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Improved accuracy using recursive bayesian estimation based language model fusion in ERP-based BCI typing systems.
[locked-in syndrome]
RSVP
Keyboard
â„¢
is
an
electroencephalography
(
EEG
)
based
brain
computer
interface
(
BCI
)
typing
system
,
designed
as
an
assistive
technology
for
the
communication
needs
of
people
with
locked-
in
syndrome
(
LIS
)
.
It
relies
on
rapid
serial
visual
presentation
(
RSVP
)
and
does
not
require
precise
eye
gaze
control
.
Existing
BCI
typing
systems
which
uses
event
related
potentials
(
ERP
)
in
EEG
suffer
from
low
accuracy
due
to
low
signal-
to
-noise
ratio
.
Henceforth
,
RSVP
Keyboard
â„¢
utilizes
a
context
based
decision
making
via
incorporating
a
language
model
,
to
improve
the
accuracy
of
letter
decisions
.
To
further
improve
the
contributions
of
the
language
model
,
we
propose
recursive
bayesian
estimation
,
which
relies
on
non-committing
string
decisions
,
and
conduct
an
offline
analysis
,
which
compares
it
with
the
existing
naïve
bayesian
fusion
approach
.
The
results
indicate
the
superiority
of
the
recursive
bayesian
fusion
and
in
the
next
generation
of
RSVP
Keyboard
â„¢
we
plan
to
incorporate
this
new
approach
.