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LDA_beamformer_stop_signal.m
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directories = {'/Volumes/PFS01/ME056/ccsrf_files/Stutterers/CC_files/ANALYSIS_OPERC+TRIANG_MAX_ORI_1mm';...
'/Volumes/PFS01/ME056/ccsrf_files/Controls_for_Adult_Stuttering_Study/CC_files/ANALYSIS_OPERC+TRIANG_MAX_ORI_5mm'};
for h=1:length(directories)
cd(directories{h})
files = dir('ccsrf*mat');
for i=1:length(files)
D = spm_eeg_load(files(i).name);
data = ftraw(D);
cfg = [];
cfg.trials = ismember(data.trialinfo,2);
ignore = ft_selectdata(cfg,data);
cfg = [];
cfg.trials = ismember(data.trialinfo,3);
goodstop = ft_selectdata(cfg,data);
cfg = [];
cfg.trials = ismember(data.trialinfo,4);
badstop = ft_selectdata(cfg,data);
cfg = [];
cfg.trials = ismember(data.trialinfo,1);
go = ft_selectdata(cfg,data);
cfg = [];
allstop = ft_appenddata(cfg,goodstop,badstop,ignore);
cfg = [];
av_ignore = ft_timelockanalysis(cfg,ignore);
av_goodstop = ft_timelockanalysis(cfg,goodstop);
av_badstop = ft_timelockanalysis(cfg,badstop);
av_allstop = ft_timelockanalysis(cfg,allstop);
av_go = ft_timelockanalysis(cfg,go);
GFP_av_ignore = ft_globalmeanfield([],av_ignore);
GFP_av_goodstop = ft_globalmeanfield([],av_goodstop);
GFP_av_badstop = ft_globalmeanfield([],av_badstop);
GFP_av_go = ft_globalmeanfield([],av_go);
GFP_av_allstop = ft_globalmeanfield([],av_allstop);
%Get channel layout for plotting
cfg = [];
cfg.rotate = 180;
cfg.grad = ignore.grad;
cfg.skipscale = 'yes';
cfg.skipcomnt = 'yes';
lay = ft_prepare_layout(cfg);
cfg = [];
cfg.operation = 'subtract';
cfg.parameter = 'avg';
%ignorevsgoodstop = ft_math(cfg,av_ignore,av_goodstop);
%ignorevsbadstop = ft_math(cfg,av_ignore,av_badstop);
%goodstopvsbadstop = ft_math(cfg,av_goodstop,av_badstop);
inhibition = ft_math(cfg,av_goodstop,av_ignore); % find the "difference wave" btwn stop & ignore trials
%all_data = ft_appenddata([],ignore,goodstop,badstop);
%alldata_avg = ft_timelockanalysis([],all_data);
ival = [0.10 .195;0.19 0.30;0.285 0.39;.445 .7]; % select a few time windows to try, below we will cycle thru these options
% compute the spatial pattern (P1, P2)
time_idx = (inhibition.time >= ival(1,1) & inhibition.time <= ival(1,2));
P1 = mean(allstop.avg(:, time_idx),2);
time_idx = (inhibition.time >= ival(2,1) & inhibition.time <= ival(2,2));
P2 = mean(allstop.avg(:, time_idx),2);
%% LDA beamforming ------------------------------------------------
% To obtain the LDA beamformer, we need the spatial pattern P and the
% corresponding continuous or epoched data (NOT the averaged data).
