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ntr
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1
9
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.
1
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Feb
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o
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n
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e
p
h
a
lo
g
ra
p
h
y
(
E
EG
)
m
o
to
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tas
k
s.
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ifac
ts
su
c
h
a
s
e
y
e
a
n
d
m
u
sc
le
m
o
v
e
m
e
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e
t
h
e
c
las
sifica
ti
o
n
p
e
r
fo
rm
a
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e
.
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a
n
y
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d
ies
try
to
e
x
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t
n
o
t
r
e
d
u
n
d
a
n
t
a
n
d
d
isc
rimin
a
ti
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e
fe
a
tu
re
s
fro
m
EE
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sig
n
a
ls.
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e
re
fo
re
,
th
is
st
u
d
y
p
r
o
p
o
se
d
a
sig
n
a
l
p
re
p
r
o
c
e
s
sin
g
a
n
d
fe
a
tu
re
e
x
trac
ti
o
n
m
e
th
o
d
fo
r
EE
G
c
las
sifica
ti
o
n
.
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c
o
n
sists
o
f
re
m
o
v
in
g
t
h
e
a
rti
fa
c
ts
b
y
u
sin
g
d
isc
re
te
fo
u
r
ier
tran
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o
rm
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)
a
s
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n
id
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ter
f
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q
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ies
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l
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tes
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h
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n
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ls
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h
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m
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se
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a
ls.
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e
n
th
e
re
su
lt
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n
t
fro
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ro
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rimin
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ro
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las
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f
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t.
K
ey
w
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d
s
:
B
r
ain
c
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m
p
u
ter
i
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ter
f
ac
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Dis
cr
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f
o
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tr
an
s
f
o
r
m
E
lectr
o
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ce
p
h
al
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r
am
Gen
etic
alg
o
r
ith
m
Su
p
p
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t
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to
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m
ac
h
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rticle
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th
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CC B
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SA
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C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Haid
er
T
h
.
Salim
Al
r
ik
ab
i
Dep
ar
tm
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
W
asit
Un
iv
er
s
ity
,
I
r
aq
E
m
ail:
h
d
h
iy
ab
@
u
o
wasit.ed
u
.
iq
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
m
o
s
t
co
m
p
lex
o
r
g
an
in
t
h
e
h
u
m
a
n
b
o
d
y
is
th
e
h
u
m
an
b
r
ain
.
T
h
e
b
asic
u
n
its
o
f
th
e
b
r
ain
ce
lls
ca
lled
n
eu
r
o
n
s
,
wh
ich
is
co
n
s
id
er
ed
th
e
ce
n
ter
o
f
th
e
h
u
m
an
n
er
v
o
u
s
s
y
s
tem
an
d
co
n
tr
o
ls
d
if
f
er
en
t o
r
g
a
n
s
an
d
f
u
n
ctio
n
s
.
Neu
r
o
n
s
s
en
d
elec
tr
ical
s
ig
n
als
to
co
n
tr
o
l
th
e
h
u
m
an
b
o
d
y
an
d
ca
n
b
e
m
ea
s
u
r
ed
u
s
in
g
elec
tr
o
en
ce
p
h
al
o
g
r
ap
h
y
(
E
E
G
)
,
wh
ich
m
ea
s
u
r
es
th
e
elec
tr
ical
ac
tiv
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o
f
th
e
b
r
ain
b
y
r
ec
o
r
d
in
g
it
v
ia
elec
tr
o
d
es
p
lace
d
eith
er
o
n
th
e
co
r
tex
o
r
th
e
s
ca
lp
.
T
h
e
s
ig
n
al
g
en
er
ated
b
y
th
is
elec
tr
ical
ac
tiv
ity
i
s
n
o
n
-
s
tatio
n
ar
y
an
d
co
m
p
lex
r
an
d
o
m
s
ig
n
als
[
1
,
2
]
.
T
h
e
EEG
s
ig
n
al
c
o
n
tain
s
a
lo
t
o
f
in
f
o
r
m
atio
n
ab
o
u
t
th
e
h
u
m
an
b
r
ain
f
u
n
ctio
n
s
,
s
o
th
e
E
E
G
a
n
aly
s
is
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d
in
f
o
r
m
atio
n
ex
tr
ac
tio
n
ar
e
v
er
y
co
m
p
licated
.
Sin
ce
th
e
E
E
G
s
ig
n
al
co
n
s
is
ts
o
f
th
e
v
er
y
lo
w
-
f
r
eq
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e
n
cy
co
m
p
o
n
e
n
ts
,
s
o
it
is
co
r
r
u
p
ted
with
d
if
f
e
r
en
t
ty
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es
o
f
ar
tifa
cts
(
n
o
is
es
an
d
p
o
wer
lin
e
f
r
eq
u
e
n
cies)
[3
-
5]
.
I
n
r
ec
en
t
y
ea
r
s
,
th
e
am
o
u
n
t
o
f
r
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ch
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d
ef
f
o
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ts
h
av
e
b
e
en
d
ir
ec
ted
to
war
d
s
th
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id
en
tif
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u
tili
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tio
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f
th
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in
f
o
r
m
atio
n
f
r
o
m
th
e
h
u
m
a
n
E
E
G
s
ig
n
al.
M
o
s
t
o
f
th
e
wo
r
k
in
b
r
ain
c
o
m
p
u
ter
in
ter
f
ac
e
(
BCI
)
liter
atu
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e
o
n
m
o
to
r
im
ag
er
y
h
as
b
ee
n
to
war
d
s
class
if
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g
m
o
v
em
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o
f
th
e
h
a
n
d
,
f
o
o
t,
an
d
to
n
g
u
e.
T
h
ese
m
o
v
em
en
ts
ar
e
lar
g
e
an
d
to
p
o
g
r
ap
h
ically
d
if
f
er
e
n
t c
o
r
r
esp
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n
d
in
g
t
o
th
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b
r
ain
ar
ea
s
.
Far
id
Gh
a
n
i,
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a
l.,
class
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d
if
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t
t
y
p
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o
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G
d
ata
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v
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n
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.
T
h
ey
u
s
ed
d
is
cr
ete
co
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in
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tr
an
s
f
o
r
m
(
DC
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an
d
in
d
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p
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co
m
p
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n
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an
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(
I
C
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to
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th
e
n
u
m
b
er
o
f
th
e
ex
tr
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ted
f
ea
tu
r
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
1
6
9
3
-
6
9
3
0
T
E
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KOM
NI
KA
T
elec
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m
m
u
n
C
o
m
p
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t E
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n
tr
o
l
,
Vo
l.
1
9
,
No
.
1
,
Feb
r
u
ar
y
202
1
:
28
5
-
29
2
286
an
d
to
im
p
r
o
v
e
th
e
ac
c
u
r
ac
y
o
f
class
if
icatio
n
[
6
]
.
T
h
er
e
ar
e
m
u
l
tip
le
s
tu
d
ies
r
elate
d
to
cla
s
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y
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g
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E
Gs
in
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o
m
e
ca
teg
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tin
g
n
o
r
m
al,
in
ter
ictal,
an
d
ep
ile
p
tic
s
ig
n
als
[
7
]
.
Mo
h
am
m
ad
H.
Alo
m
ar
i,
et
a
l.,
o
b
tain
ed
p
r
etty
g
o
o
d
class
if
icatio
n
r
esu
lts
u
s
in
g
n
eu
r
al
n
etwo
r
k
s
(
NNs)
an
d
s
u
p
p
o
r
t v
ec
t
o
r
m
ac
h
in
e
(
SVM)
to
d
is
cr
im
in
ate
b
etwe
en
E
E
G
r
ig
h
t
an
d
lef
t
-
h
an
d
m
o
v
em
en
t
af
t
er
ap
p
ly
in
g
b
a
n
d
p
ass
f
ilter
(
B
PF
)
with
a
s
p
ec
if
ic
s
et
o
f
s
tatis
tical
f
ea
tu
r
es (
m
ea
n
,
p
o
wer
an
d
e
n
er
g
y
)
[
8
]
.
R
.
Z
ar
ei,
et
a
l.,
p
r
o
p
o
s
ed
a
m
eth
o
d
to
r
e
m
o
v
e
th
e
a
r
tifa
cts
f
r
o
m
E
E
G
d
ata
b
ased
o
n
Prin
cip
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
an
d
th
e
cr
o
s
s
-
co
v
ar
ian
ce
tec
h
n
i
q
u
e
(
C
C
OV)
f
o
r
th
e
ex
tr
ac
tio
n
o
f
d
is
cr
im
in
ato
r
y
m
en
tal
in
f
o
r
m
atio
n
s
tates
f
r
o
m
E
E
G
s
ig
n
als
in
B
C
I
ap
p
lic
atio
n
s
[
9
]
.
