<p>Designing
processors
for
implantable
closed-loop
neuromodulation
systems
presents
a
formidable
challenge
due
to
the
constrained
operational
environment,
requiring
low
latency
and
high
energy
efficiency.</p>
<p>Previous
benchmarks
have
provided
limited
insights
into
efficiency
latency.
This
paper,
however,
introduces
algorithmic
metrics
that
capture
potential
limitations
of
neural
decoders
intra-cortical
brain-computer
interfaces
in
context
hardware
constraints.
study
common
decoding
methods
predicting
primate’s
finger
kinematics
from
motor
cortex,
explores
suitability
compute
decoding.
The
finds
ANN-based
provide
superior
accuracy,
many
operations
decode
signals
effectively.
Spiking
networks
emerge
as
solution,
bridging
this
gap
by
achieving
competitive
performance
within
sub-10ms
while
utilizing
fraction
computational
resources.</p>
<p>These
distinctive
advantages
neuromorphic
spiking
networks,
positions
them
highly
suitable
challenging
environment
modulation.
Their
capacity
balance
accuracy
offers
immense
reshaping
landscape
decoders,
fostering
greater
understanding,
opening
new
frontiers
intracortical
human-machine
interaction.</p>
Cyborg and Bionic Systems,
Год журнала:
2025,
Номер
6
Опубликована: Янв. 1, 2025
Primates
possess
a
more
developed
central
nervous
system
and
higher
level
of
intelligence
than
rodents.
Detecting
modulating
deep
brain
activity
in
primates
enhances
our
understanding
neural
mechanisms,
facilitates
the
study
major
diseases,
enables
brain–computer
interactions,
supports
advancements
artificial
intelligence.
Traditional
imaging
methods
such
as
magnetic
resonance
imaging,
positron
emission
computed
tomography,
scalp
electroencephalogram
are
limited
spatial
resolution.
They
cannot
accurately
capture
signals
from
individual
neurons.
With
progress
microelectromechanical
systems
other
micromachining
technologies,
single-neuron
detection
stimulation
technology
rodents
based
on
microelectrodes
has
made
important
progress.
However,
compared
with
rodents,
human
nonhuman
have
larger
volume
that
needs
deeper
implantation
depth,
test
object
safety
device
preparation
requirements.
Therefore,
high-resolution
devices
suitable
for
long-term
brains
urgently
needed.
This
paper
reviewed
electrode
array
used
electrophysiological
electrochemical
detections
primates’
brains.
The
research
recording
technologies
was
introduced
perspective
type
structures,
their
potential
value
neuroscience
clinical
disease
treatments
discussed.
Finally,
it
is
speculated
future
electrodes
will
lot
room
development
terms
flexibility,
high
resolution,
brain,
throughput.
improvements
forms
process
expand
activities,
bring
new
opportunities
challenges
further
neuroscience.
Neural
signal
degradation
poses
a
significant
challenge
in
maintaining
stable
performance
when
decoding
motor
tasks
using
multiunit
activity
(MUA)
and
local
field
potential
(LFP)
signals
the
implantable
brain
machine
interface
(iBMI)
applications.
Effective
methods
for
imputing
degraded
or
missing
are
essential
to
restore
neural
integrity,
thereby
improving
accuracy
system
robustness
over
long-term
recordings
with
fluctuating
quality.
This
study
introduces
confidence-weighted
Bayesian
linear
regression
(CW-BLR)
approach
impute
affected
by
degradation,
enhancing
consistency
of
decoding.
The
CW-BLR
was
compared
traditional
methods—mean
imputation
(Mean-imp)
Gaussian-mixture-model-based
expectation–maximization
(GMM-EM)—using
kernel-sliced
inverse
(kSIR)
decoder
evaluate
outcomes.
Four
Wistar
rats
were
trained
perform
forelimb-reaching
task
while
(MUA
LFPs)
recorded
27
days.
imputed
during
days
8–27.
Decoding
evaluated
kSIR
Mean-imp
GMM-EM.
demonstrated
superior
effectively
preserving
both
temporal
spatial
dependencies
within
signals.
CW-BLR-imputed
data
significantly
improved
methods,
showing
consistently
higher
performance,
particularly
quality
from
period.
offers
robust
effective
framework
iBMI
applications,
addressing
challenges
accurate
prolonged
recordings.
By
utilizing
confidence-based
metrics,
surpasses
providing
across
scenarios.
The Journal of Physical Chemistry Letters,
Год журнала:
2025,
Номер
unknown, С. 5312 - 5320
Опубликована: Май 20, 2025
To
emulate
the
tactile
perception
of
human
skin,
integration
sensors
with
neuromorphic
devices
has
emerged
as
a
promising
approach
to
achieve
near-sensor
information
processing.
Here,
we
present
monolithic
electronic
device
that
seamlessly
integrates
and
computing
functionalities
within
single
architecture,
synaptic
plasticity
directly
tunable
by
inputs.
This
unique
capability
stems
from
our
engineered
structure
employing
SnO2
nanowires
conductive
channel
coupled
pressure-sensitive
chitosan
layer
ionic
gating
layer.
The
demonstrates
pressure-dependent
memory
retention
learning
behaviors,
effectively
mimicking
enhanced
cognitive
functions
observed
in
humans
under
stressful
conditions.
Furthermore,
integrated
design
exhibits
potential
for
implementing
bioinspired
systems
requiring
adaptive
Frontiers in Neuroscience,
Год журнала:
2025,
Номер
19
Опубликована: Май 27, 2025
Electroencephalography
(EEG)
holds
immense
potential
for
decoding
complex
brain
patterns
associated
with
cognitive
states
and
neurological
conditions.
