cfg = [];
cfg.layout = 'easycap32ch-avg.lay';
cfg.channel = {'EEG'};
cfg.latency = [0 2.5];
cfg.method = 'montecarlo';
cfg.statistic = 'depsamplesT';
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.05;
cfg.clusterstatistic = 'maxsum';
cfg.minnbchan = 2;
cfg.tail = 0;
cfg.clustertail = 0;
cfg.alpha = 0.025;
cfg.numrandomization = 100;

subj = 4;
design = zeros(2,2*subj);
for i = 1:subj
  design(1,i) = i;
end
for i = 1:subj
  design(1,subj+i) = i;
end
design(2,1:subj)        = 1;
design(2,subj+1:2*subj) = 2;

cfg.design = design;
cfg.uvar  = 1;
cfg.ivar  = 2;

[stat] = timelockstatistics(cfg, da_test1, da_test2)


GA_VALvsINV = da_test1;
GA_VALvsINV.avg = da_test1.avg - da_test2.avg;

figure;  
timestep = 0.10;      %(in seconds)
sampling_rate = da_test1.fsample;
sample_count = length(stat.time);
j = [0:timestep:2.5];   % Temporal endpoints (in seconds) of the ERP average computed in each subplot
m = [1:timestep*sampling_rate:sample_count];  % temporal endpoints in MEEG samples

pos_cluster_pvals = [stat.posclusters(:).prob];
pos_signif_clust = find(pos_cluster_pvals < stat.cfg.alpha);
pos = ismember(stat.posclusterslabelmat, pos_signif_clust);

for k = 1:25;
     subplot(5,5,k);   
     cfg = [];   
     cfg.xlim=[j(k) j(k+1)];   
     cfg.zlim = 'maxmin'   
     pos_int = all(pos(:, m(k):m(k+1)), 2);
     cfg.highlight = 'on';
     cfg.highlightchannel = find(pos_int);       
     cfg.comment = 'xlim';   
     cfg.commentpos = 'title';   
     cfg.layout = 'easycap32ch-avg.lay';
     topoplotER(cfg, GA_VALvsINV);
end

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