Sh
ak
s
h
i,
et
a
l.,
r
e
m
o
v
ed
th
e
u
n
wan
ted
f
r
eq
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e
n
cy
co
m
p
o
n
e
n
ts
f
r
o
m
t
h
e
o
r
ig
in
al
s
ig
n
al
b
y
u
s
in
g
d
i
f
f
er
en
t
ty
p
es
o
f
f
ilter
s
.
Me
an
,
s
k
ewn
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,
s
tan
d
ar
d
d
ev
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n
,
a
n
d
v
ar
ian
ce
ar
e
u
s
ed
to
e
x
tr
ac
t
f
ea
tu
r
es
f
r
o
m
th
e
E
E
G
s
ig
n
al.
T
h
e
in
f
o
r
m
atio
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a
b
o
u
t
th
e
s
ig
n
al
was
d
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m
in
ed
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d
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er
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t
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t
DSP
to
o
ls
lik
e
d
is
cr
ete
f
o
u
r
ier
tr
an
s
f
o
r
m
(
DFT
)
,
f
ast
f
o
u
r
ier
tr
an
s
f
o
r
m
(
FFT
)
,
s
h
o
r
t
-
tim
e
f
o
u
r
ier
tr
an
s
f
o
r
m
(
STFT
)
,
a
n
d
w
av
elet
tr
an
s
f
o
r
m
[
7
]
.
Fro
m
th
e
f
o
r
eg
o
in
g
,
it b
ec
o
m
e
s
clea
r
th
at
f
ea
tu
r
e
ex
tr
ac
tio
n
p
lay
s
an
im
p
o
r
tan
t a
n
d
i
n
f
lu
en
tial r
o
le
to
h
elp
th
e
class
if
ier
f
o
r
d
is
tin
g
u
is
h
in
g
b
etwe
en
E
E
G
s
ig
n
al
class
es.
T
h
er
ef
o
r
e,
th
e
m
ain
g
o
al
o
f
th
is
s
tu
d
y
is
to
f
in
d
th
e
m
o
s
t
r
elate
d
f
ea
tu
r
es
th
at
d
is
cr
im
in
ate
E
E
G
r
ea
l
f
in
g
er
m
o
v
em
e
n
t
s
ig
n
al
an
d
u
s
es
th
e
SVM
class
if
ier
o
n
ly
as
a
to
o
l
to
d
is
tin
g
u
is
h
th
e
E
E
G
s
ig
n
als
b
ased
o
n
th
e
ex
tr
ac
te
d
f
ea
tu
r
es.
T
h
e
g
e
n
etic
alg
o
r
ith
m
was
em
p
lo
y
ed
to
f
in
d
th
e
m
o
s
t
r
elev
an
t
f
r
eq
u
e
n
cies
wh
ich
ar
e
u
s
ed
as
cu
t
o
f
f
f
r
e
q
u
en
c
y
o
f
id
ea
l
f
ilter
b
ased
o
n
DFT
.
Fin
d
in
g
th
ese
f
r
eq
u
en
cies
im
p
r
o
v
es
th
e
class
if
icatio
n
p
er
f
o
r
m
an
ce
in
ter
m
s
o
f
b
o
th
ac
cu
r
ac
y
an
d
c
o
m
p
u
tatio
n
al
tim
e.
T
h
e
o
r
g
a
n
izatio
n
o
f
th
is
ar
ticle
is
: sectio
n
2
will
d
escr
ib
e
th
e
m
ain
m
ater
ials
u
s
ed
in
t
h
is
wo
r
k
.
Sectio
n
3
d
em
o
n
s
tr
ates
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
Sectio
n
4
lis
ts
an
d
ex
p
lain
s
th
e
class
if
icatio
n
p
er
f
o
r
m
a
n
ce
.
T
h
e
last
s
ec
t
io
n
will d
is
cu
s
s
an
d
ex
p
lain
th
e
ef
f
ec
ts
o
f
ea
ch
s
tag
e
in
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
.
2.
M
AT
E
R
I
AL
S
AND
M
E
T
H
O
DO
L
O
G
Y
T
h
is
s
ec
tio
n
co
v
er
s
th
e
p
r
o
ce
d
u
r
e
u
s
ed
f
o
r
s
o
lv
i
n
g
th
e
p
r
o
b
lem
r
elate
d
to
f
in
d
th
e
d
is
cr
im
in
ativ
e
f
r
eq
u
e
n
cies
o
f
th
e
E
E
G
s
ig
n
al
.
Hen
ce
,
it
d
escr
ib
es
th
e
p
r
o
p
o
s
ed
m
eth
o
d
an
d
th
e
to
o
ls
u
s
ed
in
th
is
ar
ticle
s
u
ch
as FFT,
an
d
cr
o
s
s
co
r
r
elatio
n
.
I
t a
ls
o
d
escr
ib
e
s
th
e
p
r
o
ce
d
u
r
e
to
ac
q
u
ir
e
t
h
e
E
E
G
s
ig
n
al.
2
.
1
.
P
r
o
po
s
ed
m
et
ho
d
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
r
o
b
u
s
t
s
ch
em
e
th
at
co
n
s
is
ts
o
f
f
iv
e
s
ta
g
es.
Fig
u
r
e
1
illu
s
tr
ates
th
e
b
lo
ck
d
iag
r
am
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
ese
f
iv
e
s
tag
es a
r
e:
−
Pre
p
r
o
ce
s
s
in
g
u
s
in
g
FF
T
:
th
i
s
s
tag
e
u
s
es
DFT
as
an
id
ea
l
f
ilter
to
f
ilter
th
e
m
o
s
t
d
is
cr
im
in
ativ
e
E
E
G
f
r
eq
u
e
n
cies.
T
h
e
m
o
s
t
d
is
cr
im
in
ativ
e
f
r
e
q
u
en
cies
a
r
e
d
eter
m
in
ed
b
y
u
s
in
g
g
en
etic
alg
o
r
ith
m
(
GA
)
.
T
h
e
n
,
th
e
E
E
G
s
ig
n
als ar
e
r
ec
o
n
s
tr
u
cted
u
s
in
g
d
is
cr
ete
f
o
u
r
ier
tr
a
n
s
f
o
r
m
(
I
DFT
)
.
−
C
r
o
s
s
co
r
r
elatio
n
o
f
t
h
e
ef
f
ec
tiv
e
ch
a
n
n
el
with
r
ig
h
t/lef
t
h
em
is
p
h
er
e:
T
h
e
b
r
ai
n
is
d
i
v
id
ed
in
to
2
h
alv
es,
o
r
h
em
is
p
h
er
es,
th
at
a
r
e
co
n
n
ec
t
ed
b
y
th
e
c
o
r
p
u
s
ca
llo
s
u
m
.
I
n
f
o
r
m
atio
n
f
r
o
m
b
o
th
h
em
is
p
h
er
es
n
ee
d
s
to
b
e
ef
f
icien
tly
in
teg
r
ated
;
p
lacin
g
elec
tr
o
d
es
(
E
E
G
ch
an
n
els)
o
n
th
e
s
ca
lp
ar
e
s
p
lit
in
to
two
g
r
o
u
p
s
as
th
e
r
ig
h
t/lef
t
h
em
is
p
h
e
r
e.
Dep
e
n
d
in
g
o
n
th
e
an
ato
m
ical
l
o
ca
tio
n
o
f
th
e
s
ig
n
al
g
en
e
r
ated
in
t
h
e
b
r
ai
n
o
r
th
e
ch
an
n
els clo
s
e
to
th
e
m
o
to
r
E
E
G
s
ig
n
al
r
eg
io
n
,
th
e
e
f
f
ec
tiv
e
ch
an
n
el
was selec
ted
s
o
,
th
e
r
i
g
h
t h
em
is
p
h
er
e
ch
an
n
els
ar
e
cr
o
s
s
co
r
r
elate
d
with
th
e
F4
c
h
an
n
el
an
d
th
e
lef
t
h
em
is
p
h
er
e
ch
a
n
n
els
with
F3
.
T
h
is
is
d
o
n
e
f
o
r
wh
o
le
t
r
ain
in
g
a
n
d
test
in
g
s
ets.
C
r
o
s
s
co
r
r
elatio
n
m
ak
es
a
m
o
r
e
v
is
ib
le
m
a
g
n
itu
d
e
d
if
f
er
en
ce
b
etwe
en
th
e
two
h
em
is
p
h
er
es.
