Engineering, 2013, 5, 284-291
http://dx.doi.org/10.4236/eng.2013.510B059 Published Online October 2013 (http://www.scirp.org/journal/eng)
Mean Threshold and ARNN Algorithms for Identification
of Eye Commands in an EEG-Contr olled Wheelchair
Nguyen Thanh Hai1, Nguyen Van Trung2, Vo Van Toi1
1Biomedical Engineering Department, International University, Vietnam National University, Ho Chi Minh City, Vietnam
2Faculty of Ele ctrical-Electronics Engineering, University of Technical Education, Ho Chi Minh City, Vietnam
Received July 2013
ABSTRACT
This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye
movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes
blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass
filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured
signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The
coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a
mean threshold algorithm was employed for classifying eye movements. Two metho ds were compared to find the better
one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of con-
trolling the wheelchair in the indoor environment illustr ated the effectiveness of the proposed approaches.
Keywords: Autoregre ss ive NN Mo de l ; Threshold algorithm; EEG Technology; Eye Activity and Electrical
Wheelchair
1. Introduction
Human brain plays an important role in controlling all
body activities [1]. Moreover, it is a complex structure,
in which there are about around 100 billion neurons
which communicate from one to another with or without
external excitations to make control decisions (cognition,
motion, pattern recognition, etc.). For these reasons, non-
invasive technologies such as EEG, functional Magnetic
Resonance Imaging (fMRI) and functional Near-infrared
Spectroscopy (fNIRS) have been investigated to quantify
motor proc e s s ing funct i on of huma n bra in [2-4]. Thus the
exploration of these technologies can allow us to perform
rehabilitative problems or brain simulator leading to im-
prove or recover the motor/cognitive functions of tetrap-
legic patients with spinal cord injuries and degenerative
nerve diseases.
In recent years, EEG technology has quickly devel-
oped and also attracted many researchers related to hu-
man brain. Many Brain-computer Interface (BCI) appli-
cations as well as Brain-based diagnoses have been suc-
cessfully represented, in which BCI problems have been
investigated to implement on human in recent years. In
particular, a BCI system can allow people to communi-
cate and to control external devices [5-7]. It means that
one can translate brain activities into messages or com-
mands to control devices [8-10]. Blankertz et al. devel-
oped the non-invasive BCI system, in which the key fea-
tures were considered to predict the laterality of upcom-
ing left vs. right hand movements to produce the result of
very high information transfer rate.
An EEG system has been used to measure delta signal
of human brain corresponding to eye blinks [11]. However,
to determine the problem of eye activities, a threshold
algorithm was employed to control electric wheelchair.
In this paper, we develop a neural network model [12-14],
in which the inputs of the network are AR coefficients
[15] which were determined based on the filtered EEG
signals using a Hamming lowpass filter from the identi-
fied outputs of the network. In addition, a threshold algo-
rithm is employed to find the m ean thresholds for eye move-
ments. In this res earch, two of these methods will be com-
pared to determine the best one to control the wheelchair.
2. Materials and Methods
2.1. Data Acquisition
Data at Fp1, F7, F8 areas of human brain were obtained
from an Active-Two system as shown in Figure 1. Nine
subjects (males and females, average age: 22 ± 5.33)
were invited to participate into this study. The subjects
informed consents agreement after reading and under-
standing of the experiment protocol and the EEG tech-

N. T. HAI ET AL.
285
Figure 1. Subject with electrodes for obtaining data on the
system.
nique. Offline data were obtained at some positions such
as at Fp1, F 7, F8, CMS and DR L (see Figure 2) on h ead
of each subject through the Active-Two system. The
subjects were instructed to perform their eye activity
times (opening eyes, blinking two eyes, glanced left,
glanced right), each subject performed his/her eye ac-
tivity in 5 seconds.
2.2. Signal Pre-Processing
In the EEG signal processing, the original signal is
passed through a band-pass filter with an impulse re-
sponse in ord er to produce the output of the filter ,
][ ng
.
