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| Line 13: | Line 13: | ||
|   TR=2.047; %the fMRI TR is 2.047 seconds in this example |   TR=2.047; %the fMRI TR is 2.047 seconds in this example | ||
|   t=cell2mat({expinfo.data.timestamp}); |   t=cell2mat({expinfo.data.timestamp}); | ||
|   vols=floor(t/TR)+1; %convert the timestamps into volume numbers | |||
|   cond=double(cell2mat({expinfo.data.conditon})); %what condition is each trial?   |   cond=double(cell2mat({expinfo.data.conditon})); %what condition is each trial?   | ||
|   %p.s., note the typo on "cond'''iton'''" |   %p.s., note the typo on "cond'''iton'''" | ||
|   b=double(cell2mat({expinfo.data.block})); %what block is each trial? |   b=double(cell2mat({expinfo.data.block})); %what block is each trial? | ||
Revision as of 16:56, 11 July 2016
Determine Targets
Classifying Task vs Baseline Blocks
If the goal is simply to distinguish task block from rest periods, use findBlockBoundaries as follows:
b=bookends{1}; %creating targets for first run, so use first set of bookends
s=zeros(1,b(end)); %1 zero for each volume -- default=baseline
for block=1:size(b,1)
 s(b(block,1):b(block,2))=1; %block volumes get a '1'
end
Classifying Task Blocks
If blocks are associated with different tasks to be classified, the schedule vector, s can be created similarly, but with some modification. Here's some examples.
TR=2.047; %the fMRI TR is 2.047 seconds in this example
t=cell2mat({expinfo.data.timestamp});
vols=floor(t/TR)+1; %convert the timestamps into volume numbers
cond=double(cell2mat({expinfo.data.conditon})); %what condition is each trial? 
%p.s., note the typo on "conditon"
b=double(cell2mat({expinfo.data.block})); %what block is each trial?