information shows an interest on the importance of
learning and learning for school aged children is growing
more than anything else. The factors that affect the
learning of children this time is the basic cognitive abili-
ties to focus attention on a particular object or targ et. The
children's ability to learn is in correlation between the
attitude and the attention concentration and the restric-
tions and the lack of the attention concentration is the
primary cause of poor learning ability [1,2].
The attention con centration is an ability [3 ] to focu s on
the consciousness from an internal • external stimuli, the
process that focus selectively on relevant stimuli ac-
cording to the info rmation of the sensory receptors th at is
required for learning will select its input and filtrative
function [5,6]. Without the attentio n concentration, better
information processing is not possible and decides what
to focus on for ourselves and what information to transfer
in meaningful form that can be stored from the sensory
Children lacking the attention concentration have de-
clining attention for the surroundings in daily life and
their attention concentration becomes distracted and dis-
turbed even with a small stimuli showing problems such
as hyperactivity, unstable emotions or poor self-esteem
Also, they can be focused on the things of their interest
but monotonous and repetitive, and when performing a
tedious task it seems difficult to concentrate con tinuously
[9,10]. They cannot sit still du ring a school hour but run-
ning around with friends or simply doing other behaviors
can be cautioned but only for a little while and even tually
they will again show an erratic behavior . Therefore
attention concentration is a mandatory basic skill that is
needed for the school aged children and teenager’s
learning, acquiring new information and adap tin g to lo cal
Nowadays, computerized neuropsychological tools are
commonly used to measure the attention concentration
and such inspection tools contributes to more objective
scoring results and interpretation, Compared to such tra-
ditional rating scale, expenses can be reduced for the
time it takes for the implementation of the inspection,
grading and reporting results, with an advantage of ques-
tions of the presenting time and inspection, controlling of
conducting time and it is easy to standardize the implan-
tation for the inspection .
Copyright © 2013 SciRes. ENG
H. J. KIM, S. K. YOO 59
In recent years, using computerized neuropsychological
tests not only help to improve children's concentration
but ADHD group and normal children, and may be used
not for ADHD but neurocognitive function appearance
for a group of pediatric metal disord ers . In addition,
it is useful to be clinically used for patients with brain
damage which i s widely used i n va rious fields.
However, the attention concentration test using a
computerized neurocognitive program cannot be clear
about how much it had induced the child to be focused
on the test. To have more definitive interpretation many
studies have been conducted together with a bio-signal
measurement. Especially an analysis for concentration
using an EEG is being conducted and the status of con-
centration from a stimuli induced events can be seen
through the alpha and theta wave in a median line of the
brain , and also during a short-term concentration
experimental test due to external stimuli and based on
previous research results that have indicated an increase
in the theta rhyth m, in order to find the level o f attention
concentration, an EEG such as theta can be an important
Therefore, in this study using computerized neurocog-
nitive program to perform a concentration test for the
child who requires the attention concentration and by
designing the Fuzzy Inference System that determines
the level of concentration obtained from the response
speed and the number of errors, in order to assess the
system checking the level of concen tration during the test
using definit e parameters theta wave was co nd uct e d .
This test was conducted using health y students, 12 males
and 9 females with total of 21 persons without a history
of cardiovascular and neur ological disorders. An average
age for the children was 11 with the range of 10-14 years
of age and included elementary 3rd graders to freshmen
of junior high school. The subjects were recruited through
the Electronics and Telecommunications Research Insti-
tute (ETRI) bulletin board. All subjects have performed
the concentration inducing test using the external stimuli
2.2. Experimental Design
An external stimuli that induces the attention concentra-
tion, CNT4.0 (Computerized Neuro-physiological Test:
Computerized Neurocognitive tests) commonly used in
the hospital program was used . The audiometry test
used within the CNT program for this study was Audi-
tory Continuous Performance Test which is one kind of
CPT (Continuous Performance Test: Continuous Per-
formance Test). This test was performed by having the
subjects to listen to the target stimulation of the number
‘3’ where the subjects have to press the response button
as fast as possible in order to measure the number of
correct response, the number of omission errors and the
number of false alarm errors. For the correct response is
randomly presented 15 times every 60 seconds, distur-
bance stimulation 5 times each and the response time was
measured in the units of 1/100 seconds.
