fmri_regress_response

 generates response onset regressors for fmri data
 
   [names,vals] = fmri_regress_response(pinfo,minfo,sess)
 
 regressor to model button pressing
 
 called by fmri_generate_regress
 
 REQUIRES
 
 RETURNS
   names = cell array of six regressor names
   vals = volume X regressor matrix containing motion regressors
 
 FB 2009.11.05

0001 function [names,vals] = fmri_regress_response(pinfo,minfo,sess)
0002 
0003 % generates response onset regressors for fmri data
0004 %
0005 %   [names,vals] = fmri_regress_response(pinfo,minfo,sess)
0006 %
0007 % regressor to model button pressing
0008 %
0009 % called by fmri_generate_regress
0010 %
0011 % REQUIRES
0012 %
0013 % RETURNS
0014 %   names = cell array of six regressor names
0015 %   vals = volume X regressor matrix containing motion regressors
0016 %
0017 % FB 2009.11.05
0018 
0019 % init output vars
0020 names = {'response'};
0021 vals = [];
0022 
0023 pc = set_var_col_const(pinfo.vars);
0024 respidxs = find(~cellfun(@isempty,pinfo.data{pc.RESP_TIME}));
0025 resp_onsets = [];
0026 for ievt = 1:length(respidxs)
0027   evt = pinfo.data{pc.RESP_TIME}(respidxs(ievt));
0028   evt_onset = pinfo.data{pc.RUN_REL_TIME}(respidxs(ievt));
0029   for ion=1:length(evt{1})
0030     onset = evt{1}(ion) + evt_onset;
0031     resp_onsets = [resp_onsets; onset];
0032   end
0033 end
0034 onsets = resp_onsets/1000;
0035 
0036 % Durations and amplitude
0037 durs = zeros(size(onsets));
0038 amp  = ones(size(onsets));
0039 
0040 % Now build the regressor
0041 vals = fmri_convolve_regress(onsets,durs,amp,pinfo.TR,pinfo.dt,pinfo.actual_nvol);