−
E
E
G
f
ea
tu
r
e
e
x
tr
ac
tio
n
:
s
ig
n
if
ican
t
an
d
im
p
o
r
tan
t f
ea
tu
r
es
n
ee
d
to
b
e
ex
tr
ac
ted
f
r
o
m
th
e
E
E
G
r
aw
d
ata.
I
n
th
is
s
tu
d
y
,
ten
s
tatis
tical
f
ea
tu
r
es
ar
e
co
m
p
u
ted
f
r
o
m
t
h
e
E
E
G
d
ata
(
m
in
,
m
ax
,
m
ea
n
,
m
o
d
e,
m
ed
ian
,
s
td
,
r
an
g
e,
e
n
tr
o
p
y
,
1
s
t q
u
ar
tile,
an
d
3
r
d
q
u
ar
tile)
.
T
h
is
is
d
o
n
e
f
o
r
wh
o
le
tr
ain
in
g
an
d
test
in
g
s
ets.
−
No
r
m
aliza
tio
n
:
th
e
cu
r
r
en
t
s
tu
d
y
e
x
p
lo
r
es
th
e
a
p
p
licatio
n
o
f
n
o
r
m
alize
d
E
E
G
d
ata
to
d
etec
t
an
d
id
en
tif
y
th
e
p
atter
n
s
o
f
in
f
o
r
m
atio
n
f
lo
w
in
th
e
f
u
n
ctio
n
al
b
r
ai
n
n
etwo
r
k
s
.
I
t
m
ak
es
th
e
E
E
G
s
ig
n
al
lie
b
etwe
en
1
an
d
-
1
b
y
d
iv
id
in
g
ea
ch
c
h
an
n
el
b
y
th
e
m
ax
im
u
m
ab
s
o
lu
te
v
alu
e
o
f
th
e
s
am
e
c
h
an
n
el.
−
SVM
class
if
icatio
n
:
r
ad
ial
b
ase
Ker
n
el
f
u
n
ctio
n
with
a
u
to
k
er
n
el
s
ca
le
ar
e
th
e
co
n
f
ig
u
r
atio
n
o
f
th
e
SVM
class
if
ier
.
T
en
-
f
o
ld
cr
o
s
s
v
alid
atio
n
was u
s
ed
to
ev
alu
ate
t
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
class
if
ier
.
2
.
2
.
DF
T
R
ep
r
esen
tatio
n
o
f
th
e
d
ig
ital
s
ig
n
als
in
th
e
tim
e
d
o
m
ain
d
escr
ib
es
th
e
s
ig
n
al
am
p
litu
d
e
v
er
s
u
s
th
e
s
am
p
le
n
u
m
b
er
.
So
m
e
ap
p
licatio
n
s
,
s
ig
n
al
in
th
e
f
r
eq
u
en
cy
d
o
m
ain
co
n
tai
n
s
m
o
r
e
u
s
ef
u
l in
f
o
r
m
atio
n
th
an
th
e
s
ig
n
al
in
a
tim
e
d
o
m
ain
.
T
h
e
tr
an
s
f
o
r
m
atio
n
b
etwe
e
n
tim
e
-
d
o
m
ain
s
ig
n
al
s
am
p
les
a
n
d
f
r
eq
u
e
n
cy
d
o
m
ain
co
m
p
o
n
en
ts
v
ice
v
er
s
a
k
n
o
wn
as th
e
DFT
an
d
I
DFT
r
esp
ec
ti
v
ely
.
Fig
u
r
e
2
s
h
o
ws th
e
DFT
ap
p
licatio
n
.
I
n
ad
d
itio
n
,
th
e
DFT
is
wid
e
ly
u
s
ed
in
m
a
n
y
o
th
er
ar
ea
s
,
in
clu
d
in
g
s
p
ec
tr
al
an
aly
s
is
,
ac
o
u
s
tics
,
im
ag
in
g
/v
id
e
o
,
au
d
io
,
in
s
tr
u
m
en
tatio
n
,
an
d
c
o
m
m
u
n
icatio
n
s
s
y
s
tem
s
[
1
0
]
.
T
h
e
DFT
an
d
I
DFT
eq
u
atio
n
s
ar
e
r
esp
ec
tiv
ely
s
h
o
wn
b
el
o
w:
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
F
in
d
in
g
th
e
d
is
crimin
a
tive
fr
e
q
u
en
cies o
f
… (
S
h
a
ima
Miq
d
a
d
Mo
h
a
med
N
a
jeeb
)
287
(
)
=
∑
(
)
−
2
−
1
=
0
[
1
0
]
(
1
)
wh
er
e:
0
≤
n
≤
N
-
1
[
]
=
1
∑
[
]
2
−
1
=
0
[
1
0
]
(
2
)
Fig
u
r
e
1
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
Fig
u
r
e
2
.
DFT
ap
p
licatio
n
2
.
3
.
Cro
s
s
co
rr
ela
t
io
n
T
h
e
co
r
r
elatio
n
o
f
s
ig
n
als
is
a
s
ig
n
al
-
p
r
o
ce
s
s
in
g
tech
n
iq
u
e
o
f
ten
u
s
ed
f
o
r
m
ea
s
u
r
in
g
th
e
s
im
ilar
ity
b
etwe
en
two
s
ig
n
als
an
d
r
esu
l
ts
in
a
cr
o
s
s
-
co
r
r
elatio
n
s
eq
u
e
n
ce
.
B
asic
s
tati
s
tic
p
ar
am
eter
s
ca
n
b
e
tak
en
f
r
o
m
th
e
cr
o
s
s
-
co
r
r
elatio
n
s
eq
u
e
n
ce
as
f
ea
tu
r
es
o
f
a
s
ig
n
al
a
n
d
th
en
u
s
ed
in
class
if
icatio
n
.
C
o
r
r
elatio
n
is
also
u
s
ed
f
o
r
th
e
d
etec
tio
n
o
f
tar
g
ets
in
r
ad
ar
o
r
s
o
n
ar
s
ig
n
al.
T
h
e
s
am
p
le
o
f
cr
o
s
s
-
co
r
r
elatio
n
b
et
wee
n
two
s
ig
n
als
is
ca
lcu
lated
b
y
:
[
]
=
∑
[
]
[
−
]
−
|
|
−
1
=
0
[
1
1
]
(
3
)
wh
er
e
[
]
is
th
e
cr
o
s
s
-
co
r
r
elatio
n
at
m
lag
an
d
=
[
−
(
−
1
)
,
…
,
0
,
1
,
2
…
,
(
−
1
)
]
.
T
h
e
s
am
p
les
o
f
cr
o
s
s
co
r
r
elatio
n
f
o
r
two
s
eq
u
en
ce
s
h
as
2
N
-
1
s
am
p
le
len
g
th
,
ea
ch
o
f
th
e
s
ig
n
als,
x
a
n
d
y
,
co
n
s
is
ts
o
f
N
f
in
ite
n
u
m
b
er
o
f
s
am
p
les
[
1
1
]
.
2
.
4
.
E
E
G
f
ea
t
ure
ex
t
ra
ct
i
o
n
Featu
r
e
ex
tr
ac
tio
n
p
lay
s
an
im
p
o
r
tan
t
r
o
le
in
th
e
p
r
o
ce
s
s
o
f
class
if
y
in
g
E
E
G
s
ig
n
als.
A
tr
ain
in
g
p
r
o
ce
s
s
will
tak
e
p
lace
p
r
o
p
er
ly
if
f
ea
t
u
r
es
th
at
d
escr
ib
in
g
th
e
s
ig
n
al
ar
e
ex
tr
ac
ted
well
[
1
2
,
1
3
]
.
Ma
n
y
f
ea
tu
r
e
ex
t
r
ac
tio
n
alg
o
r
ith
m
s
ar
e
p
r
esen
ted
in
t
h
e
b
i
o
m
ed
ical
f
ield
,
th
e
s
im
p
lest
an
d
m
o
s
t
co
m
m
o
n
al
g
o
r
ith
m
t
h
at
wo
r
k
s
t
o
r
e
d
u
ce
th
e
am
o
u
n
t
o
f
d
ata
class
if
ied
f
o
r
E
E
G
s
ig
n
al
is
th
e
u
s
e
o
f
s
tatis
tical
ap
p
r
o
ac
h
es
s
u
ch
as
m
e
an
,
m
ed
ia
n
,
m
o
d
e,
an
d
s
tan
d
ar
d
d
ev
iatio
n
[
1
4
,
15]
.
