I
nte
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t
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l J
o
urna
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f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
15
,
No
.
1
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Ma
r
ch
20
26
,
p
p
.
55
~
64
I
SS
N:
2252
-
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8
1
4
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DOI
:
1
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1
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v15.
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K
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B
alin
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cr
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n
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B
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ain
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co
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p
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Dif
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p
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Featu
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ex
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T
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CC B
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li
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C
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s
p
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I
Ma
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Ag
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Mo
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s
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tec
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lo
g
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d
ev
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s
u
p
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ac
ti
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On
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ty
p
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ex
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f
th
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s
p
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r
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r
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p
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k
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co
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m
a
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s
[
1
]
.
Ver
b
al
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m
m
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s
ca
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co
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tr
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p
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[
2
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.
I
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m
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ev
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ca
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as
lo
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s
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m
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tio
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co
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m
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im
p
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[
3
]
.
On
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p
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p
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s
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tio
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to
o
v
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latin
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p
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b
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in
ter
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B
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tech
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Ap
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Ma
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wh
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co
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[
4
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[
5
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[
6
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.
Var
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[
7
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[
8
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.
Am
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s
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its
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o
w
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d
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[
9
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ased
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s
tan
tial
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[
1
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[
1
1
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T
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d
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d
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m
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a
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-
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ap
p
l
i
ca
t
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o
n
s
[
1
2
]
.
M
e
a
n
w
h
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le
,
in
t
h
e
f
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q
u
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ty
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[
1
3
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[
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4
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.
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t
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[
1
5
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,
s
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n
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g
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h
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ex
tr
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m
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:
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tu
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Hjo
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t
h
p
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am
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s
,
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an
d
DW
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.
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u
ltima
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in
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h
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(
B
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d
a
taset
d
ev
elo
p
ed
in
p
r
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v
io
u
s
r
esear
ch
[
1
6
]
.
T
h
is
s
tu
d
y
n
o
t
o
n
ly
m
a
k
es
tech
n
i
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2.
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g
i
t
m
o
r
e
c
h
a
l
l
e
n
g
i
n
g
t
h
a
n
a
s
i
m
p
l
e
a
l
p
h
a
b
e
t
.
T
h
e
d
a
t
a
s
e
t
i
s
o
p
e
n
l
y
a
c
c
e
s
s
i
b
l
e
in
M
e
n
d
e
l
e
y
D
a
t
a
a
t
h
t
t
p
s
:
/
/
d
a
t
a
.
m
e
n
d
e
l
e
y
.
c
o
m
/
d
a
t
a
s
e
t
s
/
c
3
m
4
s
2
d
t
c
r
/
3
[
1
6
]
.
T
h
i
s
d
a
t
a
s
e
t
,
d
e
s
i
g
n
e
d
a
s
a
b
e
n
c
h
m
a
r
k
f
o
r
E
E
G
-
b
a
s
e
d
B
C
I
r
e
s
e
a
r
c
h
a
l
s
o
c
a
t
a
l
y
z
e
s
t
h
e
p
r
e
s
e
r
v
a
t
i
o
n
a
n
d
d
i
g
i
t
i
z
a
t
i
o
n
o
f
B
a
l
i
n
e
s
e
s
c
r
i
p
t
,
i
n
s
p
i
r
i
n
g
a
n
d
m
o
t
i
v
a
t
i
n
g
t
h
e
a
c
a
d
e
m
i
c
c
o
m
m
u
n
i
t
y
.
ii)
Stag
e
2
:
s
ig
n
al
s
eg
m
en
tatio
n
.
T
h
e
ac
q
u
ir
ed
E
E
G
s
ig
n
als
th
en
u
n
d
er
wen
t
a
s
eg
m
en
tatio
n
p
r
o
ce
s
s
to
s
ep
ar
ate
th
e
b
aselin
e
an
d
ex
p
er
im
en
tal
s
eg
m
en
ts
[
1
6
]
,
[
1
7
]
.
T
h
is
p
r
o
ce
s
s
aim
s
to
o
b
tain
b
aselin
e
an
d
tr
ial
s
ig
n
als.
