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playground.m
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%
% for as=2:10
% eegcleantrial(as).data=cleaneeg(:,(onoffsettimes(1,as)-500):onoffsettimes(2,as));
%
% end
%
% chlist=[18:26];
% chlist=[1:120];
% chlist=20;
% count=0;
% colo=hsv(length(chlist));figure('Name','trial plots');
% for ch=chlist
% count=count+1;
% plot(eegcleantrial(5).data(ch,:),'Color',colo(count,:));hold on;
% end
% %% --------- extracting inter trial time across all wues
% intertrltm=[];starttm=[];
% for i=1:6
% nosess=length(kin_data(i).sessno);
% for j=1:nosess
% onset=kin_data(i).sessno(j).data.Onset_event;
% offset=kin_data(i).sessno(j).data.Offset_event;
% diffs=onset(2:end)-offset(1:end-1);
% intertrltm=[intertrltm;[repmat([i,j],length(diffs),1),diffs(:)]];
% starttm=[starttm;[[i,j],kin_data(i).sessno(j).data.Onset_event(1,1)]];
% end
% end
%
% % checking starttime is how many seconds after the end of the
% % TENS spikes so as to get a pre onset time reading.
% startvals=xlsread('C:\Users\PROVOST\Google Drive (sxs1790@case.edu)\TimeOffsetData.xlsx','B28:E33');
% startvals=startvals';
% startvals=startvals(:);
% startvals(startvals==0)=[];
% diffsrtval=-startvals+(starttm(:,3))*1000;
% find(diffsrtval<1750)
%
% matout=[];
% for i=1:6
% nosess=length(kin_data(i).sessno);
% for j=1:nosess
% matout=[matout;i,j,any(double(new_onsetimes(i).sessno(j).times(1:end-1))-double(events_onoffsetkin_data(i).sessno(j).data(1,:)))];
% end
% end
% % checking endtime (TENS start) is how many seconds after the end of the
% % last trial so as to get some time from which a baseline can be extracted
% endvals=xlsread('C:\Users\PROVOST\Google Drive (sxs1790@case.edu)\TimeOffsetData.xlsx','H28:K33');
% endvals=endvals';
% endvals=endvals(:);
% endvals(endvals==0)=[];
% % diffsrtval=-startvals+(starttm(:,3))*1000;
% % find(diffsrtval<1750)
%
% matout1=[];c=0;
% for i=1:6
% nosess=length(kin_data(i).sessno);
% for j=1:nosess
% c=c+1;
% tend=int32(1000*kin_data(i).sessno(j).data.Offset_event(end));
% matout1=[matout1;i,j,(endvals(c)-tend)];
% end
% end
% %% 2) band specific ------------
% alpha=[8 12];beta=[13 30];
% bands=[alpha; beta];
%
% for wueno=1:1
% %--- taking the mean of spectrograms of the 3 movement epocs
% sz=size(spectdata_SUBEPOC(wueno).data);
% sz(1,3)=sz(1,3)-2;
% NData=zeros(sz);
% NData(:,:,2,:,:)=mean(spectdata_SUBEPOC(wueno).data(:,:,2:end,:,:),3);
% NData(:,:,1,:,:)=(spectdata_SUBEPOC(wueno).data(:,:,1,:,:));
% pvalbandtemp=zeros(2,size(NData,2));
% sigValbandtemp=zeros(size(pvalbandtemp));
% freqs=size(spectdata_SUBEPOC(wueno).data,2);
% freqs=[1:freqs].*500/freqs;
% bandid=arrayfun(@(x) dsearchn(freqs',x),bands);
%
% for chans=1:length(goodelectrodes)
% for freq=1:size(bands,2)
% dat1=mean(NData(chans,bandid(freq,1):bandid(freq,2),1,:,:),2);
% dat1=reshape(dat1(chans,:,1,:,:),size(NData,4),size(NData,5));
% dat1=dat1(:);
% dat2=mean(NData(chans,bandid(freq,1):bandid(freq,2),2,:,:),2);
% dat2=reshape(dat2(chans,:,2,:,:),size(NData,4),size(NData,5));
% dat2=dat2(:);
% pvalbandtemp(chans,freq)=anova1([dat1,dat2],[1 2],'off');% 2 columns. 1st is alpha, 2nd beta.