2
.
5
.
Cla
s
s
if
ica
t
io
n
m
et
ho
d
On
e
o
f
th
e
m
o
s
t p
o
p
u
lar
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
is
SVM.
I
t is a
s
tatis
tical
lear
n
in
g
th
eo
r
y
b
ased
o
n
th
e
class
if
icatio
n
m
eth
o
d
[
1
6
,
17]
.
SVM
is
ap
p
lied
in
m
an
y
a
p
p
licatio
n
s
lik
e
E
E
G
s
ig
n
al
class
if
icatio
n
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
1
9
,
No
.
1
,
Feb
r
u
ar
y
202
1
:
28
5
-
29
2
288
ca
n
ce
r
id
en
tific
atio
n
,
b
i
o
in
f
o
r
m
atics,
s
eizu
r
e
p
r
ed
ictio
n
,
f
ac
e
r
ec
o
g
n
itio
n
,
a
n
d
s
p
ee
ch
d
is
o
r
d
er
.
T
h
e
p
r
in
cip
le
o
f
SVM
class
if
icatio
n
is
to
co
n
s
tr
u
ct
an
o
p
tim
al
h
y
p
er
p
la
n
e
a
s
th
e
d
ec
is
io
n
s
u
r
f
ac
e
to
s
ep
ar
a
te
th
e
tr
ain
in
g
d
ata
an
d
tr
ies
to
f
in
d
t
h
e
n
ea
r
est
s
u
p
p
o
r
t
v
ec
to
r
s
to
th
at
h
y
p
e
r
p
l
an
e
with
th
e
m
in
im
al
er
r
o
r
o
f
class
if
icatio
n
an
d
m
ax
im
al
m
ar
g
in
s
im
u
ltan
e
o
u
s
ly
to
s
o
lv
e
an
o
p
tim
izatio
n
p
r
o
b
lem
.
T
h
e
ess
en
tial
elem
en
t
in
SVM
is
th
e
k
er
n
el
f
u
n
ctio
n
,
wh
ich
m
a
p
s
s
am
p
les
in
o
n
e
f
ea
tu
r
e
s
p
ac
e
to
an
o
th
er
f
ea
tu
r
e
s
p
ac
e.
R
ad
ial
k
er
n
el
f
u
n
ctio
n
(
R
B
F),
lin
ea
r
k
er
n
el
f
u
n
ctio
n
,
p
o
ly
n
o
m
ial
k
er
n
el
f
u
n
ctio
n
,
an
d
g
au
s
s
ian
f
u
n
ctio
n
ar
e
s
o
m
e
o
f
th
e
p
o
p
u
lar
Ker
n
el
f
u
n
ctio
n
s
[
1
8
,
19]
.
T
h
e
o
p
er
ati
o
n
th
at
tak
es
d
ata
as
in
p
u
t
an
d
tr
an
s
f
o
r
m
s
it
in
to
th
e
r
eq
u
i
r
ed
f
o
r
m
is
th
e
f
u
n
ctio
n
o
f
th
e
SVM
k
er
n
el.
T
h
e
class
if
icatio
n
ac
cu
r
ac
y
o
f
SVM
lar
g
e
ly
d
ep
en
d
s
o
n
th
e
s
elec
tio
n
o
f
th
e
k
er
n
el
f
u
n
ctio
n
p
ar
am
eter
s
[
1
8
]
.
2
.
6
.
GA
A
GA
i
s
o
n
e
o
f
th
e
h
eu
r
is
tic
m
eth
o
d
s
f
o
r
r
an
d
o
m
izin
g
s
ea
r
ch
an
d
s
o
lv
in
g
th
e
o
p
tim
izatio
n
p
r
o
b
lem
s
.
Ma
n
y
d
if
f
e
r
en
t
r
esear
ch
f
ield
s
u
s
ed
GA,
g
en
etic
alg
o
r
ith
m
s
ca
n
b
e
u
s
ed
f
o
r
f
ea
tu
r
e
s
elec
tio
n
[
2
0
,
2
1
]
.
I
n
GA,
th
e
ch
r
o
m
o
s
o
m
e
is
a
p
o
s
s
ib
le
s
o
lu
tio
n
v
ec
to
r
,
wh
ich
c
o
n
s
is
ts
o
f
a
s
et
o
f
g
en
es.
I
n
th
e
s
o
lu
tio
n
s
p
ac
e,
a
s
et
o
f
ch
r
o
m
o
s
o
m
es c
alled
p
o
p
u
latio
n
.
T
h
e
g
en
er
al
s
ch
em
e
o
f
th
e
c
lass
ic
g
en
etic
alg
o
r
ith
m
as sh
o
wn
in
Fig
u
r
e
3
.
First,
d
ef
in
e
an
in
itial
p
o
p
u
l
atio
n
o
f
N
ch
r
o
m
o
s
o
m
es
ea
ch
o
f
le
n
g
th
L
.
E
ac
h
c
h
r
o
m
o
s
o
m
e
in
th
e
p
o
p
u
latio
n
is
th
en
ev
alu
ated
u
s
in
g
a
f
itn
ess
f
u
n
ctio
n
.
C
h
r
o
m
o
s
o
m
es
ar
e
s
elec
ted
to
b
e
p
ar
e
n
ts
an
d
r
ec
o
m
b
i
n
e
to
r
ep
r
o
d
u
ce
n
ew
o
f
f
s
p
r
in
g
.
F
o
r
a
p
ar
tic
u
lar
c
h
r
o
m
o
s
o
m
e,
a
p
r
o
b
a
b
ilit
y
o
f
s
elec
tio
n
p
ar
e
n
ts
s
h
o
u
ld
d
ep
e
n
d
o
n
th
e
f
itn
ess
f
u
n
ctio
n
.
T
h
e
s
elec
tio
n
p
r
o
b
ab
ilit
y
wo
u
ld
b
e:
=
(
)
∑
(
)
=
1
(
4
)
[
2
3
]
wh
er
e
r
ep
r
esen
ts
th
e
i
-
th
ch
r
o
m
o
s
o
m
e
in
th
e
p
o
p
u
latio
n
an
d
f
(
x
i
)
its
f
itn
ess
.
I
n
th
e
cr
o
s
s
o
v
er
o
p
er
atio
n
,
p
ar
en
ts
ar
e
s
elec
ted
f
o
r
m
er
g
i
n
g
to
g
et
h
er
an
d
p
r
o
d
u
ce
d
n
e
w
ch
ild
r
en
.
Mu
tatio
n
co
n
s
is
ts
o
f
r
a
n
d
o
m
l
y
alter
in
g
g
e
n
es in
s
id
e
ch
r
o
m
o
s
o
m
es,
with
a
v
e
r
y
lo
w
p
r
o
b
ab
i
lity
.
T
h
is
lead
s
th
e
GA
to
escap
e
co
n
v
er
g
in
g
to
war
d
s
lo
ca
l
o
p
tim
a.
T
h
e
p
r
e
v
io
u
s
p
o
p
u
latio
n
is
th
en
r
ep
lace
d
with
a
n
ew
p
o
p
u
latio
n
.
T
h
r
ee
GA
o
p
er
at
io
n
s
(
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
)
a
r
e
iter
ativ
ely
a
p
p
lied
u
n
til
s
o
m
e
s
to
p
p
in
g
cr
iter
i
o
n
is
m
et
o
r
a
p
r
ed
ef
in
e
d
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
is
r
ea
ch
e
d
.
I
n
o
r
d
e
r
to
o
b
tain
f
aster
co
n
v
er
g
en
ce
to
war
d
s
th
e
o
p
ti
m
al
s
o
lu
tio
n
an
d
m
itig
ate
th
e
r
is
k
o
f
lo
s
in
g
th
e
b
est
ch
r
o
m
o
s
o
m
e
b
y
cr
o
s
s
o
v
er
o
r
m
u
tatio
n
,
a
v
a
r
iatio
n
o
f
t
h
e
b
asic
GA
is
in
tr
o
d
u
ce
d
to
i
m
p
r
o
v
e
its
p
er
f
o
r
m
an
ce
,
b
y
ap
p
l
y
in
g
elitis
m
,
wh
ich
co
n
s
is
ts
o
f
p
r
eser
v
in
g
t
h
e
f
itte
s
t c
h
r
o
m
o
s
o
m
e
in
a
p
o
p
u
latio
n
f
o
r
t
h
e
n
e
x
t g
en
er
atio
n
[
2
3
]
.
