S
UPPLEMENTAL FIGURES
Fig. S
1
–
Supplement to Fig
. 1
–
Across session alignment algorithm
We used
semi
-
automated
intensity
-
based rigid
-
body registration to find the transform from the previous session to the
new imaging plane.
The registration error is shown in the over
l
ay where
green represents the old session (Day
1
) and
magenta
represents the new session (Day 64)
.
Fig. S
2
–
Supplement to Fig.
3,
5
,
and 7
-
Closed
-
loop, real
-
time
decoding of
movement
directions
using
pretrained model only
A)
Performance
for
2
-
direction saccade
decoding using only the pretrained
model
. Same format as in
Fig.
3
.
B)
Performance for 8
-
direction saccade
decoding using only the pretrained
model
.
Same format as in
Fig. 5
.
C)
Performance for 2
-
direction reach
decoding using only the pretrained
model. Same format as in
Fig. 7
.
Fig. S3
–
TCP Communication Architecture for real
-
time fUS
-
BMI
We designed a threaded TCP server in Python
2.7
to receive, parse, and send information between the computer running
the PsychoPy behavior software and the real
-
time fUS
-
BMI computer. Upon queries from
the fUS
-
BMI computer (“fUS
decoder”), this server transferred task information, including task timing and actual movement direction, to a real
-
time
ultrasound system. The client
-
server architecture was specifically designed to prevent data leaks, i.e., t
he actual
movement direction was never transmitted to the fUS
-
BMI until after a successful trial had ended. The TCP server also
received the fUS
-
BMI prediction and passed it to the PsychoPy software when queried. The average server write
-
read
-
parse time
was 31 +/
-
1 (mean ± STD) ms during offline testing
between
two Windows computers on a local area
network
.