In
this
paper,
we
propose
an
end-to-end
framework
EEG
classification
that
integrates
power
spectral
density
(PSD)
visibility
graph
(VG)
features
together
deep
learning
(DL)
techniques.
Our
offers
a
holistic
approach
capturing
both
frequency-domain
characteristics
temporal
dynamics
of
signals.
We
evaluate
four
DL
architectures,
namely
multilayer
perceptron
(MLP),
long
short-term
memory
(LSTM)
networks,
InceptionTime
ChronoNet,
applied
to
several
datasets
in
different
experimental
Results
demonstrate
the
efficacy
our
accurately
classifying
data,
particular
when
using
VG
features.
also
shed
new
light
on
relative
strengths
limitations
feature
extraction
methods
architectures
context
classification.
work
contributes
advancing
analysis
facilitating
development
more
accurate
reliable
EEG-based
systems
neuroscience
beyond.
The
full
code
research
is
available
https://github.com/asmab89/VisibilityGraphs.git
.
Abstract
Advanced
technologies
that
can
establish
intimate,
long‐lived
functional
interfaces
with
neural
systems
have
attracted
increasing
interest
due
to
their
wide‐ranging
applications
in
neuroscience,
bioelectronic
medicine,
and
the
associated
treatment
of
neurodegenerative
diseases.
A
critical
challenge
significance
remains
development
electronic
platforms
offer
conformal
contact
soft
brain
tissue
for
sensing
or
stimulation
activities
chronically
stable
operation
vivo,
at
scales
range
from
cellular‐level
resolution
macroscopic
areas.
This
review
summarizes
recent
advances
this
field,
an
emphasis
on
use
demonstrated
concepts,
constituent
materials,
engineered
designs,
system
integration
address
current
challenges.
The
article
begins
overview
unique
form
factors,
ranging
filamentary
probes
sheets
three‐dimensional
frameworks
alleviating
mechanical
mismatch
between
interface
materials
tissues.
Next,
active
which
utilize
inorganic/organic
semiconductor‐enabled
devices
are
reviewed,
highlighting
various
working
principles
recording
mechanisms
including
capacitively
conductively
coupled
enabled
by
high
transistor
matrices
spatiotemporal
resolution.
subsequent
section
presents
approaches
biological
multiplexed
addressing,
local
amplification
multimodal
high‐channel‐count
large‐scale
a
safe
fashion
provides
multi‐decade
performance
both
animal
models
human
subjects.
summarized
will
guide
future
direction
technology
provide
basis
next‐generation
chronic
high‐performance
operation.
Sovremennye tehnologii v medicine,
Год журнала:
2024,
Номер
16(1), С. 78 - 78
Опубликована: Фев. 28, 2024
Brain-computer
interfaces
allow
the
exchange
of
data
between
brain
and
an
external
device,
bypassing
muscular
system.
Clinical
studies
invasive
brain-computer
interface
technologies
have
been
conducted
for
over
20
years.
During
this
time,
there
has
a
continuous
improvement
approaches
to
neuronal
signal
processing
in
order
improve
quality
control
devices.
Currently,
with
intracortical
implants
completely
paralyzed
patients
robotic
limbs
self-service,
use
computer
or
tablet,
type
text,
reproduce
speech
at
optimal
speed.
Studies
regularly
provide
new
fundamental
on
functioning
central
nervous
In
recent
years,
breakthrough
discoveries
achievements
annually
made
sphere.
This
review
analyzes
results
clinical
experiments
implants,
provides
information
stages
technology
development,
its
main
achievements.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 99469 - 99498
Опубликована: Янв. 1, 2024
This
paper
introduces
Matrix-Valued
Neural
Coordinated
Federated
Deep
Extreme
Machine
Learning,
a
novel
approach
for
enhancing
health
prediction
and
intrusion
detection
on
the
Internet
of
Healthcare
Things
(IoHT).
By
leveraging
Learning
(ML),
blockchain,
Intrusion
Detection
Systems
(IDS),
this
technique
ensures
security
medical
data
while
enabling
predictive
analytics.
The
IoHT,
characterized
by
its
vast
network
interconnected
devices,
poses
significant
challenges
in
confidentiality.
However,
integration
proposed
empowers
healthcare
systems
to
proactively
address
these
concerns
patient
outcomes
reducing
costs.
Smart
healthcare,
enabled
ML
is
revolutionizing
5.0.
may
employ
IoHTs'
intelligent
interactive
characteristics
using
approach.
Despite
benefits,
aggregation
security,
ownership,
regulatory
compliance
challenges.
(FL)
key
distributed
learning
that
protects
data.
architecture
has
several
unique
benefits
like
1)
it
provides
thorough
examination
incorporation
blockchain
technology
with
FL
5.0;
2)
takes
lead
creating
robust
monitoring
system
utilizes
IDS
identify
prevent
harmful
actions;
3)
development
crucial
elements
means
mutual
neuronal
synchronization
Artificial
Networks
(ANNs)
showcases
pioneering
progress
safeguarding
data;
4)
framework
underwent
empirical
assessment
outperformed
existing
methods
accurately
predicting
disease
prediction,
achieving
an
impressive
efficiency
rate
97.75%
98%
respectively.
represents
major
step
forward
improving
abilities
within
IoHT
ecosystem,
offering
potential
revolutionary
advancements
delivery
care.