For the convolution operation between the EEG signal
and the impulse response of the Hamming low pass filter,
it is described as follows:
[][][][] []
HH
n
gnxnh nxkhnk
∞
=−∞
=×= −
∑
(1)
where
][ nx
is the EEG signal and ][nhH is the im-
pulse response,
kn,
= 1,2,…N.
The impulse response of the actual Hamming filter is
calculated as follows:


≤≤
=otherwise
N-n nwnh
nhH0
10][][
][
(2 )
where
][nw
is the Hamming window and
][nh
de-
notes the ideal impulse response.
To reject influence by voltage drift, the output signal is
calcula te d using the f ollowing formula:
N
ng
ngny
N
n
∑
=
−=
1
][
][][
(3)
In this paper, the number of the EEG signal samples is
N = 1024. The original signal and the filtered signal at
position of F8 are processed as shown in Figure 3.
After filtering noise by the filter, th e filtered EEG sig-
nals corresponding to eye events such as opening eyes,
blinking eyes, glancing left and glancing right (see Fig-
ures 4 and 5) are calculated to determine coefficients for
the recognition of eye activities using neural networ ks.
Figure 2. Five electrodes were installed at 5 positions.
Figure 3. Original signal (F8) and the filtered noisy signal.
2.3. AR Model for Feature Extraction
In this paper, an Autoregression (AR) model is used to
extract the features of the EEG signal. In the A R model,
coefficients are determined using the equation:
)2()1()(
21
−+−= nyanyany
(4)
where
)(ny
is the filtered EEG signal, n = 1, 2, …,
1
a
and
2
a
are two coefficients of the AR model.
In the EEG signals collected at three channels FP1, F7,
F8, each channel has two AR coefficients. H ence in four
experiments (blinked, opened eyes, glanced left and
glanced right), one will create four vectors and each vec-
tor has 6 AR coefficients as shown in Table 1.
2.4. Neural Network Model
Classification is an important step to determine the acti-
vity of the eye. After being extracted the signal features
of the eye activities using the AR mode, it produces the
coefficients, which will be transmitted directly into back
propagation neural networks with two hidden layers (see
Figure 6) for training [15].
The back-propagation network is to minimize the erro r
function in the weight space by the reduced gradient me-
thod. Because this method of calculating the gradient of
the error function at each iteration requires that the error
function should be continuous and indivisible. One of the
activation function used in this paper is the sigmoid
CMS
F7
F8
DRL
Fp1 electrode
00.5 11.5 22.5 33.5 44.5 5
-500
0
500 Si gnal channel F 8 (Raw)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-50
0
50
100
150 Si gnal channel F 8 (Fi l tered)
Ti me (s)
Amplitude

N. T. HAI ET AL.
286
Table 1. Vector of ar coefficients for four experiments.
Opening eyes (ao) Blinking eyes (ab) Glancing left (al) Glancing right (ar)
Fp1 F7 F8 Fp1 F7 F8 Fp1 F7 F8 Fp1 F7 F8
ao11 ao71 ao81 ab11 ab71 ab81 al11 al71 al81 ap11 ar71 ar81
ao12 ao72 ao82 ab12 ab72 ab82 al12 al72 al82 ap12 ar72 ar82
(a)
(b)
Figure 4. (a) Filtered signal y[n] in case of opening eyes; (b)
Filtered signal y[n] in case of blinking eyes .
function, which is described as follows:
x
e
xS −
+
=1
1
)(
(5)
Consider a back-propagation neural network with n
input, m output, contains a number of hidden layer neu-
rons to form the training data set (on - off), in which the
desired set, (x1,d1), (x2,d2),..., (xp,dp) contains P m × n pair
of vectors. The weights will be chosen at random. When
the data set xi are trained to create the different outpu t set
(Oi,di), then the error function E is calculated by the fol-
lowing f ormula:
∑
=
−=
P
i
ii
dOE
1
2
)( α
(6)
where P is the number of samples, O is the network out-
put, d de n otes the de s i r ed output and α is constant.
