A two-stage feature redundancy minimization methodology framework for motor imagery EEG classification
Multimedia Tools and Applications,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 14, 2025
Язык: Английский
Transformative Deep Learning Approaches for Enhanced Image Analysis and Processing
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 329 - 378
Опубликована: Март 7, 2025
This
chapter
examines
how
transformative
deep
learning
is
revolutionizing
image
processing
and
analysis,
especially
in
the
context
of
complex
imaging
tasks.
Even
with
major
improvements,
accuracy
efficiency
issues
are
still
common.
To
address
these
challenges,
we
discussed
different
methods
that
integrate
architectures,
such
as
convolutional
neural
networks
(CNNs),
RCNN
their
variants,
sophisticated
data
preprocessing
approaches.
A
thorough
analysis
model
architectures
demonstrates
significant
advantages
provides
over
conventional
techniques,
improving
diagnostic
precision
effectiveness
while
facilitating
individualized
care
a
variety
fields,
including
remote
sensing,
self-driving
vehicles,
medical
imaging.
In
chapter,
critically
review
literature,
represent
step
forward
applications
for
advanced
processing,
demonstrating
its
potential
to
current
limitations
drive
future
advancements.
Язык: Английский
A brief survey on human activity recognition using motor imagery of EEG signals
Electromagnetic Biology and Medicine,
Год журнала:
2024,
Номер
unknown, С. 1 - 16
Опубликована: Окт. 19, 2024
Human
being's
biological
processes
and
psychological
activities
are
jointly
connected
to
the
brain.
So,
examination
of
human
activity
is
more
significant
for
well-being
humans.
There
various
models
brain
detection
considering
neuroimaging
attaining
decreased
time
requirement,
increased
control
commands,
enhanced
accuracy.
Motor
Imagery
(MI)-based
Brain-Computer
Interface
(BCI)
systems
create
a
way
in
which
can
interact
with
environment
by
processing
Electroencephalogram
(EEG)
signals.
Activity
Recognition
(HAR)
deals
identifying
physiological
beings
based
on
sensory
This
survey
reviews
different
methods
available
HAR
MI-EEG
A
total
50
research
articles
from
EEG
signals
considered
this
survey.
discusses
challenges
faced
techniques
HAR.
Moreover,
papers
assessed
parameters,
techniques,
publication
year,
performance
metrics,
utilized
tools,
employed
databases,
etc.
were
many
developed
solve
problem
they
classified
as
Machine
Learning
(ML)
Deep
(DL)models.
At
last,
gaps
limitations
discussed
that
contribute
developing
an
effective
Язык: Английский