Frontiers in Human Neuroscience,
Journal Year:
2023,
Volume and Issue:
17
Published: May 4, 2023
Action
observation
(AO)
is
widely
used
as
a
post-stroke
therapy
to
activate
sensorimotor
circuits
through
the
mirror
neuron
system.
However,
passive
often
considered
be
less
effective
and
interactive
than
goal-directed
movement
observation,
leading
suggestion
that
of
actions
may
have
stronger
therapeutic
potential,
AO
has
been
shown
mechanisms
for
monitoring
action
errors.
Some
studies
also
suggested
use
form
Brain-computer
interface
(BCI)
feedback.
In
this
study,
we
investigated
potential
virtual
hand
movements
within
P300-based
BCI
feedback
system
We
explored
role
anticipation
estimation
during
observation.
Twenty
healthy
subjects
participated
in
study.
analyzed
event-related
desynchronization
synchronization
(ERD/S)
EEG
rhythms
Error-related
potentials
(ErrPs)
finger
flexion
presented
P300-BCI
loop
compared
dynamics
ERD/S
ErrPs
correct
these
markers
under
two
conditions:
when
anticipated
demonstration
was
unexpected.
A
pre-action
mu-ERD
found
both
before
loop.
Furthermore,
significant
increase
beta-ERS
incorrect
trials.
suggest
exaggerate
passive-AO
effect,
it
engages
well
error
simultaneously.
The
results
study
provide
insights
into
with
AO-feedback
tool
neurorehabilitation.
Bioengineering,
Journal Year:
2022,
Volume and Issue:
9(12), P. 768 - 768
Published: Dec. 5, 2022
Patients
with
severe
CNS
injuries
struggle
primarily
their
sensorimotor
function
and
communication
the
outside
world.
There
is
an
urgent
need
for
advanced
neural
rehabilitation
intelligent
interaction
technology
to
provide
help
patients
nerve
injuries.
Recent
studies
have
established
brain-computer
interface
(BCI)
in
order
appropriate
methods
or
more
training.
This
paper
reviews
most
recent
research
on
brain-computer-interface-based
non-invasive
systems.
Various
endogenous
exogenous
methods,
advantages,
limitations,
challenges
are
discussed
proposed.
In
addition,
discusses
between
various
modes
used
severely
paralyzed
locked
surrounding
environment,
particularly
system
utilizing
(induced)
EEG
signals
(such
as
P300
SSVEP).
discussion
reveals
examination
of
collecting
signals,
components,
signal
postprocessing.
Furthermore,
describes
development
natural
strategies,
a
focus
acquisition,
data
processing,
pattern
recognition
algorithms,
control
techniques.
International Journal of e-Collaboration,
Journal Year:
2023,
Volume and Issue:
19(2), P. 1 - 24
Published: Jan. 23, 2023
In
researching
cognitive
or
motor
learning
aspects
of
activity
control,
imagery
(MI)
is
a
widely
used
model.
Research
has
shown
that
training
can
aid
in
memorizing
functions
because
the
functional
associations
it
shares
with
physical
movement.
Because
high
level
contact
these
sports,
players
are
more
likely
to
sustain
finger
injuries.
As
group
machine
techniques,
web
services
designed
solve
AI-related
challenges.
they
modular,
be
easily
integrated
into
any
program,
making
AI
accessible
everyone.
Some
performers
return
play
early
defensive
splinting,
taping,
and
casting
depending
on
damage
position
played.
Other
injuries,
predominantly
necessitating
full
use
their
hand
for
position,
require
extended
rehabilitation
period
lengthy
time
away
from
field.
Therefore,
this
paper
proposes
imagery-based
system
(MIFRS)
sports
injury
rehabilitation.
Frontiers in Neuroscience,
Journal Year:
2021,
Volume and Issue:
15
Published: July 2, 2021
Brain-computer
interfaces
(BCIs)
provide
a
unique
technological
solution
to
circumvent
the
damaged
motor
system.
For
neurorehabilitation,
BCI
can
be
used
translate
neural
signals
associated
with
movement
intentions
into
tangible
feedback
for
patient,
when
they
are
unable
generate
functional
themselves.