% sigValbandtemp(chans,freq)=(pvalbandtemp(chans,freq)<0.05);
% end
% end
% pval_band(wueno).data=pvalbandtemp;
% sigVal_band(wueno).data=sigValbandtemp;
% end
%
%
%%
% for i=1:6
% spectsz=size(spectdata_SUBEPOC(i).data);
% temp=reshape(spectdata_SUBEPOC(i).data,spectsz(1),prod(spectsz(2:end)));
% maxval=zeros(spectsz(1),prod(spectsz(2:end)));
% currdatadB=zeros(size(temp));
%
% for j=1:prod(spectsz(3:end))
% currdata=temp(:, ((j-1)*spectsz(2)+1):((j)*spectsz(2)-1));
% maxval(:,j)=max(currdata,[],2);
% currdatadB(:,((j-1)*size(currdata,2)+1):((j)*size(currdata,2)))=...
% 10*log10(bsxfun(@rdivide,currdata,maxval(:,j)));
% end
% spectdata_SUBEPOC_dB(i).data=reshape(currdatadB,spectsz);
%
% end
%% diffstraval modification to have sess and trial info in the array
% counter=0;
% diffstrvalappend=zeros(size(diffsrtval,2),2);
% for i=1:6
% leng=length(TimeDelay(i).Tdiff);
% for j=1:leng
% counter=counter+1;
% diffstrvalappend(counter,:)=[i,j];
% end
% end
% diffsrtval=[diffstrvalappend, diffsrtval];
%% 2) band specific ------------
alpha=[8 12];beta=[13 30];
bands=[alpha; beta];
spectdata_SUBEPOC1=spectdata_SUBEPOC_norm;
for wueno=currwue
%--- taking the mean of spectrograms of the 3 movement epocs
sz=size(spectdata_SUBEPOC1(wueno).data);
sz(1,3)=sz(1,3)-2;% since one is rest and other is movement
NData=zeros(sz);
NData(:,:,2,:,:)=nanmean(spectdata_SUBEPOC1(wueno).data(:,:,[2 3 4],:,:),3);% avg of last 3 epocs of movement
NData(:,:,1,:,:)=(spectdata_SUBEPOC1(wueno).data(:,:,1,:,:));
pvalbandtemp=zeros(sz(1),size(bands,2));pvaltemp_anova=pvalbandtemp;
sigValbandtemp=zeros(size(pvalbandtemp));
sigValtemp_anova=sigValbandtemp;
moreORless=zeros(size(pvalbandtemp));
freqs=size(spectdata_SUBEPOC1(wueno).data,2);
freqs=[1:freqs].*500/freqs;
bandid=arrayfun(@(x) dsearchn(freqs',x),bands);
for chans=1:sz(1)
for freq=1:size(bands,2)
dat1=nanmean(NData(chans,bandid(freq,1):bandid(freq,2),1,:,:),2);
dat1=reshape(dat1,size(NData,4),size(NData,5));
dat1=dat1(:);dat1(isnan(dat1))=[];
dat2=nanmean(NData(chans,bandid(freq,1):bandid(freq,2),2,:,:),2);
dat2=reshape(dat2,size(NData,4),size(NData,5));
dat2=dat2(:);dat2(isnan(dat2))=[];
%[~,pvalbandtemp(chans,freq)]=ttest(dat1,dat2);
pvalbandtemp(chans,freq)=permutation_test(dat1,dat2);
pvaltemp_anova(chans,freq)=anova1([dat1,dat2],[1 2],'off');
sigValbandtemp(chans,freq)=(pvalbandtemp(chans,freq)<0.05);
sigValtemp_anova(chans,freq)=(pvaltemp_anova(chans,freq)<0.05);
% pvalbandtemp(chans,freq)=anova1([dat1,dat2],[1 2],'off');% 2 columns. 1st is alpha, 2nd beta.
% sigValbandtemp(chans,freq)=(pvalbandtemp(chans,freq)<0.05);
moreORless(chans,freq)=(nanmean(dat1)>nanmean(dat2));% baseline should have more power when significant
end
end
pval_band_norm_anova(wueno).data=[pvaltemp_anova goodelectrodes' moreORless];
sigVal_band_norm_anova(wueno).data=[sigValtemp_anova goodelectrodes' moreORless];
pval_band_norm(wueno).data=[pvalbandtemp goodelectrodes' moreORless];
sigVal_band_norm(wueno).data=[sigValbandtemp goodelectrodes' moreORless];
end
clearvars NData pvalbandtemp sigValbandtemp freqs dat1 dat2 pvalbandtemp sigValbandtemp moreORless bandid