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.

  Usage:
  ------
    

    plugout_drive -com '3DSVM [options]'

    
  Examples:
  ---------

      Training:
      plugout_drive -com '3DSVM -rt_train -trainlabels run1_categories.1D ...
                                -mask mask+orig -model model_run1'

      Testing:
      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).

  Options:
  --------
  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



Reference:
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.