[w,t1_goodstoplda,C] = LDAbeamformer(P1,goodstop,.1); % feed in the epoched data for each cond
[w,t1_badstoplda,C] = LDAbeamformer(P1,badstop,.1);
[w,t1_ignorelda,C] = LDAbeamformer(P1,ignore,.1);
[w,t1_golda,C] = LDAbeamformer(P1,go,.1);
[w,t2_goodstoplda,C] = LDAbeamformer(P2,goodstop,.1);
[w,t2_badstoplda,C] = LDAbeamformer(P2,badstop,.1);
[w,t2_ignorelda,C] = LDAbeamformer(P2,ignore,.1);
[w,t2_golda,C] = LDAbeamformer(P2,go,.1);
%% Source-level ERP
cfg = [];
cfg.keeptrials = 'no';
t1_avggoodstop_LDA = ft_timelockanalysis(cfg, t1_goodstoplda);
t1_avgbadstop_LDA = ft_timelockanalysis(cfg, t1_badstoplda);
t1_avgignore_LDA = ft_timelockanalysis(cfg, t1_ignorelda);
t1_avggo_LDA = ft_timelockanalysis(cfg, t1_golda);
t2_avggoodstop_LDA = ft_timelockanalysis(cfg, t2_goodstoplda);
t2_avgbadstop_LDA = ft_timelockanalysis(cfg, t2_badstoplda);
t2_avgignore_LDA = ft_timelockanalysis(cfg, t2_ignorelda);
t2_avggo_LDA = ft_timelockanalysis(cfg, t2_golda);
cfg.keeptrials = 'yes';
t1_goodstop_LDA = ft_timelockanalysis(cfg, t1_goodstoplda);
t1_badstop_LDA = ft_timelockanalysis(cfg, t1_badstoplda);
t1_ignore_LDA = ft_timelockanalysis(cfg, t1_ignorelda);
t1_go_LDA = ft_timelockanalysis(cfg, t1_golda);
t2_goodstop_LDA = ft_timelockanalysis(cfg, t2_goodstoplda);
t2_badstop_LDA = ft_timelockanalysis(cfg, t2_badstoplda);
t2_ignore_LDA = ft_timelockanalysis(cfg, t2_ignorelda);
t2_go_LDA = ft_timelockanalysis(cfg, t2_golda);
t1_all_goodstop{i} = t1_avggoodstop_LDA;
t1_all_badstop{i} = t1_avgbadstop_LDA;
t1_all_ignore{i} = t1_avgignore_LDA;
t1_all_go{i} = t1_avggo_LDA;
t2_all_goodstop{i} = t2_avggoodstop_LDA;
t2_all_badstop{i} = t2_avgbadstop_LDA;
t2_all_ignore{i} = t2_avgignore_LDA;
t2_all_go{i} = t2_avggo_LDA;
t1_all_goodstop_trials{i} = t1_goodstop_LDA;
t1_all_badstop_trials{i} = t1_badstop_LDA;
t1_all_ignore_trials{i} = t1_ignore_LDA;
t1_all_go_trials{i} = t1_go_LDA;
t2_all_goodstop_trials{i} = t2_goodstop_LDA;
t2_all_badstop_trials{i} = t2_badstop_LDA;
t2_all_ignore_trials{i} = t2_ignore_LDA;
t2_all_go_trials{i} = t2_go_LDA;
end
save t1_all_go t1_all_go
save t2_all_go t2_all_go
save t1_all_goodstop t1_all_goodstop
save t2_all_goodstop t2_all_goodstop
save t2_all_badstop t2_all_badstop
save t2_all_ignore t2_all_ignore
save t1_all_ignore t1_all_ignore
save t1_all_badstop t1_all_badstop
save t1_all_go_trials t1_all_go_trials
save t2_all_go_trials t2_all_go_trials
save t1_all_goodstop_trials t1_all_goodstop_trials
save t2_all_goodstop_trials t2_all_goodstop_trials
save t1_all_badstop_trials t1_all_badstop_trials
save t2_all_badstop_trials t2_all_badstop_trials
save t1_all_ignore_trials t1_all_ignore_trials
save t2_all_ignore_trials t2_all_ignore_trials
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%snips for group
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%plotting