T
h
e
b
aselin
e
s
ig
n
al
was
u
s
ed
to
r
ed
u
ce
th
e
tr
i
al
s
ig
n
al
an
d
was
o
b
tain
ed
f
r
o
m
th
e
f
ir
s
t
3
s
ec
o
n
d
s
o
f
th
e
ex
p
er
im
e
n
tal
s
ig
n
al
(
jo
in
ed
_
d
ata
)
,
wh
ile
th
e
tr
ial
s
ig
n
al
is
o
b
tain
ed
f
r
o
m
th
e
en
tir
e
ex
p
er
im
en
tal
s
ig
n
al
(
j
o
in
ed
_
d
ata)
[
1
8
]
.
iii)
Stag
e
3
:
ar
tifa
ct
r
em
o
v
al
.
Ar
tifa
ct
r
em
o
v
al
was
p
e
r
f
o
r
m
e
d
u
s
in
g
th
e
m
o
d
if
ied
weig
h
t
ed
m
ea
n
f
ilter
(
MWMF)
,
w
h
ich
is
d
esig
n
ed
to
r
em
o
v
e
b
aselin
e
E
E
G
in
ter
f
er
en
ce
.
T
h
e
m
at
h
em
atica
l
eq
u
atio
n
f
o
r
th
e
MWMF m
eth
o
d
is
s
h
o
wn
in
(
1
)
[
1
9
]
.
=
(
∑
+
+
=
−
(
2
+
1
)
∑
+
=
−
)
(
1
)
W
h
er
e
n
is
th
e
w
in
d
o
w
len
g
th
,
an
d
m
is
th
e
n
u
m
b
er
o
f
d
ata
p
o
in
ts
.
T
h
e
v
alu
e
o
f
j
=
,
+
1
,
+
2
,
…
,
+
2
,
is
th
e
b
ase
lin
e
s
ig
n
al
am
p
l
itu
d
e
af
ter
r
ed
u
ctio
n
,
is
th
e
b
aselin
e
s
ig
n
al
am
p
litu
d
e
b
ef
o
r
e
r
ed
u
ctio
n
,
an
d
is
th
e
weig
h
t v
alu
e.
iv
)
Stag
e
4
:
s
ig
n
al
d
ec
o
m
p
o
s
itio
n
.
T
h
e
d
ec
o
m
p
o
s
itio
n
s
tag
e
is
p
er
f
o
r
m
ed
b
y
ap
p
ly
in
g
a
f
o
u
r
th
-
o
r
d
er
B
u
tter
wo
r
th
b
an
d
p
ass
f
ilter
t
o
s
ep
ar
ate
th
e
E
E
G
s
ig
n
al
in
t
o
th
eta,
al
p
h
a,
b
eta,
a
n
d
g
am
m
a
f
r
eq
u
en
c
y
b
an
d
s
[
1
9
]
.
v)
Stag
e
5
:
f
ea
tu
r
e
ex
tr
ac
tio
n
.
Featu
r
e
ex
tr
ac
tio
n
is
p
er
f
o
r
m
e
d
to
o
b
tain
n
u
m
er
ical
r
ep
r
esen
tatio
n
s
o
f
th
e
E
E
G
s
ig
n
al
th
at
ar
e
r
elev
an
t
to
th
e
task
at
h
an
d
.
I
n
th
is
s
tag
e,
f
iv
e
f
ea
t
u
r
e
e
x
tr
ac
tio
n
m
eth
o
d
s
ar
e
co
m
p
ar
ed
:
s
tatis
tical
f
ea
tu
r
es,
Hjo
r
th
p
ar
am
eter
s
,
PS
D,
DE
,
an
d
DW
T
.
T
h
e
b
est
m
eth
o
d
is
s
elec
ted
b
ased
o
n
its
ac
cu
r
ac
y
in
r
ec
o
g
n
izin
g
im
ag
in
a
r
y
s
p
ee
ch
p
atte
r
n
s
.
–
Statis
t
ical
f
ea
tu
r
es a
r
e
ca
lcu
lated
f
r
o
m
th
e
E
E
G
s
ig
n
al
x
[
n
]
o
v
er
N
s
am
p
les:
Me
an
:
μ
=
1
∑
[
]
=
1
(
2
)
Var
ian
ce
:
σ
2
=
1
∑
(
[
]
=
1
−
μ
)
2
(
3
)
Stan
d
ar
d
d
e
v
iatio
n
:
σ
=
√
σ
2
(
4
)
Sk
ewn
ess
:
γ1
=
1
∑
(
[
]
=
1
−
μ
)
3
σ
3
(
5
)
Ku
r
to
s
is
:
κ
=
1
∑
(
[
]
=
1
−
μ
)
4
σ
4
(
6
)
T
h
e
s
tatis
tical
m
eth
o
d
g
en
er
at
es 2
0
f
ea
tu
r
es (
4
b
an
d
s
×5
s
tatis
tical
m
eth
o
d
s
)
f
o
r
ea
c
h
ch
an
n
el.