Clinical
interest
in
is
growing
rapidly,
as
it
would
facilitate
rehabilitation
commence
earlier
following
brain
damage
and
provides
options
patients
who
partake
traditional
physical
therapy.
However,
substantial
challenges
existing
implementations
have
prevented
its
widespread
adoption.
Recent
advances
knowledge
technology
opportunities
change,
provided
that
researchers
clinicians
using
agree
on
standardisation
of
guidelines
protocols
shared
efforts
uncover
mechanisms.
We
propose
addressing
speed
effectiveness
learning
control
priorities
field,
which
may
improved
by
multimodal
or
multi-stage
approaches
harnessing
more
sensitive
neuroimaging
technologies
early
stages,
before
transitioning
practical,
mobile
implementations.
Clarification
mechanisms
give
rise
improvement
function
an
essential
next
step
towards
justifying
clinical
use
BCI.
In
particular,
quantifying
unknown
contribution
non-motor
recovery
calls
stringent
conditions
experimental
work.
Here
we
contemporary
viewpoint
factors
impeding
scalability
Further,
future
outlook
optimal
design
best
exploit
potential,
practices
research
reporting
findings.
Stroke and Vascular Neurology,
Journal Year:
2022,
Volume and Issue:
7(6), P. 541 - 549
Published: July 19, 2022
Brain-computer
interface
(BCI)
technology
translates
brain
activity
into
meaningful
commands
to
establish
a
direct
connection
between
the
and
external
world.
Neuroscientific
research
in
past
two
decades
has
indicated
tremendous
potential
of
BCI
systems
for
rehabilitation
patients
suffering
from
poststroke
impairments.
By
promoting
neuronal
recovery
damaged
networks,
have
achieved
promising
results
motor,
cognitive,
language
Also,
several
assistive
that
provide
alternative
means
communication
control
severely
paralysed
been
proposed
enhance
patients'
quality
life.
In
this
article,
we
present
perspective
review
recent
advances
challenges
used
ACS Nano,
Journal Year:
2023,
Volume and Issue:
17(24), P. 24487 - 24513
Published: Dec. 8, 2023
Brain–computer
interfaces
(BCIs)
have
garnered
significant
attention
in
recent
years
due
to
their
potential
applications
medical,
assistive,
and
communication
technologies.
Building
on
this,
noninvasive
BCIs
stand
out
as
they
provide
a
safe
user-friendly
method
for
interacting
with
the
human
brain.
In
this
work,
we
comprehensive
overview
of
latest
developments
advancements
material,
design,
application
electrode
technology.
We
also
explore
challenges
limitations
currently
faced
by
BCI
technology
sketch
technological
roadmap
from
three
dimensions:
Materials
Design;
Performances;
Mode
Function.
aim
unite
research
efforts
within
field
technology,
focusing
consolidation
shared
goals
fostering
integrated
development
strategies
among
diverse
array
multidisciplinary
researchers.
Journal of NeuroEngineering and Rehabilitation,
Journal Year:
2022,
Volume and Issue:
19(1)
Published: Sept. 28, 2022
Abstract
Background
Brain–computer
interfaces
(BCI),
initially
designed
to
bypass
the
peripheral
motor
system
externally
control
movement
using
brain
signals,
are
additionally
being
utilized
for
rehabilitation
in
stroke
and
other
neurological
disorders.
Also
called
neurofeedback
training,
multiple
approaches
have
been
developed
link
motor-related
cortical
signals
assistive
robotic
or
electrical
stimulation
devices
during
active
training
with
variable,
but
mostly
positive,
functional
outcomes
reported.
Our
specific
research
question
this
scoping
review
was:
persons
non-progressive
injuries
who
potential
improve
voluntary
control,
which
mobile
BCI-based
methods
demonstrate
associated
improved
Neurorehabilitation
applications?
Methods
We
searched
PubMed,
Web
of
Science,
Scopus
databases
all
steps
from
study
selection
data
extraction
performed
independently
by
at
least
2
individuals.
Search
terms
included:
machine
computer
interfaces,
motor;
however,
only
studies
requiring
a
attempt,
versus
imagery,
were
retained.
Data
included
participant
characteristics,
design
details
outcomes.
Results
From
5109
papers,
139
full
texts
reviewed
23
unique
identified.
