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<p><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Dear Fieldtrip-Community,</span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">I am new in this field and have a question regarding the time-frequency smoothing parameters prior to the cluster-based permutation tests. Since my tf representations are rather pixelated, I was considering using a 2D Gaussian kernel for smoothing. However, depending on whether I use the smoothing parameters (see script below) I acquire clusters or not. I was under the impression - please correct me if I am wrong - that smoothing the representations should not interfere with the data as such. I would very much appreciate your help,</span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Best wishes,</span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Marlen</span></span></span></span></span></p>
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<div>tests = {{'Cue' 'Random'} {'Expected' 'Random' } {'Cue' 'Expected'}}; <br/>
</div>
<div>for icond = 1:numel(tests)<br/>
<br/>
tmp1 = load([ftpath 'avgfreq_eeg_tf_' tests{icond}{1} '_alpha&beta.mat']);<br/>
tmp2 = load([ftpath 'avgfreq_eeg_tf_' tests{icond}{2} '_alpha&beta.mat']);<br/>
avg1 = tmp1.avg;<br/>
avg2 = tmp2.avg;<br/>
<br/>
% frequency baseline correction<br/>
cfg = [];<br/>
cfg.baselinetype = 'db';<br/>
cfg.baseline = [-1 -0.7];<br/>
bslavg1 = ft_freqbaseline(cfg, avg1);<br/>
bslavg2 = ft_freqbaseline(cfg, avg2);<br/>
<br/>
<br/>
% smooth TFRs on the group level to account for inter-subject variation<br/>
for isub = 1:size(bslavg1.powspctrm, 1)<br/>
for ichan = 1:size(bslavg1.powspctrm, 2)<br/>
bslavg1.powspctrm(isub,ichan,:,:) = imgaussfilt(squeeze(bslavg1.powspctrm(isub,ichan,:,:)), [1 2]);<br/>
bslavg2.powspctrm(isub,ichan,:,:) = imgaussfilt(squeeze(bslavg2.powspctrm(isub,ichan,:,:)), [1 2]);<br/>
end<br/>
end</div>
<div><br/>
% prepare neighbourhood for cluster-based t-test<br/>
cfg = [];<br/>
cfg.method = 'triangulation';<br/>
cfg.layout = 'acticap-64ch-standard2.mat';<br/>
neighbours = ft_prepare_neighbours(cfg);<br/>
<br/>
<br/>
% set up cluster-based t-test<br/>
cfg = [];<br/>
cfg.channels = 'all';<br/>
cfg.method = 'montecarlo';<br/>
cfg.statistic = 'ft_statfun_depsamplesT'; <br/>
cfg.correctm = 'cluster';<br/>
cfg.clusteralpha = 0.05;<br/>
cfg.tail = 0;<br/>
cfg.clustertail = 0; <br/>
cfg.alpha = 0.025;<br/>
cfg.clusterstatistic = 'maxsum';<br/>
cfg.clusterthreshold = 'nonparametric_common';<br/>
cfg.numrandomization = 1000; <br/>
cfg.neighbours = neighbours;<br/>
%cfg.minnbchan = 2; % macdab - minimal neighbouring channels<br/>
cfg.frequency = [2 40];<br/>
cfg.latency = [-0.7 2];<br/>
<br/>
% set up design matrix<br/>
cfg.design = [1:numel(subs) 1:numel(subs); ones(1, numel(subs)) 2*ones(1, numel(subs))];<br/>
cfg.uvar = 1;<br/>
cfg.ivar = 2;<br/>
<br/>
% run t-test and save data<br/>
stat = ft_freqstatistics(cfg, bslavg1, bslavg2);<br/>
save([ftpath 'ttest_db_smooth12__eeg_tf_' tests{icond}{1} '-' tests{icond}{2} '_alpha&beta.mat'], 'stat');<br/>
<br/>
% draw plot<br/>
cfg = [];<br/>
cfg.channel = 'all';<br/>
cfg.parameter = 'stat';<br/>
cfg.maskparameter = 'mask';<br/>
cfg.maskstyle = 'outline';<br/>
cfg.maskalpha = 0.25;<br/>
cfg.colorbar = 'yes';<br/>
cfg.marker = 'on';<br/>
cfg.colormap = (brewermap(256, '*YlGnBu'));<br/>
cfg.zlim = [-2 2];<br/>
cfg.xlim = [-0.7 2];<br/>
cfg.layout = 'acticap-64ch-standard2.mat';<br/>
figure<br/>
ft_multiplotTFR(cfg, stat);<br/>
title(['alpha&beta t-test ' tests{icond}{1} '-' tests{icond}{2}]);<br/>
<br/>
end </div>
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