AFNI file: README.3dsvm.realtime
How to configure the 3dsvm plugin for real-time experiments using plugout_drive:
plugout_drive is a command-line program that can be used to drive (control)
AFNI (please see README.driver for more details) and allows the user to automate
the configuration of the 3dsvm plugin for real-time experiments.
Using plugout_drive to set up the 3dsvm plugin for real-time experiments is
very similar to the usage of the command-line program 3dsvm for off-line
SVM analysis. Most of the 3dsvm (and SVM-Light) command-line options can be
used in conjunction with plugout_drive.
plugout_drive -com '3DSVM [options]'
plugout_drive -com '3DSVM -rt_train -trainlabels run1_categories.1D ...
-mask mask+orig -model model_run1'
plugout_drive -com '3DSVM -rt_test -model model_run1+orig ...
-stim_ip 111.222.333.444 -stim_port 5000'
N.B.: -rt_train and -rt_test serve as flags for the real-time training
and testing modes, respectively. No brik or nifti file is
specified since it is expected from the scanner (or rtfeedme).
N.B. The plugout_drive options are almost identical to the "normal" 3dsvm usage,
(see 3dsvm -help) but restricted to 2-class classification and regression.
Coming soon (or someday when asked): multi-class classification
LaConte, S., Strother, S., Cherkassky, V. and Hu, X. 2005. Support vector
machines for temporal classification of block design fMRI data.
NeuroImage, 26, 317-329.
Specific to real-time fMRI:
S. M. LaConte. (2011). Decoding fMRI brain states in real-time. NeuroImage, 56:440-54.
S. M. LaConte, S. J. Peltier, and X. P. Hu. (2007). Real-time fMRI using brain-state classification. Hum Brain Mapp, 208:1033–1044.
Please also consider to reference:
T. Joachims, Making Large-Scale SVM Learning Practical.
Advances in Kernel Methods - Support Vector Learning,
B. Schoelkopf and C. Burges and A. Smola (ed.), MIT Press, 1999.
RW Cox. AFNI: Software for analysis and visualization of
functional magnetic resonance neuroimages.
Computers and Biomedical Research, 29:162-173, 1996.
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