PerformExpImagAnalysis.m
% ImageryLatencyLR(varargin)
%% dynamic classification across conditions (Imag)
% Get data (change path to your directory)
load('/Users/tysonaflalo/Dropbox/GitCode/Tyson/ForDistribution/eLifeTactile/ExpImagData.mat')
DC=DynCLass('overwrite',0,'Type','classification');
prefix='BasicAnalysis';
for i=1:length(ExpImagData)
basename=sprintf('%d-%s',i,prefix);
[AnalysisResults(i)]=DC.fit_transform(basename,ExpImagData(i).NeuralData,ExpImagData(i).Labels,ExpImagData(i).Time);
DC.plotAcc({AnalysisResults(i).cvAccuracy},AnalysisResults(i).timeWindow,'date',prefix)
end
%% Collapse across sessions
% Note, this includes imagery and touch to the right and left body sides;
% results in paper are only for imagery and right side.
cvAll=cell(3,3);
for i=1:length(AnalysisResults)
tmp=AnalysisResults(i).cvAccuracy;
for j1=1:3
for j2=1:3
cvAll{j1,j2}(:,:,i)=tmp{j1,j2};
end
end
end
for j1=1:3
for j2=1:3
cvAllMu{j1,j2}=mean(cvAll{j1,j2},3);
end
end
%% Cross condition classification
DC.plotAcc({cvAllMu},round(AnalysisResults(i).timeWindow*10),'basename',prefix);
%% Generalization
plt.fig('units','inches','width',5,'height',5,'font','Arial','fontsize',16);
clear V
for i=1:size(cvAll{j1,j2},3)
V{2}(i,:)=(mean(cvAll{3,3}(18:22,:,i),1));
end
Analyze.plotEventRelatedAverage(V,{'early','late'},'useBootStrap')
ylim([-20 150])
xlim([25 65])
ylim([20 90])
% ylim([-2 2])
plt.hline(33.3,{'k--'})
set(gca, 'XTick',(25:5:65)+3)
tmp=AnalysisResults(1).timeWindow((25:5:65));
tmp(1)=-1;
set(gca, 'XTickLabel',tmp)
ylabel(sprintf('Cross-Validated \n Accuracy'))
xlabel('Time (S)')
plt.vline(38,{'k--'})
title(sprintf('Generalization between \n Cue/Delay and Active Imagery'))
%%