–
Hjo
r
th
p
ar
am
eter
s
C
alcu
lated
b
ased
o
n
th
e
f
ir
s
t a
n
d
s
ec
o
n
d
d
er
iv
ativ
es o
f
th
e
E
E
G
s
ig
n
al
as in
(
7
)
-
(
9
)
.
=
2
(
7
)
=
√
′
2
2
(
8
)
W
h
er
e
x
′ is
th
e
f
ir
s
t d
er
iv
ativ
e
o
f
x
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
c
h
2
0
2
6
:
55
-
64
58
=
(
′
)
(
)
(
9
)
T
h
e
Hjo
r
th
p
ar
a
m
eter
m
eth
o
d
g
en
er
ates
1
2
f
ea
tu
r
es
(
4
b
an
d
s
×3
Hjo
r
th
p
ar
am
eter
s
m
eth
o
d
s
)
f
o
r
ea
ch
ch
an
n
el
.
–
Po
wer
s
p
ec
tr
al
d
en
s
ity
Usi
n
g
th
e
W
elch
m
eth
o
d
as in
(
1
0
)
.
=
1
∑
|
(
[
]
.
[
]
)
|
2
=
1
(
1
0
)
W
h
er
e
L
is
th
e
n
u
m
b
er
o
f
s
e
g
m
en
ts
,
U
is
th
e
win
d
o
w
en
e
r
g
y
n
o
r
m
aliza
tio
n
f
ac
to
r
,
[
]
is
th
e
win
d
o
win
g
f
u
n
ctio
n
,
[
]
is
th
e
ith
s
eg
m
en
t o
f
t
h
e
E
E
G
s
ig
n
al
.
T
h
e
PS
D
m
eth
o
d
g
e
n
er
ates f
o
u
r
f
ea
tu
r
es (
4
b
a
n
d
s
×1
PS
D
m
eth
o
d
)
p
er
ch
a
n
n
el.
–
Dif
f
er
en
tial e
n
tr
o
p
y
Fo
r
a
Gau
s
s
ian
s
ig
n
al
with
v
ar
ian
ce
σ
2
as in
(
1
1
)
.
=
1
2
(
2
2
)
(
1
1
)
T
h
e
DE
m
eth
o
d
g
e
n
er
ates f
o
u
r
f
ea
tu
r
es (
4
b
an
d
s
×1
DE
m
et
h
o
d
)
f
o
r
ea
c
h
ch
a
n
n
el.
–
Dis
cr
ete
wav
elet
tr
an
s
f
o
r
m
s
T
h
e
p
r
o
ce
s
s
o
f
d
ec
o
m
p
o
s
in
g
t
h
e
E
E
G
s
ig
n
al
[
]
in
to
d
etail
co
ef
f
icien
ts
an
d
ap
p
r
o
x
im
atio
n
at
lev
el
j
.
Dec
o
m
p
o
s
itio
n
:
[
]
=
∑
[
]
⋅
(
2
−
)
(
1
2
)
[
]
=
∑
[
]
⋅
ℎ
(
2
−
)
(
1
3
)
wh
er
e∙
[
]
is
th
e
lo
w
-
p
ass
wav
elet
f
ilter
an
d
∙
ℎ
[
]
is
th
e
h
ig
h
-
p
ass
wav
elet
f
ilter
.
–
B
an
d
en
er
g
y
f
ea
tu
r
e
ex
tr
ac
tio
n
:
=
∑
|
[
k
]
|
2
(
1
4
)
I
n
th
is
s
tu
d
y
,
th
e
d
b
4
wav
elet
was
u
s
ed
,
r
esu
ltin
g
in
1
6
f
ea
tu
r
es
(
4
b
an
d
s
×4
DW
T
m
eth
o
d
s
)
f
o
r
ea
ch
ch
an
n
el
[
2
0
]
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
wo
r
k
f
lo
w
o
f
th
e
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
,
in
clu
d
in
g
s
tatis
t
ical,
Hjo
r
th
p
ar
am
eter
,
an
d
DW
T
.
