Some physiological measurements require more human inspection and interaction to be good enough for additional analyses (e.g. in a GSR peak-to-peak analysis). This function allows to visualize parts of a measurement (e.g. GSR data channel) as single curves (e.g. 15-second windows following onsets).

The physiological data must be available as a one of the supported object types, ACQ or NTT. It is fairly simple to store any channel data in a new NTT object, if necessary:

plotcurves_new_ntt.m

% load data from a MAT file
load HPS1344_session1_GSR.mat;
 
% create new NTT
ntt = xff('new:ntt');
 
% store data from mat file in ntt
ntt.Data = data;

Usually, it is best to first resample the data and then filter the channel which is to be examined (potentially into a new channel):

plotcurves_pres1.m

% data is loaded in object gsr
% this resamples all channels, applying cubic/gaussian resampling
% for channel 1 and simple re-indexing for channels 2 through 9
gsr.Resample(100, struct('cubic', [true, false(1, 8)]));
 
% this filters channel 1 into a new channel (10)
% all parameters are default, the post-filtering cut-off is set to 0.5s
gsr.Filter(1, struct('dest', 10, 'post', 0.5));

  plotcurves  - plot different curves and select those of interest
 
  FORMAT:       [sel, varc, spot, sets] = plotcurves(obj [, opts]]);
 
  Input fields:
 
        obj         xff object with plotable data or SxC double data
        opts        optional settings
         .curves    Cx2 cell array with names and a 1x3 double array
                    containing [channelnumber, onset, offset]
                    for multiple curves, the mean will be computed
         .freq      data sampling frequency (if not in object)
         .resfreq   resampling frequency
         .sets      Sx2 cell array with names and lists of curve indices
         .spot      Sx1 cell array with Cx1 X or Cx2 x/y coordinates
         .spotcol   Sx3 RGB color (either [0 .. 255] or [0 .. 1])
         .spotnames Sx1 cell array with strings
         .spottype  Sx1 cell array with either of
                    {'free'}, 'max', 'min', 'minmax'
         .var       1xV struct with fields
           .calc    either of 'dx', {'dy'}, 'mean', 'std', 'var', 'x', 'y'
           .spot    1x1 or 1x2 index into S
           .trans   apply additional transformation to each value, one of
                    {'none'}, 'log', 'log+1', 'sqrt'
         .varnames  
 
  Output fields:
 
        sel         selection (Cx1 boolean array)
        varc        computed variables
        spot        updated spots
        sets        updates sets (new indices)

This function has its own GUI. Once called (with appropriate parameters), the following dialog will open:

The most basic usage is by simply creating as many onsets as required and defining some basic spots:

plotcurves_genericonsets.m

% data is in object gsr and already preprocessed
% onsets are in variable onsets, data in channel 1
curves = cell(numel(onsets), 2);
for cc = 1:numel(onsets)
    curves{cc, 1} = sprintf('Onset %d, t=%.4f', cc, onsets(cc));
    curves{cc, 2} = [1, onsets(cc) - 2, onsets(cc) + 20];
end
 
% we define three spots of interest:
% - onset (time of stimulus),
% - delay of response (minimum before peak),
% - and peak of response (amplitude)
spotnames = {'onset', 'delay', 'peak'};
spottype = {'onset', 'min', 'max'};
 
% the spots are simply defined as the onsets (and added values)
spots = {onsets, onsets + 3, onsets + 7};
 
% then we define some variables of interest:
% - Latency (defined as x-diff between onset and delay)
% - Response time (defined as x-diff between delay and peak)
% - Base amplitude (defined as y-value at delay)
% - Peak amplitude (defined as y-value at peak)
% - Peak-to-peak response amplitude (defined as y-diff between delay and peak)
% additionally, we define that for the response amplitude, the log+1 transform
% should be applied after we're done
vars = struct( ...
    'name', {'Latency', 'Resp. time', 'Base ampl.', 'Peak ampl.', 'Resp. ampl.'}, ...
    'calc', {'dx', 'dx', 'y', 'y', 'dy'}, ...
    'spot', {[1,2], [2,3], [2], [3], [2,3]}, ...
    'trans', {'none', 'none', 'none', 'none', 'log+1'});
 
% options:
% - data is now in channel 10 (filtered!)
% - original data is in channel 1 (pre-filter)
% - variables and spots as defined
pcopt = struct( ...
    'var',       vars, ...
    'spot',      {spot}, ...
    'spotnames', {spotnames}, ...
    'spottype',  {spottype});
 
% now we call plotcurves
plotcurves(gsr, pcopts);

If the data contains onset/stimulus information, the plotcurves function has implemented functionality to detect peaks (min/max):

plotcurves_withonsets.m

% data is in object gsr and already preprocessed
% we define three spots of interest:
% - onset (time of stimulus),
% - delay of response (minimum before peak),
% - and peak of response (amplitude)
spotnames = {'onset', 'delay', 'peak'};
spottype = {'onset', 'min', 'max'};
 
% the spot values are set to empty arrays with size 0x2!
% this tells plotcurves to autodetect...
spot = {zeros(0,2), zeros(0,2), zeros(0, 2)};
 
% re-using the variables definition from above, options:
% - data is now in channel 10 (filtered!)
% - original data is in channel 1 (pre-filter)
% - onsets are coded in channel 8 (0Volts nothing, 5 Volts boxes)
% - variables and spots as defined
pcopt = struct( ...
    'dchannel', 10, ...
    'odchannel', 1, ...
    'ochannel',  8, ...
    'var',       vars, ...
    'spot',      {spot}, ...
    'spotnames', {spotnames}, ...
    'spottype',  {spottype});
 
% now we call plotcurves
plotcurves(gsr, pcopts);