The basic paradigm of CPT makes occasional target
stimuli or possible to measure selective attention to rele-
vant stimuli. Therefore, the CPT program performs the
inspection by rapidly presenting the target stimuli or tar-
get pattern while constantly changing and the time
needed to perform the test will vary with each inspection
but it was designed sufficiently to measure the attentive-
The screen shown in Figure 1 was an actual CNT in-
spection monitored using a CNT program and the resu lts
of the correct response, the number of omission errors
and the number of false alarm errors can be seen(Table 1 )
Figure 1. CNT hearing test results.
Copyright © 2013 SciRes. ENG
H. J. KIM, S. K. YOO
Table 1. The attention concentration, computer assignments
test results items.
Item test results Interpreting the results
Correct response Measuring the lev el of concentration by subject’s
The number of
omission errors Measuring the area of non-response to the target
stimuli by the s ubje cts.
The number of
comission errors Measuring the impulse that occurs when subjects
in response to non-tar get stimuli and disinhibi t ion.
The test process was performed by attaching the EEG
electrodes to the subjects and before the testing of the
cognitive ability test was conducted, the subjects had
listened to the ‘Air on a G string; for 5 minutes with an
opened eye. This is to have the subjects maintain a steady
state before the external stimuli test is performed .
After a steady state, running the CNT program inducing
audiometry stimuli for 9 minutes measuring the EEG and
the attention con centration test was conducted.
2.3. Design of Fuzzy Inference System
It is known that each relationship between response rate
and concentration with the error rate and the concentra-
tion do exists. According to the study of Japan's National
Institute of Radiolog ical Science, results were drawn that
chewing gum can increase the concentration and during
such moment whether or not such concentration was
made were confirmed th rough the response speed.
And of computerized neurocognitive program having
low number of CPT in correct response and slow reaction
time, further determining the number of false alarm er-
rors and the number of omission errors, it was suggested
that abnormal cases had attention concentration problem.
However when comparing the each concentration test
result with the response speed and the number of errors,
and because there are many ambiguity to determine the
level of concentration, therefore using uncertain informa-
tion with set of membership fu nction to check th e degree
of the binary system logic of each object belongs or does
not belong to any gatherings, a fuzzy theory that articu-
lates mathematically was used.
The system to check the level of concentration is con-
trolled by the fuzzy theory. Two inputs are the response
speed and the number of errors and the output is the level
of concentration. Through fuzzification section, a fuzzy
set is structured having two inputs and one output. The
range of response speed is 0.6030 seconds which was the
fastest and 0.0776 seconds which was the slowest and
based on the maximum value of the number of errors it
has a range of 0 to 65. The range of response speed is
divided into 3 membership functions; fast, average and
slow. The number of errors is divided into 5 membership
functions; very low, low, average, many, extremely many.
Having 13 or less is regarded as very low errors and 65
or more is regarded as extremely many errors. The output
has 2 membership functions; have concentrated and not.
For each of the membership functions, it was able to se-
lect a range of distinctly different and range could be
divided differently. This, as with all design activities it is
based on the experience of designers and system re-
The total number of the rules in the rule-base is the
value multiplied by the number of each set of input
variables. The response speed has 3 membership func-
tions and the number of errors has 5 so there are total of
Mamdani type fuzzy reasoning rules were used.
By taking the maximum value from the goodness of fit
calculated by the fuzzy arithmetic, found the output
member value and in order to find the exact value related
to the level of concentration it must use defuzzification.
The output values were computed based on the center
of gravity method. The centroid method is multiply the
only value and the corresponding value of the output
member, divided by the sum of the belonging value.
