Al-Rafidain Journal of Medical Sciences ( ISSN 2789-3219 ),
Journal Year:
2023,
Volume and Issue:
5(1S), P. S113 - 118
Published: Nov. 9, 2023
Background:
The
diversity
of
autism
spectrum
disorder
presentation
necessitates
the
use
simple
tests.
Quantitative
electroencephalography
is
a
low-cost,
instrument
that
being
investigated
as
clinical
tool
for
monitoring
abnormal
brain
development.
Objective:
To
study
waves
by
computer-analyzed
EEG
(quantitative
EEG)
in
autistic
children
and
correlate
changes
to
severity
children.
Methods:
involved
65
children;
30
were
recruited
from
center
pediatric
neurology
consultant
child
welfare
teaching
hospital,
Medical
City,
met
DSM-5
criteria
autism.
Another
35
age-matched,
normally-developed
ASD
criteria,
Childhood
Autism
Rating
Scale,
severity.
Absolute
relative
spectral
power
measurements
used
investigate
activity.
Results:
absolute
delta
increased
patients
compared
controls
(p<0.05)
all
regions.
There
an
association
between
disease
score
theta
areas.
wave
peaked
occipital
temporal
region.
Conclusions:
can
aid
evaluation
classification
ASD.
QEEG
testing
revealed
abnormalities
be
helpful
assessment
Sovremennye tehnologii v medicine,
Journal Year:
2023,
Volume and Issue:
15(6), P. 63 - 63
Published: Dec. 27, 2023
Brain-computer
interfaces
(BCIs)
are
a
group
of
technologies
that
allow
mental
training
with
feedback
for
post-stroke
motor
recovery.
Varieties
these
have
been
studied
in
numerous
clinical
trials
more
than
10
years,
and
their
construct
software
constantly
being
improved.
Despite
the
positive
treatment
results
availability
registered
medical
devices,
there
currently
number
problems
wide
application
BCI
technologies.
This
review
provides
information
on
most
types
BCIs
its
protocols
describes
evidence
base
effectiveness
upper
limb
recovery
after
stroke.
The
main
scaling
this
technology
ways
to
solve
them
also
described.
Brain
computer
interface
(BCI)-based
system
has
become
an
alternative
treatment
for
stroke
rehabilitation.
Multichannel
EEG
signal
extraction
is
commonly
used
in
BCIs,
whereby
reducing
channels
number
significant
role
the
model
complexity
and
computational
time
hence
patient
rehabilitation
session
time.
Thus,
this
paper,
a
BCI
post-stroke
neurorehabilitation
with
reduced
of
presented.
The
presented
balances
between
complexity/rehabilitation
accuracy
by
optimizing
number.
was
evaluated
using
five
classification
algorithms
at
different
dataset
50
poststroke
patients.
results
showed
that
selected
8
60%
achieved
88%.
JOINS (Journal of Information System),
Journal Year:
2024,
Volume and Issue:
9(1), P. 67 - 74
Published: July 19, 2024
Penyakit
stroke
adalah
salah
satu
penyakit
kardiofaskuler
jika
menyerang
akan
menyebabkan
cacad
permanen
dan
meninggal
dunia.
Proses
pemeriksaan
membutuhkan
dokter
hanya
berdasarkan
visual
unruk
mendiagnosa
penyakit,
dilakukan
banyak
maka
diagnose
berbeda-beda.
Unruk
menghindari
hal
tersebut
dibutuhkan
alat
EEG
untuk
mengambil
aktivitas
gelombang
otak
yang
hasil
pengambilan
data
dalam
bentuk
mentah.
Data
mentah
agar
dapat
dihasilkan
proses
analisis
diperlukan
pemrosesan
sinyal
terdiri
dari
band
pass
filter,
cleaning
data,
segmentasi
decomposisi.
Permasalahan
selama
ini
timbul
bahwa
tersebt
masih
noise
baik
pergerakan
mata
ataupun
otot.,
sehingga
telah
diolah
menjadi
dasar
pemilihan
feature.
Penelitian
menggunakan
metode
penelitian
2
tahapan
yaitu
pre
processing,
dimana
processing
memiliki
3
langkah
segmentasi.Hasil
akhir
Segmentasi
Sinyal
Berdasarkan
Domain
Waktu
Sebagai
Dasar
Dalam
Pengolahan
Pengambilan
Keputusan
Rehabilitasi
Stroke.
Kata
kunci:
EEG,
Stroke,
Segmentasi,
Cleaning
Data,
Band
Pass
Filter,
Feature
Journal of Neural Engineering,
Journal Year:
2024,
Volume and Issue:
21(6), P. 066049 - 066049
Published: Dec. 1, 2024
.
Brain-computer
interface(BCI)
is
leveraged
by
artificial
intelligence
in
EEG
signal
decoding,
which
makes
it
possible
to
become
a
new
means
of
human-machine
interaction.