cd(directories{1})
load('t1_all_badstop.mat')
load('t1_all_goodstop.mat')
load('t2_all_goodstop.mat')
load('t1_all_ignore.mat')
load('t2_all_badstop.mat')
load('t2_all_ignore.mat')
load('t1_all_go.mat')
load('t2_all_go.mat')
load('t1_all_badstop_trials.mat')
load('t1_all_goodstop_trials.mat')
load('t2_all_goodstop_trials.mat')
load('t1_all_ignore_trials.mat')
load('t2_all_badstop_trials.mat')
load('t2_all_ignore_trials.mat')
load('t1_all_go_trials.mat')
load('t2_all_go_trials.mat')
Stutt_t1_all_ignore = t1_all_ignore;
Stutt_t1_all_goodstop = t1_all_goodstop;
Stutt_t2_all_goodstop = t2_all_goodstop;
Stutt_t1_all_badstop = t1_all_badstop;
Stutt_t2_all_ignore = t2_all_ignore;
Stutt_t2_all_badstop = t2_all_badstop;
Stutt_t1_all_go = t1_all_go;
Stutt_t2_all_go = t2_all_go;
Stutt_t1_all_ignore_trials = t1_all_ignore_trials;
Stutt_t1_all_goodstop_trials = t1_all_goodstop_trials;
Stutt_t2_all_goodstop_trials = t2_all_goodstop_trials;
Stutt_t1_all_badstop_trials = t1_all_badstop_trials;
Stutt_t2_all_ignore_trials = t2_all_ignore_trials;
Stutt_t2_all_badstop_trials = t2_all_badstop_trials;
Stutt_t1_all_go_trials = t1_all_go_trials;
Stutt_t2_all_go_trials = t2_all_go_trials;
StuttGM_t1_goodstop = ft_timelockgrandaverage([],t1_all_goodstop{:});
StuttGM_t1_badstop = ft_timelockgrandaverage([],t1_all_badstop{:});
StuttGM_t1_ignore = ft_timelockgrandaverage([],t1_all_ignore{:});
StuttGM_t1_go = ft_timelockgrandaverage([],t1_all_go{:});
StuttGM_t2_goodstop = ft_timelockgrandaverage([],t2_all_goodstop{:});
StuttGM_t2_badstop = ft_timelockgrandaverage([],t2_all_badstop{:});
StuttGM_t2_ignore = ft_timelockgrandaverage([],t2_all_ignore{:});
StuttGM_t2_go = ft_timelockgrandaverage([],t2_all_go{:});
cd(directories{2})
load('t1_all_badstop.mat')
load('t2_all_badstop.mat')
load('t1_all_goodstop.mat')
load('t2_all_goodstop.mat')
load('t1_all_ignore.mat')
load('t2_all_ignore.mat')
load('t1_all_go.mat')
load('t2_all_go.mat')
load('t1_all_badstop_trials.mat')
load('t1_all_goodstop_trials.mat')
load('t2_all_goodstop_trials.mat')
load('t1_all_ignore_trials.mat')
load('t2_all_badstop_trials.mat')
load('t2_all_ignore_trials.mat')
load('t1_all_go_trials.mat')
load('t2_all_go_trials.mat')
Ctrl_t1_all_ignore = t1_all_ignore;
Ctrl_t1_all_goodstop = t1_all_goodstop;
Ctrl_t2_all_goodstop = t2_all_goodstop;
Ctrl_t1_all_badstop = t1_all_badstop;
Ctrl_t2_all_ignore = t2_all_ignore;
Ctrl_t2_all_badstop = t2_all_badstop;
Ctrl_t1_all_go = t1_all_go;
Ctrl_t2_all_go = t2_all_go;
Ctrl_t1_all_ignore_trials = t1_all_ignore_trials;
Ctrl_t1_all_goodstop_trials = t1_all_goodstop_trials;
Ctrl_t2_all_goodstop_trials = t2_all_goodstop_trials;
Ctrl_t1_all_badstop_trials = t1_all_badstop_trials;
Ctrl_t2_all_ignore_trials = t2_all_ignore_trials;