Fig
u
r
e
3.
s
tep
s
o
f
t
h
e
g
en
etic
a
lg
o
r
ith
m
[
2
2
]
2
.
7
.
E
E
G
da
t
a
s
et
a
cquis
it
io
n
E
E
G
r
aw
s
ig
n
al
f
r
o
m
th
e
u
s
er
s
ca
lp
is
co
llected
,
am
p
lifi
ed
,
d
ig
itized
a
n
d
tr
a
n
s
m
itted
th
r
o
u
g
h
a
B
lu
eto
o
th
m
o
d
u
le
to
t
h
e
p
er
s
o
n
al
co
m
p
u
ter
u
s
in
g
E
MO
T
I
V
E
POC
h
ea
d
s
et
wi
th
a
s
am
p
lin
g
r
ate
o
f
1
2
8
b
p
s
.
E
MO
T
I
V
h
ea
d
s
et
m
ea
s
u
r
es
E
E
G
s
ig
n
al
f
r
o
m
1
4
lo
ca
tio
n
s
p
o
s
iti
o
n
ed
at:
AF3
,
AF4
,
F3
,
F4
,
F7
,
F8
,
FC
5
,
FC
6
,
P7
,
P8
,
T
7
,
T
8
,
O1
,
an
d
O
2
as sh
o
wn
in
Fig
u
r
e
4
[
2
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
F
in
d
in
g
th
e
d
is
crimin
a
tive
fr
e
q
u
en
cies o
f
… (
S
h
a
ima
Miq
d
a
d
Mo
h
a
med
N
a
jeeb
)
289
T
h
ir
teen
s
u
b
jects
p
er
f
o
r
m
e
d
r
ea
l
r
ig
h
t/lef
t
f
in
g
er
m
o
v
em
e
n
ts
.
T
h
e
s
u
b
jects
s
at
in
a
c
o
m
f
o
r
tab
le
ch
air
wea
r
in
g
th
e
h
ea
d
s
et
with
clo
s
ed
ey
es.
I
n
ea
ch
s
ess
io
n
,
th
e
s
u
b
ject
was
in
f
o
r
m
e
d
in
a
d
v
a
n
ce
wh
ich
h
an
d
to
m
o
v
e.
A
u
d
ito
r
y
s
tim
u
li we
r
e
u
s
ed
to
n
o
tify
th
e
o
n
-
ac
tio
n
p
e
r
io
d
o
f
th
e
s
u
b
ject
f
i
n
g
er
m
o
v
em
en
t.
T
h
e
d
u
r
atio
n
o
f
ea
ch
m
o
v
em
e
n
t
was
s
ix
s
ec
o
n
d
s
wh
ile
th
e
r
est
p
e
r
io
d
s
in
b
etwe
en
h
ad
d
if
f
er
e
n
t
d
u
r
atio
n
s
.
T
h
is
p
r
o
ce
s
s
was
p
er
f
o
r
m
ed
f
o
u
r
tim
es
in
ea
ch
s
ess
io
n
an
d
s
ep
ar
ated
b
y
r
esti
n
g
p
er
io
d
s
d
u
r
atio
n
s
.
T
h
e
d
u
r
atio
n
o
f
ea
ch
m
o
v
em
en
t
was
s
ix
s
ec
o
n
d
s
wh
ile
th
e
r
est
p
e
r
io
d
s
in
b
etwe
en
h
ad
d
i
f
f
er
en
t
len
g
th
s
.
T
h
is
p
r
o
ce
s
s
was
p
er
f
o
r
m
ed
f
o
u
r
tim
es in
ea
c
h
s
ess
io
n
an
d
s
e
p
ar
ated
b
y
a
r
esti
n
g
p
er
i
o
d
d
u
r
atio
n
s
[
2
5
]
.
Fig
u
r
e
4.
E
m
o
tiv
E
POC
elec
tr
o
d
e
p
lace
m
e
n
t
[
2
4
]
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
NS
C
las
s
if
icatio
n
m
o
to
r
m
o
v
em
e
n
ts
f
r
o
m
th
e
E
E
G
s
ig
n
al
f
ac
es
a
lo
t
o
f
d
i
f
f
icu
lties
,
o
n
e
o
f
t
h
em
i
s
ar
tifa
cts
r
em
o
v
al.
Sin
ce
m
o
to
r
s
ig
n
als
ar
e
em
b
e
d
d
ed
am
o
n
g
h
u
m
an
b
o
d
y
ar
tifa
cts
lik
e
e
y
e
m
o
v
e
m
en
t
ey
e
b
lin
k
,
an
d
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ter
n
al
o
r
g
an
s
s
ig
n
als.
M
o
to
r
s
ig
n
als
ar
e
also
s
u
f
f
er
ed
f
r
o
m
ex
ter
n
al
ar
tifa
cts
lik
e
b
ad
e
lectr
o
d
e
p
lace
m
e
n
t,
en
v
ir
o
n
m
en
t
s
o
u
n
d
s
.
B
ad
elec
tr
o
d
e
p
lace
m
en
t
a
d
d
s
a
d
if
f
er
e
n
t
r
atio
o
f
n
o
is
es
to
ea
ch
elec
tr
o
d
e
d
e
p
en
d
in
g
o
n
th
e
s
ca
lp
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n
n
ec
tiv
ity
with
th
e
elec
tr
o
d
e.
T
h
er
ef
o
r
e,
p
r
ep
r
o
ce
s
s
in
g
is
n
ee
d
ed
wh
ich
tr
ies
to
g
et
r
id
o
f
th
ese
ar
tifa
cts
an
d
ex
tr
ac
ts
th
e
E
E
G
m
o
to
r
s
ig
n
als.
On
e
o
f
t
h
e
m
o
s
t
p
o
p
u
lar
m
eth
o
d
s
is
f
ilter
in
g
b
u
t
th
e
f
r
e
q
u
en
cies
o
f
th
e
m
o
to
r
s
ig
n
als ar
e
u
n
k
n
o
wn
.
Sin
ce
GA
is
u
s
ed
to
s
ea
r
ch
f
o
r
th
ese
f
r
e
q
u
en
cies
(
m
o
to
r
d
is
cr
im
in
ativ
e
f
r
e
q
u
en
cies)
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
is
u
s
ed
as
a
f
itn
ess
f
u
n
ctio
n
o
f
GA
to
s
ea
r
ch
f
o
r
th
e
d
is
cr
im
in
ativ
e
f
r
e
q
u
en
cies
o
f
o
n
ly
two
s
u
b
jects
(
s
u
b
jects
2
a
n
d
6
)
.
T
h
is
o
p
er
a
tio
n
is
d
o
n
e
u
s
in
g
th
e
m
en
tio
n
ed
s
u
b
jects
in
o
r
d
er
n
o
t
to
f
a
ll
in
to
lo
ca
l
o
p
tim
a.
T
h
e
p
o
p
u
latio
n
s
ize
was
ch
o
s
en
as
2
0
s
in
ce
th
e
d
iv
er
s
ity
is
en
s
u
r
ed
an
d
to
r
e
d
u
ce
t
h
e
h
a
r
m
f
u
l
ef
f
ec
ts
o
f
t
h
e
m
u
tatio
n
o
p
er
ato
r
.
I
f
th
e
s
ize
o
f
th
e
p
o
p
u
latio
n
is
to
o
s
m
all,
th
is
lead
s
to
th
e
n
eg
ativ
e
im
p
ac
t
o
f
th
e
g
en
etic
alg
o
r
ith
m
b
y
th
e
m
u
tatio
n
o
p
er
ato
r
,
an
d
co
n
v
er
s
ely
,
th
e
la
t
en
cy
tim
e
o
f
th
e
GA
will
in
cr
ea
s
e.
T
h
er
ef
o
r
e
,
th
e
p
o
p
u
latio
n
s
ize
is
ch
o
s
en
ex
p
er
im
en
tally
.
T
h
e
GA
T
wen
ty
GA
iter
atio
n
s
wer
e
p
er
f
o
r
m
ed
to
ex
p
lo
r
e
th
e
f
r
eq
u
e
n
cies
b
etwe
en
0
-
6
4
HZ
an
d
it
f
o
u
n
d
o
n
l
y
2
7
f
r
eq
u
en
ci
es
ar
e
th
e
m
o
s
t
d
is
cr
im
in
ativ
e
f
r
eq
u
en
cies.
T
h
es
e
f
r
eq
u
e
n
cies
ar
e
6
,
7
,
9
-
1
5
,
1
8
,
1
9
,
2
3
,
2
4
,
2
7
,
2
8
,
3
3
,
3
7
,
3
9
,
4
4
-
4
6
,
4
8
,
5
0
,
5
1
,
5
3
,
5
9
an
d
6
4
.