Af
ter
s
eg
m
e
n
tatio
n
an
d
ar
tifa
ct
r
e
m
o
v
al,
th
ese
th
r
ee
m
eth
o
d
s
ex
tr
ac
t
r
aw
E
E
G
s
ig
n
al
f
ea
tu
r
es
at
1
-
s
ec
o
n
d
i
n
ter
v
als
f
r
o
m
a
s
in
g
le
ch
an
n
el.
T
h
e
s
tati
s
tical
m
eth
o
d
p
r
o
d
u
ce
s
f
iv
e
f
ea
tu
r
es:
m
ea
n
,
v
ar
ian
ce
,
s
tan
d
a
r
d
d
e
v
iatio
n
,
s
k
ewn
ess
,
an
d
k
u
r
t
o
s
is
.
T
h
e
Hjo
r
th
p
ar
am
eter
m
eth
o
d
p
r
o
d
u
ce
s
th
r
ee
f
ea
tu
r
es:
ac
tiv
ity
,
m
o
b
ilit
y
,
an
d
co
m
p
lex
ity
.
T
h
e
DW
T
m
eth
o
d
d
ec
o
m
p
o
s
es
th
e
s
ig
n
al
i
n
to
f
o
u
r
s
u
b
b
a
n
d
s
—
ap
p
r
o
x
im
atio
n
3
(
A3
)
,
d
etail
3
(
D3
)
,
d
eta
il
2
(
D2
)
,
an
d
d
etail
1
(
D1
)
.
Fro
m
ea
c
h
s
u
b
b
an
d
,
f
o
u
r
f
ea
tu
r
es
ar
e
d
e
r
iv
ed
:
en
er
g
y
,
e
n
tr
o
p
y
,
m
ea
n
,
an
d
s
tan
d
ar
d
d
ev
iatio
n
.
T
h
u
s
,
th
e
DW
T
m
et
h
o
d
p
r
o
d
u
ce
s
1
6
f
ea
tu
r
es p
er
c
h
an
n
el
f
o
r
ea
ch
o
n
e
-
s
ec
o
n
d
s
e
g
m
en
t.
Fig
u
r
e
3
illu
s
tr
ates
th
e
f
r
am
e
wo
r
k
f
o
r
E
E
G
f
ea
tu
r
e
ex
t
r
ac
tio
n
u
s
in
g
th
e
DE
an
d
PS
D
m
et
h
o
d
s
.
Featu
r
e
ex
tr
ac
tio
n
is
p
er
f
o
r
m
ed
af
ter
s
eg
m
en
tatio
n
a
n
d
ar
tifa
ct
r
em
o
v
al,
f
o
llo
w
ed
b
y
s
ig
n
al
d
ec
o
m
p
o
s
itio
n
in
to
f
o
u
r
f
r
eq
u
en
cy
b
an
d
s
:
th
eta,
al
p
h
a,
b
e
ta,
an
d
g
am
m
a.
Fr
o
m
ea
c
h
f
r
eq
u
en
cy
b
a
n
d
,
b
o
th
e
x
tr
ac
tio
n
m
eth
o
d
s
ar
e
a
p
p
lied
to
o
b
tain
r
ep
r
esen
tativ
e
f
ea
tu
r
es.
As
a
r
esu
lt,
ea
ch
m
eth
o
d
y
ield
s
f
o
u
r
f
ea
tu
r
es p
er
ch
an
n
el
f
o
r
e
v
er
y
o
n
e
-
s
ec
o
n
d
E
E
G
s
eg
m
en
t.
v
i)
Stag
e
6
:
b
aselin
e
r
ed
u
ctio
n
.
B
aselin
e
r
ed
u
ctio
n
was
p
er
f
o
r
m
ed
to
g
en
er
ate
f
ea
t
u
r
e
v
al
u
es
f
r
o
m
th
e
ex
p
er
im
en
tal
s
ig
n
al
th
at
r
ef
lec
t
th
e
p
a
r
ticip
an
t'
s
co
g
n
itiv
e
o
r
em
o
tio
n
al
r
esp
o
n
s
es.
T
h
is
p
r
o
ce
s
s
u
s
es
th
e
r
elativ
e
d
if
f
er
e
n
ce
ap
p
r
o
ac
h
a
s
d
escr
ib
ed
in
th
e
r
elate
d
liter
a
tu
r
e
[
2
1
]
,
[
2
2
]
.
v
ii)
Stag
e
7
:
class
if
icatio
n
.