(a)
(b)
Figure 5. (a) Filtered signal y[n] in case of glancing left; (b)
Filtered signal y[n] in case of glancing right.
Figure 6. The structure of a NN model with two hidden
layers.
The back-propagation algorithm is used to find the lo-
cal minima of the error function. Therefore, the gradient
of the error function is calculated to change the initial
weight values for the network. The weights are the pa-
rameters changed to reduce errors and then each weight
will increase a typical value:
w
E
w∂
∂
−=∆
η
(7)
00.5 11.5 22.5 33.5 44.5 5
-50
0
50 Si gnal channel F p1 (Fi l tered)
Ti me (s)
Ampl itude
00.5 11.5 22.5 33.5 44.5 5
-100
0
100 Si gnal channel F 7 (Fi l tered)
Ti me (s)
Ampl itude
00.5 11.5 22.5 33.5 44.5 5
-50
0
50 Si gnal channel F 8 (Fi l tered)
Ti me (s)
Ampl itude
00.5 11.5 22.5 33.5 44.5 5
-500
0
500 Si gnal channel F p1 (Fi l tered)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F7 (F i l t ered)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F8 (F i l t ered)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-100
0
100 Si gnal channel F p1 (F il tered)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-100
0
100 Si gnal channel F 7 (F il tered)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F 8 (F il tered)
Ti me (s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-100
0
100 Si gnal channel Fp1 (F i l t ered)
Ti me ( s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F7 (F i l t ered)
Ti me ( s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F8 (F i l t ered)
Ti me ( s)
Amplitude
•
•
•
•
•
•
•
•
•
Input
Layer
Hidden-1
Layer
Output
Layer
Hidden-2
Layer

N. T. HAI ET AL.
287
where the weight vector w is in the network,
η
denotes
the learning rate (constant),
wE ∂∂ /
is the derivative of
the error function w.
Although there are many rules to optimize the neural
networks developed, the network architecture was often
derived from trial and error approach. Another factor
affecting the convergence of back propagation algorithm
is the learning rate,
η
. With the large value of
η
, the
network will increase the learning rate, but the network
with the too large value will not be able to converge.
Inversely, small values can ensure the convergence algo-
rithm, but the learning rate is very slow. For this reason,
the algorithm with adaptive learning rate is applied and
described as follows:
ηηη ∆+=+ )()1( kk
(8a)
and error of learning rate





−=∆
0
)(
)(
kb
ka
η
η
η
(8b)
where a is the increase coefficient, b is the decrease
coefficient,
)(k η
is the kth learning rate.
2.5. Mean Threshold Algorithm
In this project, a threshold algorithm will be applied to
determine cases of open eye, two eyes blinking, glanced
left, glanced right. The average value M of open eye sig-
nals is calculated using the following equation:
N
N
ny(n)
M
∑
=
=1
(9)
where y(n) is the set of EEG signals (rejected voltage
drift) and N denotes the number of samples.
From Equation (9), the standard deviation SD in case
of open eye signal can be calculated as follows:
N
N
nMny
∑
=−
=1))(( SD
(10)
A mean threshold algorithm ThM is built to determine
cases of eyes open, eyes blinks, the left and right glance:
SDa*−= MThM
(11)
where a is the coefficient of the standard deviation.
This paper shows the detection of eye states based on
the change of amplitude of signals with its frequency
range (0.5 to 3.5 Hz) at Fp1, F7 and F8 positions. There-
fore the mean threshold determined based on EEG sig-
nals in the open eye case plays an important role.
