kitab-eReader/readerEpochFeedbackSignals.m at master · suhasvijayakumar/kitab-eReader · GitHub

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function [testdata,testevents]=readerContFeedbackSignals(clsfr,varargin)

% apply classifier to data after the indicated events
%
%  [testdata,testevents]=imContFeedbackSignals(clsfr,varargin)
%
% Inputs:
%  clsfr  -- [struct] a classifier structure as returned by train_ersp_clsfr
% Outputs:
%  testdata   -- [struct 1xN] data classifier was applied to in each epoch
%  testevents -- [struct 1xN] event which triggered the classifier to be applied
% Options:
%  buffhost, buffport, hdr
%  startSet      -- {2 x 1} cell array of {event.type event.value} to match to start getting data to
%                   apply the classifier to.                                   ({'stimulus.target'})
%  endType, endValue  -- event type and value to match to stop giving feedback
%  trlen_ms/samp -- [int] length of trial to apply classifier to               ([])
%                     if empty, then = windowFn size used in the classifier training

opts=struct('buffhost','localhost','buffport',1972,'hdr',[],...
            'startSet',{'stimulus.target'},...
            'endType','stimulus.test','endValue','end','verb',0,...
            'trlen_ms',[],'trlen_samp',[],...
            'alpha',[],'timeout_ms',1000);

        [opts,varargin]=parseOpts(opts,varargin);
trlen_samp=opts.trlen_samp;

if ( isempty(trlen_samp) )
    trlen_samp=0;
    if ( ~isempty(opts.trlen_ms) )
        if(~isempty(opts.hdr))
            fs=opts.hdr.fsample;
        else
            hdr=buffer('get_hdr',opts.buffhost,opts.buffport);
            fs=hdr.fsample;
        end;
        trlen_samp = opts.trlen_ms /1000 * fs;
    end

  % ensure is at least a big as the welch window size!
    if ( isfield(clsfr,'windowFn') ) % est from size welch window function
        trlen_samp=max(trlen_samp,size(clsfr.windowFn,2));
    end
end

testdata={};
testevents={}; %N.B. cell array to avoid expensive mem-realloc during execution loop
state=[];
endTest=false;
nepochs=0;

while ( ~endTest )
    % wait for data to apply the classifier to
    [data,devents,state]=buffer_waitData(opts.buffhost,opts.buffport,state,'startSet',opts.startSet,'trlen_samp',trlen_samp,'exitSet',{'data' {opts.endType}},'verb',opts.verb);

    % process these events
    for ei=1:numel(devents)
        if ( matchEvents(devents(ei),opts.endType,opts.endValue) ) % end training
            if ( opts.verb>0 )
                fprintf('Got end feedback event\n');
            end;
            endTest=true;
        elseif ( matchEvents(devents(ei),opts.startSet) ) % flash, apply the classifier
        if ( opts.verb>0 )
            fprintf('Processing event: %s',ev2str(devents(ei)));
        end;
        nepochs=nepochs+1;
        if ( nargout>0 )
            testdata{nepochs}=data(ei);
            testevents{nepochs}=devents(ei);
        end;

        % apply classification pipeline to this events data
        [f,fraw,p]=buffer_apply_ersp_clsfr(data(ei).buf,clsfr);
        sendEvent('classifier.prediction',f,devents(ei).sample);
        if ( opts.verb>0 )
            fprintf('Sent classifier prediction = %s.\n',sprintf('%g ',f));
        end;
        elseif ( opts.verb>0 )
            fprintf('Unmatched event : %s\n',ev2str(devents(ei)));
        end;
    end
end % devents


if( nargout>0 )
    testdata=cat(1,testdata{:});
    testevents=cat(1,testevents{:});
end;

end % feedback phase