T
o
ev
alu
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
,
t
wo
co
m
m
o
n
ly
u
s
ed
class
if
icatio
n
alg
o
r
ith
m
s
f
o
r
E
E
G
s
ig
n
al
a
n
aly
s
is
wer
e
em
p
lo
y
ed
:
th
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
an
d
th
e
a
r
tific
ial
n
e
u
r
al
n
etwo
r
k
(
ANN)
.
T
h
ese
two
m
eth
o
d
s
wer
e
ch
o
s
en
b
ased
o
n
th
ei
r
f
r
eq
u
e
n
t u
s
e
in
p
r
ev
io
u
s
r
esear
ch
o
n
E
E
G
s
ig
n
al
class
if
icatio
n
[
1
0
]
,
[
1
2
]
.
v
iii)
Stag
e
8
:
ev
alu
atio
n
.
T
h
e
r
esu
ltin
g
m
o
d
el
was
ev
alu
ated
u
s
in
g
th
e
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
ati
o
n
tech
n
iq
u
e.
Per
f
o
r
m
an
ce
ev
alu
atio
n
was
p
er
f
o
r
m
ed
u
s
in
g
f
o
u
r
m
ain
m
etr
ics:
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
T
h
e
m
o
d
el
test
in
g
s
ch
em
e
is
s
h
o
wn
in
Fig
u
r
e
4
.
T
en
m
o
d
el
e
v
alu
atio
n
s
ce
n
ar
i
o
s
ar
e
p
r
o
p
o
s
ed
.
I
n
ea
ch
s
ce
n
ar
io
,
ev
er
y
f
ea
tu
r
e
e
x
tr
ac
tio
n
m
eth
o
d
is
ev
alu
ated
u
s
in
g
b
o
th
SVM
an
d
ANN
class
if
ier
s
.
T
h
ese
ev
alu
atio
n
s
p
r
o
d
u
ce
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
v
alu
es f
o
r
th
e
s
ix
cla
s
s
es o
f
B
alin
ese
s
cr
ip
t c
h
ar
ac
ter
s
b
ein
g
r
ec
o
g
n
ized
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
P
erfo
r
ma
n
ce
co
mp
a
r
is
o
n
o
f fe
a
tu
r
e
ex
tr
a
ctio
n
meth
o
d
s
fo
r
…
(
I
Ma
d
e
A
g
u
s
Wir
a
w
a
n
)
59
Fig
u
r
e
2
.
W
o
r
k
f
lo
w
o
f
th
e
f
ea
t
u
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
,
in
clu
d
in
g
s
tatis
tical,
Hjo
r
th
p
ar
am
eter
,
an
d
DW
T
Fig
u
r
e
3
.
W
o
r
k
f
lo
w
o
f
th
e
f
ea
t
u
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
,
in
clu
d
in
g
DE
an
d
PS
D
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
c
h
2
0
2
6
:
55
-
64
60
Fig
u
r
e
4
.
T
h
e
m
o
d
el
ev
alu
atio
n
s
ch
em
e
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
C
las
s
if
icatio
n
p
er
f
o
r
m
an
ce
wa
s
ev
alu
ated
b
y
co
m
p
ar
in
g
f
iv
e
f
ea
tu
r
e
ty
p
es:
DE
,
DW
T
,
H
jo
r
th
,
PS
D,
an
d
s
tatis
tic
s
.
T
h
e
clas
s
if
icati
o
n
p
r
o
ce
s
s
was
ap
p
lied
to
two
d
if
f
er
en
t
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els:
an
ANN
an
d
an
SVM.
Fig
u
r
e
5
p
r
esen
ts
th
e
class
if
icatio
n
ac
cu
r
ac
y
o
f
th
e
test
ed
f
ea
tu
r
es
u
s
in
g
a
n
AN
N.
I
n
g
en
er
al,
th
e
DE
f
ea
tu
r
e
y
ield
s
th
e
h
ig
h
est
ac
cu
r
ac
y
am
o
n
g
t
h
e
o
th
e
r
f
ea
t
u
r
es.
Fig
u
r
e
5
.