To reduce the error of eye blinking recognition, the
threshold value ThM was calculated in the case of open
eye by comparing with the maximum values in the
measured times of eye movements to determine the coef-
ficient a. Therefore, the mean thresholds ThM at the po-
sitions Fp1, F7 and F 8, w ere calculated as follows:
Fp1
OFp1 BFp1
(OFp1, R F p1, LF p1)Max Max
ThM Max
=
<<
(12)
( )
RBF7OF7 F7
Min <<Min
=Min OF7,LBF7,BF7
ThM (13)
( )
RBF8OF8 F8
Min <<Min
=Min OF8,LBF8,BF8
ThM (14)
where Max is the maximum amplitude at Fp1 of the eye
opening signal (OFp1), right glance signal (RFp1), left
glance signal (LFp1) and MaxBFp1 denotes the maximum
amplitude in the case of blinking eye at Fp1.
3. Results and Discussion
EEG signals were collected at three channels FP1, F7
and F8, in which each channel is processed to extract
features. Mainly two methods of the ARNN model and
the mean threshold algorithm were applied to find the
best method for the wheelchair control.
3.1. Features of Eye Movements Using AR Mode
From the EEG signals of eye movements, each channel
has two AR coefficients. Hence one of the eye states
created a vector with six AR coefficients as shown in
Table 2. Time for an eye activity is less than 1 second,
so the eye activity is just set 1 second. The vectors are
inputs of the feedforward neural networks.
Figures 7(a), 7(b), 8(a) and 8(b) showed the AR
model coefficients of the signals corresponding to eye
open time at 1 second. From the figures, we see that in
the case of eye open and eye blinks, the signals are the
same, so the coefficients of three channels are nearly
equal. While Figures 9(a), 9(b), 10(a) and 10(b) re-
present different coefficients of glancing left and right.
All different coefficients generate four vectors (in Table
2).
Figure 11 represents six coefficients versus the am-
plitude of signals. The c la ssification of four coefficient
vectors shows that they are the same shape of signals but
Table 2. AR coefficient vectors.
Eye activity Coefficient vectors
Opening eyes −1.101 0.203 −1.059 0.189 −1.158 0.186
Blinking eyes −1.952 0.956 −1.715 0.720 −1.566 0.569
Glancing left −1.252 0.396 −1.205 0.266 −1.710 0.892
Glancing right −1.078 0.109 −1.369 0.372 −1.159 0.162

N. T. HAI ET AL.
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(a)
(b)
Figure 7. (a) Signals of opening eyes; (b) AR model coeffi-
cients.
(a)
(b)
Figure 8. (a) Signals of blinking eyes; (b) AR model coeffi-
cients.
(a)
(b)
Figure 9. (a) Signals of glancing left; (b) AR model coeffi-
cients.
(a)
(b)
Figure 10. (a) Signals of glancing right; (b) AR model coef-
ficients.
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F p1 (F il tered)
Ti me (s)
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F 7 (F il tered)
Ti me (s)
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F 8 (F il tered)
Ti me (s)
Ampli tude
123
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
six cofficients
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-500
0
500 Si gnal channel Fp1 (F i l t ered)
Ti me ( s)
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F7 (F i l t ered)
Ti me ( s)
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F8 (F i l t ered)
Ti me ( s)
Ampli tude
123456
-2
-1.5
-1
-0.5
0
0.5
1
six cofficients
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-500
0
500 Signal channel F p1 (Fi l tered)
Ti me ( s)
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Signal channel F 7 (Fi l tered)
Ti me ( s)
Ampli tude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Signal channel F 8 (Fi l tered)
Ti me ( s)
Ampli tude
123456
-2
-1.5
-1
-0.5
0
0.5
1
six cofficients
Amplit ude
00.5 11.5 22.5 33.5 44.5 5
-500
0
500 Si gnal channel Fp1 (F i l t ered)
Ti me ( s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F7 (F i l t ered)
Ti me ( s)
Amplitude
00.5 11.5 22.5 33.5 44.5 5
-200
0
200 Si gnal channel F8 (F i l t ered)
Ti me ( s)
Amplitude
1 2 3 4 5 6
-1. 4
-1. 2
-1
-0. 8
-0. 6
-0. 4
-0. 2
0
0.2
0.4
six cofficients
Ampli tude

N. T. HAI ET AL.
289
Figure 11. Representation of six coefficients of the eye ac-
tivities.
their amplitudes are different. These coefficient vectors
will be applied to inputs of the neural network model for
recognizing the eye activities.