Fig
u
r
e
5
s
h
o
ws
th
e
b
est an
d
wo
r
s
t c
o
s
t v
al
u
es o
f
o
n
l
y
two
s
u
b
jects d
u
r
i
n
g
G
A
s
ea
r
ch
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
is
ap
p
li
ed
to
class
if
y
th
e
m
o
v
em
e
n
t
o
f
th
e
th
ir
teen
s
u
b
jects
u
s
in
g
th
e
s
p
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ied
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r
eq
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e
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cies.
Fig
u
r
e
6
illu
s
tr
ates
th
e
class
if
icatio
n
p
e
r
f
o
r
m
a
n
ce
u
s
in
g
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
e
r
elia
b
ilit
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o
f
th
e
p
r
ep
r
o
ce
s
s
in
g
m
eth
o
d
(
p
r
o
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o
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ed
m
eth
o
d
)
is
o
b
v
io
u
s
ly
clea
r
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d
th
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s
h
o
wn
with
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h
e
im
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ac
t
p
er
f
o
r
m
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ce
th
at
h
as
a
n
im
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T
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o
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ten
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u
b
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t
o
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n
s
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o
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ly
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as
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im
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ac
t
a
b
o
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e
7
0
%
an
d
th
e
r
est
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s
u
b
jects)
h
av
e
an
im
p
ac
t
r
an
g
e
o
f
8
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8
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%.
T
h
e
f
o
llo
win
g
eq
u
atio
n
is
u
s
ed
to
ev
alu
ate
th
e
class
if
icat
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n
r
ate:
=
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(
5
)
T
h
e
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ec
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d
s
tag
e
in
th
e
p
r
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m
eth
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d
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cr
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r
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tr
ies
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e
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ce
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r
ain
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h
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es.
Fig
u
r
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7
illu
s
tr
ates
th
e
e
f
f
ec
ts
o
f
c
r
o
s
s
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co
r
r
elatio
n
.
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h
e
d
if
f
er
e
n
ce
b
etwe
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e
r
ig
h
t
an
d
lef
t
f
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g
er
m
o
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e
m
en
ts
is
s
h
o
wn
in
Fig
u
r
e
s
7
(
a)
an
d
(
b
)
an
d
th
is
d
if
f
er
en
ce
i
s
n
'
t
o
b
v
io
u
s
ly
clea
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
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o
m
m
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n
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t E
l Co
n
tr
o
l
,
Vo
l.
1
9
,
No
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1
,
Feb
r
u
ar
y
202
1
:
28
5
-
29
2
290
Me
an
wh
ile,
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ter
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s
in
g
c
r
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s
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r
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,
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e
d
if
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er
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ce
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ig
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ec
o
m
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tr
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ely
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r
as sh
o
wn
in
Fig
u
r
e
s
7
(
c)
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d
(
d
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.
Fig
u
r
e
5
.
T
h
e
b
est an
d
th
e
wo
r
s
t c
o
s
t v
alu
es o
f
GA
Fig
u
r
e
6
.
T
h
e
class
if
icatio
n
r
ates o
f
1
3
s
u
b
ject
(
a
)
(
b
)
(
c)
(
d
)
Fig
u
r
e
7
.
E
E
G
t
o
p
o
g
r
ap
h
y
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t
er
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d
b
ef
o
r
e
cr
o
s
s
-
co
r
r
elatio
n
ef
f
ec
ts
;
(
a)
an
d
(
b
)
o
r
ig
i
n
al
E
E
G
r
ig
h
t/lef
t f
in
g
e
r
m
o
v
em
en
ts
r
esp
ec
tiv
ely
,
(
c)
a
n
d
(
d
)
s
am
e
E
E
G
s
ig
n
al
af
ter
cr
o
s
s
-
co
r
r
elatio
n
T
h
e
s
tatis
tical
p
ar
am
eter
s
tag
e
r
ed
u
ce
s
th
e
f
ea
tu
r
e
s
p
ac
e
e
x
tr
ac
ted
f
r
o
m
th
e
E
E
G
s
ig
n
al
wh
ich
r
ef
lect
to
th
e
co
m
p
u
tatio
n
al
tim
e
an
d
it
al
s
o
f
ilter
o
u
t
th
e
u
n
n
ec
ess
ar
y
an
d
r
ed
u
n
d
a
n
t
f
ea
tu
r
es.
T
ab
le
1
illu
s
tr
ates
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
F
in
d
in
g
th
e
d
is
crimin
a
tive
fr
e
q
u
en
cies o
f
… (
S
h
a
ima
Miq
d
a
d
Mo
h
a
med
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a
jeeb
)
291
am
o
u
n
t
o
f
f
ea
tu
r
es
r
ed
u
ctio
n
af
ter
u
s
in
g
th
e
ten
f
ea
tu
r
es
s
tatis
tical
ca
lcu
latio
n
.
I
n
th
is
t
ab
le,
ten
s
tatis
tical
f
ea
tu
r
es
p
r
o
d
u
ce
1
4
0
f
ea
tu
r
es
(
1
4
ch
an
n
els
x
1
0
s
tatis
tical
f
ea
tu
r
es)
an
d
th
e
am
o
u
n
t
o
f
d
ata
r
ep
r
esen
ts
t
h
e
f
e
d
d
ata
b
ef
o
r
e
a
n
d
af
ter
cr
o
s
s
-
co
r
r
elatio
n
.
I
n
5
esti
m
ate
th
e
am
o
u
n
t
o
f
d
ata
r
ed
u
ctio
n
.
T
h
er
e
f
o
r
e,
th
e
am
o
u
n
t
o
f
d
ata
f
ed
to
t
h
e
class
if
ier
ar
e
r
e
d
u
ce
d
to
7
.
8
% a
n
d
3
.
9
% b
ef
o
r
e
an
d
af
ter
cr
o
s
s
co
r
r
elatio
n
s
tag
e
r
esp
ec
tiv
ely
.
=
.
.
100%
(
6
)
T
ab
el
1.
T
h
e
d
ata
r
e
d
u
ctio
n
p
e
r
ce
n
tag
es a
f
ter
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d
b
ef
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r
e
f
ea
t
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r
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ex
tr
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tio
n
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o
.
o
f
i
n
p
u
t
d
a
t
a
D
a
t
a
A
m
o
u
n
t
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o
.
o
f
f
e
a
t
u
r
e
s
D
a
t
a
r
e
d
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c
t
i
o
n
O
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g
i
n
a
l
d
a
t
a
1
4
c
h
a
n
n
e
l
s
x
1
2
8
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mp
l
e
s
1
4
0
7
.
8
%
C
r
o
ss
c
o
r
r
e
l
a
t
e
d
d
a
t
a
1
4
c
h
a
n
n
e
l
s
x
2
5
5
s
a
mp
l
e
s
1
4
0
3
.
9
%
4.
CO
NCLU
SI
O
NS
T
h
e
p
ap
er
p
r
esen
ts
th
e
p
r
o
p
o
s
ed
m
eth
o
d
f
o
r
p
r
e
p
r
o
ce
s
s
in
g
an
d
ex
tr
ac
tin
g
f
ea
tu
r
es
f
r
o
m
E
E
G
r
ea
l
m
o
to
r
m
o
v
em
en
ts
.
I
t
em
p
lo
y
s
less
co
m
p
lex
to
o
ls
lik
e
DFT
an
d
cr
o
s
s
-
co
r
r
elatio
n
u
n
lik
e
u
s
in
g
I
C
A
o
r
PC
A
m
en
tio
n
ed
i
n
s
ec
tio
n
o
n
e
o
f
s
o
m
e
r
esear
ch
es.
T
h
e
p
r
o
p
o
s
e
d
m
eth
o
d
p
r
o
v
es
its
ef
f
ec
tiv
e
n
ess
ev
en
with
E
E
G
s
ig
n
als
ac
q
u
ir
ed
b
y
g
am
m
in
g
ac
q
u
is
itio
n
eq
u
ip
m
en
t
(
E
MO
T
I
V
E
POC
+)
,
s
ee
F
ig
u
r
e
6
.
Hen
c
e,
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
h
as
g
o
o
d
p
er
f
o
r
m
an
ce
f
o
r
th
ir
tee
n
s
u
b
jects
s
o
th
at
it
p
r
o
v
es
th
at
G
A,
wh
ich
a
p
p
lied
o
n
two
E
E
G
s
u
b
ject
s
ig
n
als,
d
o
es
n
'
t f
all
in
to
lo
ca
l o
p
tim
a.