Ctrl_t2_all_badstop_trials = t2_all_badstop_trials;
Ctrl_t1_all_go_trials = t1_all_go_trials;
Ctrl_t2_all_go_trials = t2_all_go_trials;
CtrlGM_t1_goodstop = ft_timelockgrandaverage([],t1_all_goodstop{:});
CtrlGM_t1_badstop = ft_timelockgrandaverage([],t1_all_badstop{:});
CtrlGM_t1_ignore = ft_timelockgrandaverage([],t1_all_ignore{:});
CtrlGM_t1_go = ft_timelockgrandaverage([],t1_all_go{:});
CtrlGM_t2_goodstop = ft_timelockgrandaverage([],t2_all_goodstop{:});
CtrlGM_t2_badstop = ft_timelockgrandaverage([],t2_all_badstop{:});
CtrlGM_t2_ignore = ft_timelockgrandaverage([],t2_all_ignore{:});
CtrlGM_t2_go = ft_timelockgrandaverage([],t2_all_go{:});
figure;
subplot(2,4,1)
ft_singleplotER([],StuttGM_t1_goodstop,CtrlGM_t1_goodstop)
ylim([-0.5 1.5])
subplot(2,4,2)
ft_singleplotER([],StuttGM_t1_badstop,CtrlGM_t1_badstop)
ylim([-0.5 1.5])
subplot(2,4,3)
ft_singleplotER([],StuttGM_t1_ignore,CtrlGM_t1_ignore)
ylim([-0.5 1.5])
subplot(2,4,4)
ft_singleplotER([],StuttGM_t1_go,CtrlGM_t1_go)
ylim([-0.5 1.5])
subplot(2,4,5)
ft_singleplotER([],StuttGM_t2_goodstop,CtrlGM_t2_goodstop)
ylim([-0.5 1.5])
subplot(2,4,6)
ft_singleplotER([],StuttGM_t2_badstop,CtrlGM_t2_badstop)
ylim([-0.5 1.5])
subplot(2,4,7)
ft_singleplotER([],StuttGM_t2_ignore,CtrlGM_t2_ignore)
ylim([-0.5 1.5])
subplot(2,4,8)
ft_singleplotER([],StuttGM_t2_go,CtrlGM_t2_go)
ylim([-0.5 1.5])
legend('stutt','ctrl')
cfg = [];
cfg.spmversion = 'spm12';
cfg.method = 'analytic';
cfg.statistic = 'ft_statfun_indepsamplesT'; % use the independent samples T-statistic as a measure to
cfg.correctm = 'fdr';
cfg.alpha = 0.05; % alpha level of the permutation test
cfg.numrandomization = 1000; % number of draws from the permutation distribution
design = zeros(1,size(t1_all_go,2) + size(t1_all_go,2));
design(1,1:size(t1_all_go,2)) = 1;
design(1,(size(t1_all_go,2)+1):(size(t1_all_go,2) + size(t1_all_go,2))) = 2;
cfg.design = design; % design matrix
cfg.ivar = 1; % number or list with indices indicating the independent variable(s)
stat = ft_timelockstatistics(cfg, Stutt_t2_all_badstop{:}, Ctrl_t2_all_badstop{:});
figure;ft_singleplotER([],StuttGM_t2_badstop,CtrlGM_t2_badstop)
hold on
sig = zeros(length(stat.prob),1);
sig(fdr(stat.prob)<0.05) = 1;
sig(~sig) = NaN;
plot(Ctrl_t1_all_go{1}.time,sig,'* k')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%TF
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Ctrl_t1_all_go_tf = [];
% Stutt_t1_all_go_tf = [];
% %
% for i =1:length(Ctrl_t1_all_go_trials)
%
% cfg = [];
% cfg.output = 'pow';
% cfg.channel = 'all';
% cfg.method = 'mtmconvol';
% cfg.taper = 'hanning';
% cfg.foi = 2:0.5:40; % analysis 5 to 30 Hz in steps of 0.5 Hz
% cfg.t_ftimwin = 5./cfg.foi;
% cfg.tapsmofrq = 0.4 * cfg.foi;
% cfg.toi = -0.5:0.05:1.0; % time window "slides" from -0.5 to 1.5 sec in steps of 0.05 sec (50 ms)