Vis
u
aliza
tio
n
o
f
th
e
ac
cu
r
ac
y
co
m
p
ar
is
o
n
o
f
f
i
v
e
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
co
m
b
in
ed
with
th
e
ANN
m
eth
o
d
Fo
r
s
ev
er
al
s
u
b
jects,
s
u
ch
as
P0
6
,
P1
3
,
P2
3
,
P2
4
,
P2
8
,
P2
9
,
an
d
P3
1
,
t
h
e
ac
cu
r
ac
y
e
v
e
n
ex
ce
ed
e
d
9
0
%,
in
d
icatin
g
th
at
DE
ca
n
ca
p
tu
r
e
t
h
e
ch
ar
ac
te
r
is
tics
o
f
E
E
G
s
ig
n
als
in
a
m
o
r
e
r
ep
r
esen
tativ
e
m
an
n
er
.
Me
an
wh
ile,
o
th
er
f
ea
tu
r
es
s
u
ch
as
DW
T
,
Hjo
r
th
,
PS
D,
an
d
s
tatis
tical
f
ea
tu
r
es
sh
o
w
r
elativ
ely
lo
wer
p
er
f
o
r
m
an
ce
,
with
an
av
er
a
g
e
ac
cu
r
ac
y
u
n
d
er
3
5
%.
T
h
is
co
n
s
id
er
ab
le
p
er
f
o
r
m
an
ce
g
ap
s
u
g
g
ests
th
at
ANN
i
s
m
o
r
e
ef
f
e
ctiv
e
at
o
p
tim
izin
g
t
h
e
n
o
n
lin
ea
r
f
ea
tu
r
e
r
e
p
r
esen
t
atio
n
s
o
f
DE
.
I
n
co
n
tr
ast,
th
e
o
th
er
f
ea
t
u
r
es
ar
e
less
ca
p
ab
le
o
f
p
r
o
v
id
in
g
s
u
f
f
i
cien
tly
d
is
cr
im
in
ativ
e
in
f
o
r
m
a
tio
n
f
o
r
th
e
m
o
d
el.
Fig
u
r
e
6
illu
s
tr
ates th
e
class
if
i
ca
tio
n
p
er
f
o
r
m
an
ce
u
s
in
g
th
e
SVM
m
o
d
el.
Similar
to
th
e
ANN
r
esu
lts
,
th
e
DE
f
ea
tu
r
e
co
n
s
is
ten
tly
ac
h
iev
ed
th
e
h
ig
h
est
ac
cu
r
ac
y
,
with
av
er
ag
e
v
alu
es
b
etwe
en
4
5
%
an
d
6
5
%.
T
h
is
in
d
icate
s
th
at
alth
o
u
g
h
SVM
ca
n
lev
er
a
g
e
DE
as
an
in
f
o
r
m
ativ
e
f
ea
tu
r
e
,
its
g
e
n
er
aliza
tio
n
ca
p
a
b
ilit
y
r
em
ai
n
s
lim
ited
in
m
o
d
el
in
g
th
e
co
m
p
lex
ity
o
f
E
E
G
s
ig
n
al
p
atter
n
s
.
T
h
e
f
in
d
i
n
g
s
co
n
f
ir
m
th
at
D
E
s
u
r
p
ass
es
DW
T
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
P
erfo
r
ma
n
ce
co
mp
a
r
is
o
n
o
f fe
a
tu
r
e
ex
tr
a
ctio
n
meth
o
d
s
fo
r
…
(
I
Ma
d
e
A
g
u
s
Wir
a
w
a
n
)
61
Hjo
r
th
p
ar
am
eter
s
,
PS
D,
an
d
s
tatis
t
i
ca
l
f
ea
tu
r
es
in
cla
s
s
if
y
i
n
g
s
ix
B
alin
ese
s
cr
ip
t
clas
s
es
b
ased
o
n
E
E
G
d
ata.
B
y
p
r
o
v
i
d
in
g
a
m
o
r
e
in
f
o
r
m
ativ
e
r
ep
r
esen
tatio
n
o
f
th
e
p
r
o
b
ab
ilis
tic
s
tr
u
ctu
r
e
o
f
E
E
G
ac
tiv
ity
,
DE
p
r
o
v
es
m
o
r
e
e
f
f
ec
tiv
e
i
n
ca
p
t
u
r
in
g
n
o
n
-
lin
ea
r
an
d
n
o
n
-
s
tatio
n
ar
y
b
r
ain
d
y
n
am
ics
[
2
3
]
.
