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Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface.
[locked-in syndrome]
The
bedside
detection
of
potential
awareness
in
patients
with
disorders
of
consciousness
(
DOC
)
currently
relies
only
on
behavioral
observations
and
tests
;
however
,
the
misdiagnosis
rates
in
this
patient
group
are
historically
relatively
high
.
In
this
study
,
we
proposed
a
visual
hybrid
brain
-computer
interface
(
BCI
)
combining
P
300
and
steady-
state
evoked
potential
(
SSVEP
)
responses
to
detect
awareness
in
severely
brain
injured
patients
.
Four
healthy
subjects
,
seven
DOC
patients
who
were
in
a
vegetative
state
(
VS
,
n
=
4
)
or
minimally
conscious
state
(
MCS
,
n
=
3
)
,
and
one
locked-
in
syndrome
(
LIS
)
patient
attempted
a
command-following
experiment
.
In
each
experimental
trial
,
two
photos
were
presented
to
each
patient
;
one
was
the
patient
's
own
photo
,
and
the
other
photo
was
unfamiliar
.
The
patients
were
instructed
to
focus
on
their
own
or
the
unfamiliar
photos
.
The
BCI
system
determined
which
photo
the
patient
focused
on
with
both
P
300
and
SSVEP
detections
.
Four
healthy
subjects
,
one
of
the
4
VS
,
one
of
the
3
MCS
,
and
the
LIS
patient
were
able
to
selectively
attend
to
their
own
or
the
unfamiliar
photos
(
classification
accuracy
,
66
-
100
%
)
.
Two
additional
patients
(
one
VS
and
one
MCS
)
failed
to
attend
the
unfamiliar
photo
(
50
-
52
%
)
but
achieved
significant
accuracies
for
their
own
photo
(
64
-
68
%
)
.
All
other
patients
failed
to
show
any
significant
response
to
commands
(
46
-
55
%
)
.
Through
the
hybrid
BCI
system
,
command
following
was
detected
in
four
healthy
subjects
,
two
of
7
DOC
patients
,
and
one
LIS
patient
.
We
suggest
that
the
hybrid
BCI
system
could
be
used
as
a
supportive
bedside
tool
to
detect
awareness
in
patients
with
DOC
.
Diseases
Validation
Diseases presenting
"achieved significant accuracies for their own photo"
symptom
locked-in syndrome
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