The system is designed by using MATLAB R2009a as
shown in Figure 2.
Using the self designed Fuzzy Inference System to
obtain concentration result and changes in the Theta
wave result (Table 2) from the audiometry test result
(Table 3) show that 15 out of 21 person’s data had
matched each other’s results (Table 4).
By using the response speed and the number of errors
obtained through the concentration test for children, to
find out whether or not the subject has concentrated or
not by using the results and the Theta wave, one of EGG
parameter, from a fuzzy system design. As a result 15 out
of 21 person’s data had matched each other’s results.
Copyright © 2013 SciRes. ENG
H. J. KIM, S. K. YOO 61
Figure 2. Fuzzy infe rence system.
Table 2. Change of theta wave.
Change of Theta wave
Name Before experiment After experiment
Subject 1 KSM 0.1323 0.1533
Subject 2 KYK 0.1072 0.1349
0ubject 3 KYJ1 0.0999 0.0999
Subject 4 KYJ2 0.095 0.0927
Subject 5 KYH 0.1115 0.1389
Subject 6 KYJ 0.1108 0.1077
Subject 7 REH 0.112 0.077
Subject 8 PAH 0.0956 0.1178
Subject 9 SHN 0.0898 0.083
Subject 10 LSM 0.1085 0.1262
Subject 11 LYH 0.1499 0.0965
Subject 12 LJJ 0.0993 0.083
Subject 13 JYJ 0.1209 0.1142
Subject 14 JMH 0.0858 0.102
Subject 15 JES 0.128 0.1298
Subject 16 JJB 0.1038 0.1199
Subject 17 JJ 0.1495 0.1429
Subject 18 JHG 0.0972 0.0995
Subject 19 CHR 0.1142 0.1226
Subject 20 HJW1 0.1144 0.0911
Subject21 HJW2 0.1359 0.1249
Table 3. CNT hearing test results.
CNT hearing test results
Name Reaction time The number of Er rors
Subject 1 K S M 0.615 12
Subject 2 KYK 0.684 41
Subject 3 KYJ1 0.627 44
Subject 4 KYJ2 0.776 27
Subject 5 KYH 0.665 16
Subject 6 KYJ 0.646 42
Subject 7 REH 0.655 41
Subject 8 PAH 0.622 24
Subject 9 SHN 0.76 9
Subject 10LSM 0.68 12
Subject 11LYH 0.714 31
Subject 12LJJ 0.662 39
Subject 13JY J 0.664 43
Subject 14JMH 0.603 64
Subject 15JES 0.672 7
Subject 16JJ B 0.706 46
Subject 17JJ 0.667 36
Subject 18JHG 0.713 24
Subject 19CHR 0.682 30
Subject 20HJW1 0.71 16
Subject21HJW2 0.672 28
Table 4. Comparison of results matching the results of fuzzy
Inference system and theta waves of change.
Name check the concentratio n l ev el
Subject 1 KSM O
Subject 2 KYK X
0ubject 3 KYJ1 O
Subject 4 KYJ2 O
Subject 5 KYH O
Subject 6 KYJ X
Subject 7 REH O
Subject 8 PAH O
Subject 9 SHN O
Subject 10 LSM O
Subject 11 LYH O
Subject 12 LJJ O
Subject 13 JYJ X
Subject 14 JMH X
Subject 15 JES O
Subject 16 JJB O
Subject 17 JJ O
Subject 18 JHG O
Subject 19 CHR O
Subject 20 HJW1 X
Subject21 HJW2 X
In order to secure more data from many subjects and
Genetic algorithm and more sophisticated fuzzy system
through a combination of different algorithms, future
research goal is to improve the performance of the sys-
tem presented in this study.
Copyright © 2013 SciRes. ENG
H. J. KIM, S. K. YOO
Copyright © 2013 SciRes. ENG
This work was supported by the National Research
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