3.2. Identification o f E ye Movements Using NN
Subjects worked out their tasks for recordings, in which
20 times blink eyes, 15 times glanced left, glanced right
15 times and 20 times to open eyes. Thus, we have a total
of 70 vector samples, in which 50 sample vectors (eye
blinking_15, glancing le ft_10, glancing right_10 and
opening eyes_10) will be used to train the Artificial
Neural Networks (ANNs) and the 20 remaining vectors
will be applied to check the training results.
The experiment using a two hidden layers network
structure with sigmoid function has its output is a linear
function as described in Table 3. In this experiment, the
number of hidden layer neurons was chosen as in Table
4, in which the first hidden layer with 15 neurons and the
number of neurons in the second hidden layer will be
changed for investigating the accuracy of the network.
From Table 4, we see that the network with the 25 neu-
rons in the 2nd hidden layer is the highest with the aver-
age accuracy of 94%.
In this paper, the feed forward neural networks with
back-propagation learning rule using gradient reduction
algorithm were used, in which learning rate is 0.001
(learning rate is the smaller, training time is longer, but
the obtained results are more accurate). While the num-
ber of iterations is 1000 (the number of iterations is as
large as possible, because the error of the network out-
puts and the real outputs is smaller), the increasing ratio
of learning rate is a = 1.07 and the decreasing ratio of
learning rate b = 0.7.
3.3. Eye Movements Using M ean Threshold
From Equations (11), (12) and (13), one determined the
coefficients a, in which 3.5 < aFp1 < 13.75 and we chose
aFp1 = 11 at the Fp1 position for the case of eye blink s and
similarly, aF7 = −4 and aF8 = −4 were chosen for the posi-
tions, F7 and F8. Based on the v alues aFp1 = 11, aF7 = −4
and aF8 = −4, the mean thresholds were calculated as
Table 3. Description of the NN outputs.
Eye activity Desired outputs
Opening eyes 1 0 0 0
Blinking eyes 0 1 0 0
Glancing left 0 0 1 0
Glancing right 0 0 0 1
Table 4. Results using neural networks.
Number of
hidden layer
neurons
Accuracy (%)
Eyes
open Eyes Blinks Left Glance Right Glance Average
15 * 10 95 95 78 85 89
15 * 20 95 90 90 85 90
15 * 25 90 97 92 95 94
15 * 30 90 95 90 90 92
shown in Table 5 In similarity, the mean threshold val-
ues were obtained on nine subjects as shown in Table 6.
The mean threshold values were applied into eye tasks
and we recognized the eye activities times (see Figure
12).
From Table 7, the ANN method gives the higher per-
formance. However, time for training data is more ex-
pensive. Therefore, which method chosen here is depen-
dent on each typical application.
The author et al. applied the SVMs and ANNs for the
eye movements using EEG and showed the classification
accuracies, in which the SVMs is 90.8% and accuracy of
86.8% is of the ANNs [13]. While based on AR coeffi-
cients, the ANNs have accuracy of 93.5% and the accu-
racy of the mean threshold is 86.25% in this paper. This
means that our proposed methods are the effectiveness.
3.4. Wheelchair Control Strategy
In a Brain-Computer Interface (BCI) system for control
of an electric wheelchair as shown in Figure 13, the user
was concentrating to drive the wheelchair by the eye
movements. Figure 14 shows the directions of the elec-
tric wheelchair, in which the wheelchair can be driven to
move with commands such as forward, backward, stop,
turning left and turning right.