% cfg.pad = 'nextpow2';
% go_ft_ctrl = ft_freqanalysis(cfg, Ctrl_t1_all_go_trials{i});
% Ctrl_t1_all_go_tf{i} = go_ft_ctrl;
% end
%
% for i =1:length(Stutt_t1_all_go_trials)
%
% cfg = [];
% cfg.output = 'pow';
% cfg.channel = 'all';
% cfg.method = 'mtmconvol';
% cfg.taper = 'hanning';
% cfg.foi = 2:0.5:40; % analysis 5 to 30 Hz in steps of 0.5 Hz
% cfg.t_ftimwin = 5./cfg.foi;
% cfg.tapsmofrq = 0.4 * cfg.foi;
% cfg.toi = -0.5:0.05:1.0; % time window "slides" from -0.5 to 1.5 sec in steps of 0.05 sec (50 ms)
% cfg.pad = 'nextpow2';
% go_ft_stutt = ft_freqanalysis(cfg, Stutt_t1_all_go_trials{i});
% Stutt_t1_all_go_tf{i} = go_ft_stutt;
% end
%
%
% cfg = [];
% cfg.spmversion = 'spm12';
% cfg.channel = 'LDAsource';
% cfg.frequency = [5 30];
% cfg.neighbourdist = 4;
% cfg.latency = [0 .5];
% cfg.avgovertime = 'no';
% cfg.avgoverfreq ='no';
% cfg.avgoverchan = 'no';
%
% cfg.clusteralpha = 0.05;
% cfg.statistic = 'ft_statfun_indepsamplesT'; % use the independent samples T-statistic as a measure to
% cfg.numrandomization = 500;
% cfg.correctm = 'none';
% cfg.method = 'analytic';
%
%
% design = zeros(1,size(Ctrl_t1_all_go_tf,2) + size(Ctrl_t1_all_go_tf,2));
% design(1,1:size(Ctrl_t1_all_go_tf,2)) = 1;
% design(1,(size(Ctrl_t1_all_go_tf,2)+1):(size(Ctrl_t1_all_go_tf,2) + size(Ctrl_t1_all_go_tf,2))) = 2;
%
% cfg.design = design; % design matrix
% cfg.ivar = 1; % number or list with indices indicating the independent variable(s)
%
% stat = ft_freqstatistics(cfg, Ctrl_t1_all_go_tf{:}, Stutt_t1_all_go_tf{:});
% %
% Ctrl_t1_all_go_tf_bc = [];
% Stutt_t1_all_go_tf_bc = [];
% cfg = [];
% cfg.baseline = [-0.2 0];
% cfg.baselinetype = 'relchange';
%
% for i=1:length(Ctrl_t1_all_go_tf)
% Ctrl_t1_all_go_tf_bc{i} = ft_freqbaseline(cfg,Ctrl_t1_all_go_tf{i});
% end
%
% for i=1:length(Stutt_t1_all_go_tf)
% Stutt_t1_all_go_tf_bc{i} = ft_freqbaseline(cfg,Stutt_t1_all_go_tf{i});
% end
%
% Ctrl_t1_all_go_tf_GM = ft_freqgrandaverage([],Ctrl_t1_all_go_tf_bc{:});
% Stutt_t1_all_go_tf_GM = ft_freqgrandaverage([],Stutt_t1_all_go_tf_bc{:});
%
% figure;
% cfg = [];
% cfg.xlim = [-0.2 0.75];
% cfg.ylim = [5 30];
% cfg.zlim = [-.25 .25];
% subplot(2,1,1)
% ft_singleplotTFR(cfg,Ctrl_t1_all_go_tf_GM)
% subplot(2,1,2)
% ft_singleplotTFR(cfg,Stutt_t1_all_go_tf_GM)
%
%
% go_ft_ctrl = ft_freqanalysis(cfg, Ctrl_t1_all_goodstop{1});
%
%
% badstop = ft_freqanalysis(cfg, Ctrl_t1_all_badstop{1});
% % go = ft_freqanalysis(cfg, Ctrl_t1_all_go{1});
% % %
% % cfg = [];
% cfg.baseline = [-2.0 -1.1];
% cfg.baselinetype = 'relchange';
% cfg.zlim = [-0.3 0];
% cfg.xlim = [-0.5 1];
% cfg.ylim = [2 30];
% cfg.channel = 'all'; % top figure
%
% figure
% subplot(2,1,1)
% ft_singleplotTFR(cfg, goodstop);
% title('Goodstop')
% subplot(2,1,2)
% ft_singleplotTFR(cfg, badstop);
% title('Badstop')