Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(4), P. 456 - 456
Published: Feb. 13, 2025
Background/Objectives:
The
following
systematic
review
integrates
neuroimaging
techniques
with
deep
learning
approaches
concerning
emotion
detection.
It,
therefore,
aims
to
merge
cognitive
neuroscience
insights
advanced
algorithmic
methods
in
pursuit
of
an
enhanced
understanding
and
applications
recognition.
Methods:
study
was
conducted
PRISMA
guidelines,
involving
a
rigorous
selection
process
that
resulted
the
inclusion
64
empirical
studies
explore
modalities
such
as
fMRI,
EEG,
MEG,
discussing
their
capabilities
limitations
It
further
evaluates
architectures,
including
neural
networks,
CNNs,
GANs,
terms
roles
classifying
emotions
from
various
domains:
human-computer
interaction,
mental
health,
marketing,
more.
Ethical
practical
challenges
implementing
these
systems
are
also
analyzed.
Results:
identifies
fMRI
powerful
but
resource-intensive
modality,
while
EEG
MEG
more
accessible
high
temporal
resolution
limited
by
spatial
accuracy.
Deep
models,
especially
CNNs
have
performed
well
emotions,
though
they
do
not
always
require
large
diverse
datasets.
Combining
data
behavioral
features
improves
classification
performance.
However,
ethical
challenges,
privacy
bias,
remain
significant
concerns.
Conclusions:
has
emphasized
efficiencies
detection,
technical
were
highlighted.
Future
research
should
integrate
advances,
establish
innovative
enhance
system
reliability
applicability.
Language: Английский
From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications
Brain Sciences,
Journal Year:
2025,
Volume and Issue:
15(3), P. 220 - 220
Published: Feb. 20, 2025
Background/Objectives:
This
systematic
review
presents
how
neural
and
emotional
networks
are
integrated
into
EEG-based
emotion
recognition,
bridging
the
gap
between
cognitive
neuroscience
practical
applications.
Methods:
Following
PRISMA,
64
studies
were
reviewed
that
outlined
latest
feature
extraction
classification
developments
using
deep
learning
models
such
as
CNNs
RNNs.
Results:
Indeed,
findings
showed
multimodal
approaches
practical,
especially
combinations
involving
EEG
with
physiological
signals,
thus
improving
accuracy
of
classification,
even
surpassing
90%
in
some
studies.
Key
signal
processing
techniques
used
during
this
process
include
spectral
features,
connectivity
analysis,
frontal
asymmetry
detection,
which
helped
enhance
performance
recognition.
Despite
these
advances,
challenges
remain
more
significant
real-time
processing,
where
a
trade-off
computational
efficiency
limits
implementation.
High
cost
is
prohibitive
to
use
real-world
applications,
therefore
indicating
need
for
development
application
optimization
techniques.
Aside
from
this,
obstacles
inconsistency
labeling
emotions,
variation
experimental
protocols,
non-standardized
datasets
regarding
generalizability
recognition
systems.
Discussion:
These
developing
adaptive,
algorithms,
integrating
other
inputs
like
facial
expressions
sensors,
standardized
protocols
elicitation
classification.
Further,
related
ethical
issues
respect
privacy,
data
security,
machine
model
biases
be
much
proclaimed
responsibly
apply
research
on
emotions
areas
healthcare,
human–computer
interaction,
marketing.
Conclusions:
provides
critical
insight
suggestions
further
field
toward
robust,
scalable,
applications
by
consolidating
current
methodologies
identifying
their
key
limitations.
Language: Английский
Neurodevelopmental Pathways from Temperamental Fear to Anxiety
Current topics in behavioral neurosciences,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
Language: Английский
Machine Learning Models for Predicting Emotional Valence from Brain Activity and Physiological Responses
IFMBE proceedings,
Journal Year:
2025,
Volume and Issue:
unknown, P. 461 - 469
Published: Jan. 1, 2025
Language: Английский
Perceiving Etruscan Art: AI and Visual Perception
Humans,
Journal Year:
2024,
Volume and Issue:
4(4), P. 409 - 429
Published: Dec. 18, 2024
This
research
project
is
aimed
at
exploring
the
cognitive
and
emotional
processes
involved
in
perceiving
Etruscan
artifacts.
The
case
study
Sarcophagus
of
Spouses
National
Museum
Rome,
one
most
important
masterpieces
pre-Roman
art.
utilized
AI
eye-tracking
technology
to
analyze
how
viewers
engaged
with
Spouses,
revealing
key
patterns
visual
attention
engagement.
OpenAI,
ChatGPT-4
(accessed
on
12
October
2024)
was
used
conjunction
Colab–Python
order
elaborate
all
spreadsheets
data
arising
from
recording.
results
showed
that
primarily
focused
central
figures,
especially
their
faces
hands,
indicating
a
high
level
interest
human
elements
artifact.
longer
fixation
duration
these
features
suggest
find
them
particularly
engaging,
which
likely
due
detailed
craftsmanship
symbolic
significance.
also
highlighted
specific
gaze
patterns,
such
as
diagonal
scanning
across
sarcophagus,
reflects
composition’s
ability
guide
viewer
strategically.
indicate
focus
centers
elements,
suggesting
hold
both
esthetic
Language: Английский