The wheelchair was designed to move with the speed
of 5 km/h in the indoor environment. For the smooth
movement of the wheelchair, when the wheelchair rece-
ives a typical command to move to the left or the right, it
was designed to follow a curve around the inflection
point of the cubic equa tion.
4. Conclusion
This paper investigated an AR neural network algorithm
and the mean threshold algorithm in an EEG-controlled
-2 .5
-2
-1 .5
-1
-0 .5
0
0 .5
1
1 .5
012 34567
Coefficient
Amplitude
Opening eyes
Blinking eyes
Glancing left
Glancing right

N. T. HAI ET AL.
290
Table 5. Experimental results.
Times
Fp1 F7 F8
Eye Blink Eye open Right glance Eye open Left glance Eye open
MaxFp1 MaxBFp1 ThMOFp1 MinRB F7 MaxOF7 ThMOF7 MaxRBF8 MinOF8 ThMOF8
1 56 166 121 −91 −32 −120 −112 −45 −56
2 30 182 154 −103 −33 −128 −146 −24 −64
3 39 171 154 −112 −38 −44 −89 −35 −84
4 29 164 143 −133 −61 −80 −125 −39 −108
5 58 147 110 −110 −59 −76 −117 −33 −112
6 29 163 110 −112 −22 −76 −109 −25 −112
Mean 40 165.5 132 −110 −40 −84 −116 −33.5 −89
Table 6. The mean thresholds.
Subject ThMOF1 ThMOF7 ThMOF8
S1 132 −84 −89
S2 134 −100 −93
S3 145 −89 −87
S4 156 −80 −80
S5 160 −112 −100
S6 142 −102 −87
S7 115 −98 −85
S8 143 −87 −80
S9 165 −90 −98
Mean 143 −93 −88
Table 7. Results of two methods.
Method
Accuracy (%)
Eyes
open Eyes Blink Left Glance Right Glance Average
ANN 90 97 92 95 93.5
ThM 85 90 85 85 86.25
wheelchair for severely disabled people. From original
signals, the Hamming low pass filter was applied to pro-
duce the frequency bands for feature extraction. The
coefficients, which bring the feature of each eye activity,
are extracted using the AR model. These coefficients
generated the feature vectors for connecting to inputs of
the neural network which was employed to recognize the
eye movements such as opening eyes, blinking eyes,
glancing left and glancing right. After recognizing these
characteristics, user can drive the wheelchair to reach the
target. Experimental results showed that the wheelchair
user can move in the indoor environment.
(a)
(b)
(c)
Figure 12. (a) The threshold ThMOFp1; (b) The threshold
ThMOF7; (c) The threshol d ThMOF8.
00.5 11.5 22.5 33.5 44.5 5
-100
-50
0
50
100
150
200
250 Si gnal c hannel F p1 (F i ltered)
Ti me ( s)
Ampli tude
THR
OFp1
=143
Eyes open
Eyes blinks
00.5 11.5 22.5 33.5 44.5 5
-140
-120
-100
-80
-60
-40
-20
0
20
40
60 Si gnal channel F 7 (Fi l tered)
Ti me (s)
Amplitude
THR
OF7
=-93
Eyes open
Glanced right
00.5 11.5 22.5 33.5 44.5 5
-120
-100
-80
-60
-40
-20
0
20
40 Si gnal channel F 8 (Fi l tered)
Ti me ( s)
Amplitude
THR
OF8
=-88
Eyes open
Glanced left

N. T. HAI ET AL.
291
Figure 13. A user is controlling the wheelchair.
Figure 14. The directions of the wheelchair motion.
5. Acknowledgements
We would like to thank Vietnam National University in
Ho Chi Minh City for supporting research grant No.
C2013-28-06. Furthermore research was partly supported
by a research fund from International University in Ho
Chi Minh City. Finally, an honorable mention goes to our
volunteers and colleagues for supports on us in complet-
ing this project.
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