However,
the
performance
current
decoding
methods
still
insufficient
for
clinical
applications
because
inadequate
information
extraction
and
limited
computational
resources
hospitals.
This
paper
introduces
hybrid
network
that
employs
transformer
with
modified
locally
linear
embedding
sliding
window
convolution
decoding.
Brain Sciences,
Journal Year:
2024,
Volume and Issue:
15(1), P. 28 - 28
Published: Dec. 29, 2024
Background/Objectives:
Noninvasive
brain
stimulation
(NIBS)
can
boost
motor
recovery
after
a
stroke.
Certain
movement
phases
are
more
responsive
to
NIBS,
so
system
that
auto-detects
these
would
optimize
timing.
This
study
assessed
the
effectiveness
of
various
machine
learning
models
in
identifying
hemiparetic
individuals
undergoing
simultaneous
NIBS
and
EEG
recordings.
We
hypothesized
transcranial
direct
current
(tDCS),
form
enhance
signals
related
improve
classification
accuracy
compared
sham
stimulation.
Methods:
data
from
10
chronic
stroke
patients
11
healthy
controls
were
recorded
before,
during,
tDCS.
Eight
algorithms
five
ensemble
methods
used
classify
two
(hold
posture
reaching)
during
each
periods.
Data
preprocessing
included
z-score
normalization
frequency
band
power
binning.
Results:
In
participants
who
received
active
tDCS,
for
hold
vs.
reach
increased
pre-stimulation
late
intra-stimulation
period
(72.2%
75.2%,
p
<
0.0001).
Late
tDCS
surpassed
(75.2%
71.5%,
Linear
discriminant
analysis
was
most
accurate
(74.6%)
algorithm
with
shortest
training
time
(0.9
s).
Among
methods,
low
gamma
(30–50
Hz)
achieved
highest
(74.5%),
although
this
result
did
not
achieve
statistical
significance
actively
stimulated
participants.
Conclusions:
Machine
showed
enhanced
phase
These
results
suggest
their
feasibility
real-time
detection
neurorehabilitation,
including
brain–computer
interfaces
recovery.
Mathematics,
Journal Year:
2023,
Volume and Issue:
11(17), P. 3801 - 3801
Published: Sept. 4, 2023
Electrical
activities
of
the
human
brain
can
be
recorded
with
electroencephalography
(EEG).
To
characterize
motor
imagery
(MI)
tasks
for
brain–computer
interface
(BCI)
implementation
is
an
easy
and
cost-effective
tool.
The
MI
task
represented
by
a
short-time
trial
multichannel
EEG.
In
this
paper,
signal
each
channel
raw
EEG
decomposed
into
finite
set
narrowband
signals
using
Fourier-transformation-based
bandpass
filter.
Rhythmic
components
are
that
related
to
tasks.
subband
arranged
extend
dimension
in
spatial
domain.
features
extracted
from
extended
trials
common
pattern
(CSP).
An
optimum
number
employed
classify
artificial
neural
network.
integrated
approach
full-band
implemented
derive
discriminative
classification.
addition,
subject-dependent
parameter
optimization
scheme
enhances
performance
proposed
method.
evaluation
method
obtained
two
publicly
available
benchmark
datasets
(Dataset
I
Dataset
II).
experimental
results
terms
classification
accuracy
(93.88%
91.55%
II)
show
it
performs
better
than
recently
developed
algorithms.
enhanced
very
much
applicable
BCI
implementation.
Al-Rafidain Journal of Medical Sciences ( ISSN 2789-3219 ),
Journal Year:
2023,
Volume and Issue:
5(1S), P. S113 - 118
Published: Nov. 9, 2023
Background:
The
diversity
of
autism
spectrum
disorder
presentation
necessitates
the
use
simple
tests.
Quantitative
electroencephalography
is
a
low-cost,
instrument
that
being
investigated
as
clinical
tool
for
monitoring
abnormal
brain
development.
Objective:
To
study
waves
by
computer-analyzed
EEG
(quantitative
EEG)
in
autistic
children
and
correlate
changes
to
severity
children.
Methods:
involved
65
children;
30
were
recruited
from
center
pediatric
neurology
consultant
child
welfare
teaching
hospital,
Medical
City,
met
DSM-5
criteria
autism.
Another
35
age-matched,
normally-developed
ASD
criteria,
Childhood
Autism
Rating
Scale,
severity.
Absolute
relative
spectral
power
measurements
used
investigate
activity.
Results:
absolute
delta
increased
patients
compared
controls
(p<0.05)
all
regions.
There
an
association
between
disease
score
theta
areas.
wave
peaked
occipital
temporal
region.
Conclusions:
can
aid
evaluation
classification
ASD.
QEEG
testing
revealed
abnormalities
be
helpful
assessment