T
h
e
s
ec
o
n
d
s
tag
e
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
en
lar
g
es th
e
d
if
f
er
en
ce
b
etwe
en
th
e
two
E
E
G
class
es.
W
e
ca
n
clea
r
ly
s
ee
th
at
th
e
Fig
u
r
e
7
m
ea
n
wh
ile
u
tili
zin
g
th
e
s
tatis
tic
m
eth
o
d
s
to
r
e
d
u
ce
th
e
am
o
u
n
t
o
f
t
h
e
p
r
o
ce
s
s
ed
f
ea
tu
r
es f
ed
t
o
th
e
class
if
ier
as it is sh
o
wn
in
th
e
T
ab
le
1
.
RE
F
E
R
E
NC
E
S
[1
]
A.
Rid
o
u
h
,
D.
Bo
u
tan
a
a
n
d
M
.
B
e
n
id
ir,
“
C
o
m
p
a
ra
ti
v
e
S
t
u
d
y
o
f
Ti
m
e
F
re
q
u
e
n
c
y
An
a
l
y
sis Ap
p
li
c
a
ti
o
n
o
n
A
b
n
o
rm
a
l
EE
G
S
ig
n
a
ls,”
Rec
e
n
t
A
d
v
a
n
c
e
s i
n
El
e
c
trica
l
E
n
g
in
e
e
rin
g
a
n
d
C
o
n
tro
l
A
p
p
li
c
a
ti
o
n
s,
S
p
ri
n
g
e
r,
p
p
.
3
5
5
-
3
6
8
,
2
0
1
7
.
[2
]
H.
F
a
u
z
i,
A.
M
.
Az
z
a
m
,
I.
M
.
S
h
a
p
iai,
M
.
K
y
o
so
,
U.
Kh
a
iru
d
d
in
a
n
d
T.
Ko
m
u
ra
,
“
E
n
e
rg
y
e
x
trac
ti
o
n
m
e
th
o
d
f
o
r
EE
G
c
h
a
n
n
e
l
se
lec
ti
o
n
,
”
T
EL
KO
M
NI
KA
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
m
p
u
t
in
g
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
,
v
o
l.
1
7
,
n
o
.
5
,
pp.
2
5
6
1
-
2
5
7
1
,
2
0
1
9
.
[3
]
S
h
a
k
sh
i
a
n
d
J.
Ra
m
a
v
tar,
“
Bra
in
Wav
e
Clas
sifica
ti
o
n
a
n
d
F
e
a
tu
re
Ex
trac
ti
o
n
o
f
E
EG
S
ig
n
a
l
b
y
Us
i
n
g
F
F
T
o
n
Lab
Vie
w,”
In
ter
n
a
ti
o
n
a
l
Res
e
a
rc
h
J
o
u
rn
a
l
o
f
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
(IR
J
ET
)
,
v
o
l.
7
,
no
.
3
,
p
p
.
1
2
0
8
-
1
2
1
2
,
2
0
1
6
.
[4
]
S
.
M
.
Isla
m
,
M
.
A.
El
-
Ha
jj
,
H.
Al
a
wie
h
,
Z.
Da
wy
a
n
d
N.
Ab
b
a
s,
“
EE
G
m
o
b
il
it
y
a
rti
fa
c
t
re
m
o
v
a
l
fo
r
a
m
b
u
lato
ry
e
p
il
e
p
ti
c
se
izu
re
p
re
d
ictio
n
a
p
p
li
c
a
ti
o
n
s,”
Bi
o
me
d
ica
l
S
i
g
n
a
l
Pr
o
c
e
ss
in
g
a
n
d
Co
n
tro
l
,
El
se
v
ier
,
v
o
l.
5
5
,
2
0
2
0
.
[5
]
A.
Bish
t
,
C.
Ka
u
r
a
n
d
P
.
S
in
g
h
,
“
Re
c
e
n
t
a
d
v
a
n
c
e
s
in
a
rti
fa
c
ts
r
e
m
o
v
a
l
tec
h
n
iq
u
e
s
fo
r
EE
G
sig
n
a
l
p
r
o
c
e
ss
in
g
,
”
In
telleg
e
n
t
c
o
mm
u
n
ica
ti
o
n
,
c
o
n
tr
o
l
a
n
d
d
e
v
ice
s,
a
d
v
a
n
c
e
s
in
i
n
telle
g
e
n
t
a
n
d
c
o
mp
u
ti
n
g
,
v
o
l.
9
8
9
,
p
p
.
3
8
5
-
3
9
2
,
2
0
2
0
.
[6
]
G
.
F
a
rid
,
S
.
Ha
m
e
e
d
a
h
,
A.
Dil
n
a
sh
in
,
F
.
Om
a
r
a
n
d
U.
K.
Yu
s
u
f,
“
Clas
sifica
ti
o
n
o
f
Wr
ist
M
o
v
e
m
e
n
ts
Us
in
g
EE
G
S
ig
n
a
ls,”
J
o
u
rn
a
l
o
f
Ne
x
t
Ge
n
e
ra
t
io
n
I
n
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
(J
NIT
),
v
o
l.
4
,
Oc
to
b
e
r
2
0
1
3
.
[7
]
V.
M
.
M
.
An
d
riu
s
,
M
.
Tad
a
s
a
n
d
S
.
Ru
ta,
“
Al
g
o
rit
h
m
fo
r
a
u
to
m
a
ti
c
EE
G
c
las
sific
a
ti
o
n
a
c
c
o
rd
i
n
g
to
t
h
e
e
p
il
e
p
sy
ty
p
e
:
Be
n
ig
n
fo
c
a
l
c
h
il
d
h
o
o
d
e
p
i
lep
sy
a
n
d
stru
c
tu
ra
l
fo
c
a
l
e
p
il
e
p
s
y
,
”
Bi
o
me
d
ica
l
S
ig
n
a
l
Pro
c
e
ss
in
g
a
n
d
C
o
n
tr
o
l,
v
o
l.
4
8
,
p
p
.
1
1
8
-
1
2
7
,
2
0
1
9
.
[8
]
H.
A.
M
o
h
a
m
m
a
d
,
S
.
A
y
a
a
n
d
A
.
Kh
a
led
,
“
Au
t
o
m
a
ted
Clas
sifica
ti
o
n
o
f
L/
R
Ha
n
d
M
o
v
e
m
e
n
t
EE
G
S
ig
n
a
ls
u
sin
g
Ad
v
a
n
c
e
d
F
e
a
tu
re
E
x
trac
ti
o
n
a
n
d
M
a
c
h
in
e
Lea
rn
i
n
g
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
Co
mp
u
ter
S
c
ien
c
e
a
n
d
Ap
p
li
c
a
ti
o
n
s,
v
o
l
.
4
,
n
o
.
6
,
p
p
.
2
0
7
-
2
1
2
,
2
0
1
3
.
[9
]
R.
Zare
i,
J.
He
,
S
.
S
iu
l
y
a
n
d
Y.
Z
h
a
n
g
,
“
A P
CA aid
e
d
c
ro
ss
-
c
o
v
a
ri
a
n
c
e
sc
h
e
m
e
fo
r
d
isc
rimin
a
ti
v
e
fe
a
tu
re
e
x
trac
ti
o
n
fro
m
EE
G
sig
n
a
ls,”
Co
m
p
u
ter
M
e
th
o
d
s a
n
d
Pro
g
r
a
ms
in
Bi
o
me
d
icin
e
,
El
se
v
ier
,
v
o
l.
1
4
6
,
p
p
.
4
7
-
5
7
,
2
0
1
7
.
[1
0
]
S
.
P
a
rth
a
n
a
,
P
.
Tri
p
a
t
h
i,
P
.
S
.
M
a
n
a
sh
a
n
d
K.
S
.
Ka
n
d
a
rp
a
,
“
P
re
-
p
r
o
c
e
ss
in
g
a
n
d
F
e
a
tu
re
Ex
trac
ti
o
n
Tec
h
n
iq
u
e
s
fo
r
EE
G
BCI
Ap
p
li
c
a
ti
o
n
s,”
J
o
u
r
n
a
l
o
f
E
n
g
i
n
e
e
rin
g
T
e
c
h
n
o
lo
g
y
,
v
o
l
.
5
,
2
0
1
6
.
[1
1
]
I.
T.
S
.
D.
