Mind Reading: Difference between revisions

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  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});
  t=t/TR; %convert the timestamps into volume numbers
  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 15:56, 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?