freqstatistics with indepsamplesZcoh: incompatible dimensions?

[Dahlia Sharon]

Hi,

I'm trying to run cluster statistics on coherence between a single channel (actually 2) and a set of 516 channels. My code crashes when ft_freqstatistics reaches clusterstat [Error using ==> reshape; To RESHAPE the number of elements must not change.
Error in ==> clusterstat at 182;      posclusobs = findcluster(reshape(postailobs,  [cfg.dim,1]),channeighbstructmat,cfg.minnbchan);     ]
At this point postailobs is much larger than multiplying the elements of cfg.dim.
But digging into it the problem starts earlier, and as far as I can see is a result of incompatibility between the output of freqanalysis and the way indepsamplesZcoh treats the data:
In freqanalysis I calculate csd between each of the two channels and all the rest, i.e. I have 2 * (516+1) = 1034 pairs. The dimension of the resulting freq.crssspctrm is 10 (trials) x 1034 (channel pairs) x 51 (frequencies) x 1 (times).
In indepsamplesZcoh using the freqanalysis output, for each frequency I have a 1034x10 matrix multiplied by its transpose to get a dimension of 1034x1034. This is of-course much higher than the dimension of the data which should be something like 517x2.

How should I transform the freqanalysis output so that it'll be used correctly by indepsamplesZcoh?

Thanks!
Dahlia.

PS - here is part of the code that might be helpful, please let me know if any further information would help.

+++++++++++++++++++++++++++++++++++++++++++++++++++++
% configuration for freqanalysis
cfgfa            = [];
cfgfa.output     = 'powandcsd';          % 'pow';
cfgfa.toi        = -.25:.025:1.25;
cfgfa.foi        = 6:2:80;             %%
cfgfa.method     = 'mtmfft';       % 'mtmconvol';
 cfgfa.trials     = 'all';
cfgfa.keeptrials = 'yes';
cfgfa.channel    = {'all' };
cfgfa.channelcmb = { 'rh-Jz MT' 'all' ; 'lh-Jz MT' 'all' }; % compute csd between right MT and all

% configuration for freqstatistics
cfgfs =                    [];
cfgfs.numrandomization =   100;
cfgfs.method =             'montecarlo';
cfgfs.correctm =           'cluster';
cfgfs.clusteralpha =       0.05;       % significance level for sample-level statistic, i.e. for being considered candidate for clustering
cfgfs.clusterstatistic =   'maxsum';   % maxsize
cfgfs.clusterthreshold =   'parametric';   % uses T distribution to calculate the sample-level statistic threshold - appropriate for cfg.statistic=indepsamplesT
cfgfs.minnbchan =          0;          %
cfgfs.alpha =              0.05;       % significance level for cluster-level statistic, i.e. for final result of significant cluster.
cfgfs.tail =               0;          % 0 for two-sided testing
cfgfs.correcttail =        'alpha';
cfgfs.ivar =               1;
cfgfs.latency =            'all';
cfgfs.frequency =          'all';
cfgfs.neighbours =          [];

cfgfs_coh =                 cfgfs;
cfgfs_coh.statistic =       'indepsamplesZcoh';
cfgfs_coh.label =           { 'all' }; %{ 'rh-Jz MT' 'all' ; 'lh-Jz MT' 'all' };

    for trig = 100:100:200
        trigstr = num2str( trig); disp([subj '  trig' trigstr])
        invtrigfile = strcat( invtrigfile_base{1} , trigstr , invtrigfile_base{2});
        load(invtrigfile)

        freq = ft_freqanalysis( cfgfa, data_ftrip);
        ntrl = size( freq.powspctrm, 1 ); eval(['ntrl' trigstr '=ntrl;'])
    end
    design = ones(1, ntrl100 + ntrl200); design(ntrl200+1:end)=2;
    ccfgfs_coh.design = design;
%     cfgfs_coh.label = freq.labelcmb;
    cfgfs_coh.channelcmb = freq.labelcmb;
    coh_stat = ft_freqstatistics( cfgfs_coh, freq200, freq100);

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