Ha
ri
Krish
n
a
,
“
Au
t
o
n
o
m
u
o
s
Ro
b
o
t
C
o
n
tro
l
b
a
se
d
o
n
EE
G
a
n
d
Cro
ss
-
c
o
rre
latio
n
,
”
IEE
E
Co
n
fer
e
n
c
e
s,
p
p
.
1
-
4
,
2
0
1
6
.
[1
2
]
H.
H,
M
.
A
a
n
d
S
.
S
.
,
“
F
e
a
tu
r
e
Ex
trac
ti
o
n
El
e
c
tr
o
En
c
e
p
h
a
lo
G
ra
m
(EE
G
)
u
sin
g
wa
v
e
let
tran
sf
o
rm
fo
r
c
u
rso
r
m
o
v
e
m
e
n
t,
”
3
rd
An
n
u
a
l
A
p
p
li
e
d
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
C
o
n
fer
e
n
c
e
,
v
o
l.
4
3
4
,
n
o
.
1
,
p
p
.
1
-
8,
2
0
1
8
.
[1
3
]
A.
Na
rin
,
Y.
Isle
r
a
n
d
M
.
Oz
e
r,
“
Eff
e
c
t
o
f
F
e
a
tu
re
S
e
lec
ti
o
n
b
y
G
e
n
e
ti
c
Alg
o
rit
h
m
o
n
Earl
y
P
re
d
icti
o
n
P
e
rfo
rm
a
n
c
e
o
f
P
AF
At
tac
k
,
”
In
n
o
v
a
ti
o
n
s in
In
telli
g
e
n
t
S
y
ste
ms
a
n
d
A
p
p
li
c
a
ti
o
n
s Co
n
fer
e
n
c
e
,
2
0
1
8
.
[1
4
]
C.
C.
Lu
n
g
,
S
.
Ru
b
it
a
a
n
d
S
.
H.
S
it
i,
“
F
e
a
tu
re
e
x
trac
ti
o
n
o
f
e
e
g
sig
n
a
l
u
si
n
g
wa
v
e
let
tran
sf
o
rm
fo
r
a
u
ti
sm
c
las
sifica
tv
o
l,
”
J
o
u
rn
a
l
o
f
E
n
g
in
e
e
rin
g
a
n
d
Ap
p
li
e
d
S
c
ien
c
e
s,
v
o
l.
1
0
,
n
o
.
1
9
,
p
p
.
8
5
3
3
-
8
5
4
0
,
2
0
1
5
.
[1
5
]
M
.
Ka
m
e
l
a
n
d
R.
Aic
h
a
,
“
M
u
l
t
i
o
p
ti
m
ize
d
S
VM
c
las
sifiers
fo
r
m
o
to
r
ima
g
e
ry
left
a
n
d
ri
g
h
t
h
a
n
d
m
o
v
e
m
e
n
t
id
e
n
ti
fica
ti
o
n
,
”
A
u
stra
l
a
s P
h
y
s E
n
g
S
c
i
M
e
d
,
v
o
l.
4
2
,
n
o
.
2
,
p
p
.
9
4
9
-
9
5
8
,
2
0
1
9
.
[1
6
]
R.
M
.
M
u
sta
ffa
,
W.
,
Ye
e
,
N.
Ab
d
u
ll
a
h
a
n
d
A.
N.
Na
sh
a
ru
d
d
i
n
,
“
Co
lo
u
r
-
b
a
se
d
b
u
i
ld
i
n
g
re
c
o
g
n
it
i
o
n
u
si
n
g
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
,
”
T
EL
KOM
NIK
A
T
e
lec
o
mm
u
n
ic
a
ti
o
n
C
o
mp
u
ti
n
g
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l
,
v
o
l.
1
7
,
n
o
.
1
,
p
p
.
4
7
3
-
4
8
0
,
F
e
b
ru
a
ry
2
0
1
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
1
9
,
No
.
1
,
Feb
r
u
ar
y
202
1
:
28
5
-
29
2
292
[1
7
]
P
.
B.
Ha
rn
e
,
Y.
B
o
b
a
d
e
,
D.
R.
Dh
e
k
e
k
a
r
a
n
d
A.
Hiwa
le,
“
S
VM
c
las
sifica
ti
o
n
o
f
EE
G
sig
n
a
l
t
o
a
n
a
l
y
z
e
th
e
e
ffe
c
t
o
f
OM
m
a
n
tra m
e
d
it
a
ti
o
n
o
n
t
h
e
b
ra
in
,
”
2
0
1
9
IEE
E
1
6
t
h
In
d
ia
Co
u
n
c
il
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
(INDICO
N)
,
2
0
1
9
.
[1
8
]
B.
P
a
n
d
S
.
K.
J
.
,
“
S
u
p
p
o
rt
Ve
c
to
r
M
a
c
h
i
n
e
Tec
h
n
iq
u
e
f
o
r
EE
G
S
ig
n
a
ls,”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Co
mp
u
ter
Ap
p
li
c
a
ti
o
n
s,
v
o
l
.
6
3
,
n
o
.
1
3
,
p
p
.
1
-
5
,
2
0
1
3
.
[1
9
]
E.
Ay
d
e
m
ir,
T.
T
u
n
c
e
r
a
n
d
S
.
Do
g
a
n
,
“
A
T
u
n
a
b
le
-
Q
wa
v
e
let
tran
sf
o
rm
a
n
d
q
u
a
d
ru
p
le
sy
m
m
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].
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rsity
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n
wa
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in
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0
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3
.
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rre
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g
,
a
rti
fic
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telli
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n
t.
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M
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su
l
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it
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–
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M
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n
,
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n
wa
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q
.
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h
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m
b
e
r
o
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l
d
a
tab
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se
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–
3
.
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h
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m
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tab
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se
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–
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.
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r
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m
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Rik
a
b
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h
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s
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c
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n
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K
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t,
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q
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re
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.
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re
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tri
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l
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fr
o
m
th
e
Al
M
u
s
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Un
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n
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g
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d
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q
.
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is
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.
S
c
.
d
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re
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m
m
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m
s
fro
m
Ca
li
fo
rn
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sta
te
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n
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/F
u
ll
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r
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n
,
USA
in
2
0
1
4
.
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rre
n
t
re
se
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rc
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m
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m
s
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s
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rt
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it
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a
n
d
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n
tern
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o
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Th
i
n
g
s
(I
o
T
).
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K
u
t
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it
y
–
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y
ALRa
b
e
e
,
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it
,
Ira
q
.
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h
e
n
u
m
b
e
r
o
f
a
rti
c
les
in
n
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ti
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n
a
l
d
a
ta
b
a
se
s
–
1
0
.
Th
e
n
u
m
b
e
r
o
f
a
rti
c
les
in
in
tern
a
ti
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n
a
l
d
a
tab
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s
e
s
–
20
.
S
h
a
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m
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M
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a
m
m
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d
Ali
,
sh
e
is
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tl
y
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n
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o
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c
lt
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m
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ter
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h
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g
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g
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e
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rtme
n
t,
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rt
h
e
rn
Tec
h
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ica
l
Un
iv
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rsity
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n
M
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su
l
,
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n
wa
,
Ira
q
.
S
h
e
re
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.
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d
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re
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in
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o
m
p
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ter
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n
g
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rin
g
in
2
0
0
2
fro
m
No
rth
e
rn
Tec
h
n
ica
l
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v
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r
sity
in
M
o
su
l
,
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q
.
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r
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.
Tec
h
.
d
e
g
re
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n
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o
m
p
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ter
En
g
i
n
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rin
g
fo
c
u
sin
g
o
n
sy
n
th
e
sis
S
y
ste
m
s
c
o
n
fi
g
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re
d
o
n
F
P
G
A,
IRAQ
i
n
2
0
1
6
.
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r
c
u
rre
n
t
re
se
a
rc
h
in
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in
c
l
u
d
e
sig
n
a
l
p
ro
c
e
ss
in
g
,
EE
G
S
i
g
n
a
l
Us
in
g
G
e
n
e
ti
c
Alg
o
rit
h
m
,
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n
telli
g
e
n
t
tec
h
n
o
l
o
g
ies
.
Al
M
o
su
l
c
it
y
–
Ha
y
AL
b
a
k
e
r,
Ne
n
wa
,
Ira
q
.
Th
e
n
u
m
b
e
r
o
f
a
rti
c
les
in
n
a
ti
o
n
a
l
d
a
tab
a
se
s
–
1
.
T
h
e
n
u
m
b
e
r
o
f
a
rti
c
l
e
s in
in
ter
n
a
ti
o
n
a
l
d
a
tab
a
se
s
–
2
.
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