T
h
e
co
n
s
is
ten
t
im
p
r
o
v
em
en
t
in
ac
cu
r
ac
y
f
u
r
th
er
v
alid
ates
p
r
ev
io
u
s
r
esear
ch
,
em
p
h
asizin
g
th
at
DE
i
s
p
ar
ticu
lar
ly
well
-
s
u
ited
f
o
r
m
o
d
elin
g
co
m
p
lex
E
E
G
p
atter
n
s
,
esp
ec
i
ally
wh
en
in
teg
r
ated
with
d
ee
p
lear
n
in
g
m
o
d
els
[
1
3
]
.
T
h
u
s
,
DE
ca
n
b
e
r
eg
ar
d
e
d
as
th
e
p
r
im
ar
y
f
ea
tu
r
e
e
x
tr
ac
tio
n
a
p
p
r
o
ac
h
,
wh
ile
o
th
er
m
et
h
o
d
s
m
a
y
s
er
v
e
as
c
o
m
p
lem
e
n
tar
y
tec
h
n
iq
u
es
in
h
y
b
r
id
f
r
a
m
ewo
r
k
s
t
o
im
p
r
o
v
e
r
o
b
u
s
tn
ess
.
T
h
ese
r
esu
lts
alig
n
with
p
r
i
o
r
s
tu
d
ies
r
ep
o
r
tin
g
t
h
e
s
u
p
e
r
io
r
d
is
cr
im
in
ativ
e
p
o
wer
o
f
DE
c
o
m
p
ar
ed
to
co
n
v
en
ti
o
n
al
m
et
h
o
d
s
[
1
0
]
,
[
2
4
]
,
[
2
5
]
.
Fu
r
th
er
m
o
r
e,
ex
p
e
r
im
en
tal
o
u
tco
m
es
s
h
o
w
th
at
DE
co
n
s
is
ten
tly
y
ield
s
th
e
h
ig
h
est
ac
cu
r
ac
y
ac
r
o
s
s
b
o
th
ANN
an
d
SVM
class
if
ier
s
.
Alth
o
u
g
h
o
v
er
all
ac
c
u
r
ac
y
r
an
g
ed
b
etwe
en
4
5
%
an
d
6
5
%,
A
NN
d
em
o
n
s
tr
ated
b
etter
p
er
f
o
r
m
an
ce
th
a
n
SVM,
s
u
g
g
esti
n
g
its
g
r
ea
ter
ef
f
ec
tiv
e
n
ess
in
ca
p
tu
r
in
g
t
h
e
n
o
n
-
lin
ea
r
ch
ar
ac
ter
is
tics
o
f
E
E
G
s
ig
n
al
s
.
Fig
u
r
e
6
.
Vis
u
aliza
tio
n
o
f
th
e
ac
cu
r
ac
y
co
m
p
ar
is
o
n
o
f
f
i
v
e
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
co
m
b
in
ed
with
th
e
SVM
m
eth
o
d
Desp
ite
its
ad
v
an
tag
es,
class
if
icatio
n
p
er
f
o
r
m
an
ce
s
till
v
ar
ies
ac
r
o
s
s
p
ar
ticip
an
ts
.
Ad
d
r
ess
in
g
th
is
ch
allen
g
e
r
eq
u
ir
es
f
u
tu
r
e
s
tu
d
ies
to
em
p
h
asize
two
k
ey
d
ir
ec
tio
n
s
.
First,
d
ata
au
g
m
en
tatio
n
is
es
s
en
tial,
as
th
e
B
I
SE
d
ataset
p
r
o
v
i
d
e
s
a
lim
ited
n
u
m
b
er
o
f
s
am
p
l
es
p
er
s
u
b
ject.
Alth
o
u
g
h
th
e
d
ataset
m
ee
ts
th
e
m
in
im
u
m
th
r
esh
o
ld
f
o
r
s
am
p
l
e
s
ize,
tech
n
iq
u
es
s
u
ch
as
r
ad
iu
s
-
s
y
n
th
etic
m
in
o
r
ity
o
v
er
-
s
am
p
lin
g
tech
n
iq
u
e
(
R
ad
iu
s
-
SMOT
E
)
,
ad
ap
tiv
e
s
y
n
th
etic
s
am
p
lin
g
(
ADASYN
)
,
an
d
g
e
n
er
ativ
e
m
o
d
el
s
(
e.
g
.
,
g
e
n
er
ativ
e
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Evaluation Warning : The document was created with Spire.PDF for Python.