All
EEG
and,
except
one,
on
population.
The
most
commonly
reported
Fugl-Meyer
Assessment
(FMA;
n
=
13)
Action
Research
Arm
Test
(ARAT;
6)
then
assess
effectiveness,
evaluate
features,
correlate
doses.
Statistically
functionally
significant
pre-to
post
changes
seen
FMA,
not
ARAT.
did
differ
between
feedback
paradigms.
Notably,
FMA
positively
correlated
dose.
Conclusion
This
confirms
previous
findings
effectiveness
improving
some
evidence
enhanced
neuroplasticity
adults
stroke.
Associative
learning
paradigms
emerged
more
recently
may
be
particularly
feasible
effective
Neurorehabilitation.
More
clinical
trials
pediatric
adult
neurorehabilitation
refine
doses
compare
evidence-based
strategies
warranted.
Frontiers in Human Neuroscience,
Journal Year:
2024,
Volume and Issue:
18
Published: April 3, 2024
In
the
realm
of
motor
rehabilitation,
Brain-Computer
Interface
Neurofeedback
Training
(BCI-NFT)
emerges
as
a
promising
strategy.
This
aims
to
utilize
an
individual’s
brain
activity
stimulate
or
assist
movement,
thereby
strengthening
sensorimotor
pathways
and
promoting
recovery.
Employing
various
methodologies,
BCI-NFT
has
been
shown
be
effective
for
enhancing
function
primarily
upper
limb
in
stroke,
with
very
few
studies
reported
cerebral
palsy
(CP).
Our
main
objective
was
develop
electroencephalography
(EEG)-based
system,
employing
associative
learning
paradigm,
improve
selective
control
ankle
dorsiflexion
CP
potentially
other
neurological
populations.
First,
cohort
eight
healthy
volunteers,
we
successfully
implemented
system
based
on
detection
slow
movement-related
cortical
potentials
(MRCP)
from
EEG
generated
by
attempted
simultaneously
activate
Neuromuscular
Electrical
Stimulation
which
assisted
movement
served
enhance
sensory
feedback
cortex.
Participants
also
viewed
computer
display
that
provided
real-time
visual
range
motion
individualized
target
region
displayed
encourage
maximal
effort.
After
evaluating
several
potential
strategies,
employed
Long
short-term
memory
(LSTM)
neural
network,
deep
algorithm,
detect
intent
prior
onset.
We
then
evaluated
10-session
training
protocol
child
CP.
By
transfer
across
sessions,
could
significantly
reduce
number
calibration
trials
50
20
without
compromising
accuracy,
80.8%
average.
The
participant
able
complete
required
100
per
session
all
10
sessions
post-training
demonstrated
increased
velocity,
walking
speed
step
length.
Based
exceptional
performance,
feasibility
preliminary
effectiveness
CP,
are
now
pursuing
clinical
trial
larger
children
Sensors,
Journal Year:
2024,
Volume and Issue:
24(18), P. 6125 - 6125
Published: Sept. 22, 2024
Brain–computer
interfaces
(BCIs)
are
promising
tools
for
motor
neurorehabilitation.
Achieving
a
balance
between
classification
accuracy
and
system
responsiveness
is
crucial
real-time
applications.
This
study
aimed
to
assess
how
the
duration
of
time
windows
affects
performance,
specifically
false
positive
rate,
optimize
temporal
parameters
MI-BCI
systems.
We
investigated
impact
window
on
employing
Linear
Discriminant
Analysis
(LDA),
Multilayer
Perceptron
(MLP),
Support
Vector
Machine
(SVM)
data
acquired
from
six
post-stroke
patients
external
BCI
IVa
dataset.
EEG
signals
were
recorded
processed
using
Common
Spatial
Patterns
(CSP)
algorithm
feature
extraction.
Our
results
indicate
that
longer
generally
enhance
reduce
positives
across
all
classifiers,
with
LDA
performing
best.
However,
maintain
responsiveness,
practical
applications,
must
be
struck.
The
suggest
an
optimal
1–2
s,
offering
trade-off
performance
excessive
delay
guarantee
responsiveness.
These
findings
underscore
importance
optimization
in
systems
improve
usability
real
rehabilitation
scenarios.