1
2How to configure the 3dsvm plugin for real-time experiments using plugout_drive:
3===============================================================================
4
5  plugout_drive is a command-line program that can be used to drive (control)
6  AFNI (please see README.driver for more details) and allows the user to automate
7  the configuration of the 3dsvm plugin for real-time experiments.
8
9  Using plugout_drive to set up the 3dsvm plugin for real-time experiments is
10  very similar to the usage of the command-line program 3dsvm for off-line
11  SVM analysis. Most of the 3dsvm (and SVM-Light) command-line options can be
12  used in conjunction with plugout_drive.
13
14  Usage:
15  ------
16
17
18    plugout_drive -com '3DSVM [options]'
19
20
21  Examples:
22  ---------
23
24      Training:
25      plugout_drive -com '3DSVM -rt_train -trainlabels run1_categories.1D ...
26                                -mask mask+orig -model model_run1'
27
28      Testing:
29      plugout_drive -com '3DSVM -rt_test -model model_run1+orig ...
30                                -stim_ip 111.222.333.444 -stim_port 5000'
31
32      N.B.: -rt_train and -rt_test serve as flags for the real-time training
33            and testing modes, respectively. No brik or nifti file is
34            specified since it is expected from the scanner (or rtfeedme).
35
36  Options:
37  --------
38  N.B. The plugout_drive options are almost identical to the "normal" 3dsvm usage,
39       (see 3dsvm -help) but restricted to 2-class classification and regression.
40       Coming soon (or someday when asked): multi-class classification
41
42
43
44Reference:
45LaConte, S., Strother, S., Cherkassky, V. and Hu, X. 2005. Support vector
46    machines for temporal classification of block design fMRI data.
47    NeuroImage, 26, 317-329.
48
49Specific to real-time fMRI:
50S. M. LaConte. (2011). Decoding fMRI brain states in real-time. NeuroImage, 56:440-54.
51S. M. LaConte, S. J. Peltier, and X. P. Hu. (2007). Real-time fMRI using brain-state classification. Hum Brain Mapp, 208:1033–1044.
52
53
54Please also consider to reference:
55T. Joachims, Making Large-Scale SVM Learning Practical.
56     Advances in Kernel Methods - Support Vector Learning,
57     B. Schoelkopf and C. Burges and A. Smola (ed.), MIT Press, 1999.
58
59RW Cox. AFNI: Software for analysis and visualization of
60    functional magnetic resonance neuroimages.
61    Computers and Biomedical Research, 29:162-173, 1996.
62
63