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Hi Nova,
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<div class="">Welcome on board! </div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">My name is Nova and I am new to FieldTrip. I am currently working on the parametric statistics of the ERP data (obtained from 32 electrodes) but I face some problems. I have tried googling and reading
the tutorials and posts on mailing lists, but I still can't solve the problems. Could you please assist me in finding the solutions?</div>
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I can give it a try.</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">Here are the problems:</div>
<div style="margin-top: 0px; margin-bottom: 0px;" class="">I would like to perform the parametric statistics on the ERP data, I have 2 sets of data called <i class="">dataresponse</i> and <i class="">databaseline</i> (each has 270 trials, with dimensions 270x32x584
double). I calculated the ERPs of each dataset by averaging the signals over the number of trials. I would like to compare both data of one subject on the channel level. On the tutorial website, it was on the group level. I tried to adjust my codes for the <i class="">cfg.design</i>,
but I still can't find the solutions.</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">In order to calculate the statistics on channel electrode 10, I used the ttest2 function on MATLAB with this script:<br class="">
<span class=""><i class="">[h,p] = ttest2(avgdataresponse.avg(10,<wbr class="">:), avgdatabaseline.avg(10,:),'<wbr class="">alpha',0.05);</i></span></div>
<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class=""><i class=""><span class="">%avgdataresponse and avgdatabaseline are the average over trials,</span><br class="">
</i></span></div>
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</span></div>
<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">then I obtained the h=1, p= <span class="">1.323e-04.</span></span></div>
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<div>Let me get this straight, I think that based on the above, you computed a t-score (and corresponding p-value) between 2 time series, i.e. between the amplitude values (over time) of a single channel’s signal from the response and the baseline variables.
Was this intended, because typically it is not a comparison that one would do?</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">However, when I tried with Fieldtrip function, I obtained different numbers.</div>
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<div class="">1) When using the "<span class="">ft_statfun_indepsamplesT" function with this script, I obtained <b class="">NaN</b> for <b class="">stat, critval,</b> and <b class="">prob</b>. Does it have to do with the <i class="">cfg.design</i>? What is
the correct <i class="">cfg.design</i> for the parametric T-test on a channel level for a single subject?</span></div>
<div class="">cfg = [];</div>
<div class="">cfg.channel = 'Audio10';</div>
<div class="">cfg.latency = [0.017 0.6];</div>
<div class="">cfg.avgovertime = 'yes';</div>
<div class="">cfg.parameter = 'avg';</div>
<div class="">cfg.method = 'analytic';</div>
<div class="">cfg.statistic = 'ft_statfun_indepsamplesT';</div>
<div class="">cfg.alpha = 0.05;</div>
<div class="">cfg.correctm = 'no';</div>
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<div class="">n_dataresponse = size(dataresponse.trial, 1); <span class="">%number of trials in data response (since the number of trials is the same as that of databaseline, I use one of them)</span></div>
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<div class="">cfg.design(1,1:2*n_<wbr class="">dataresponse) = [ones(1,n_databaseline), ones(1,n_dataresponse)*2];</div>
<div class="">cfg.design(2,1:2*n_<wbr class="">dataresponse) = [1:n_dataresponse 1:n_dataresponse];</div>
<div class="">cfg.ivar = 1;</div>
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<span class="">stat = ft_timelockstatistics(cfg, avgdataresponse, avgdatabaseline); </span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">2. When I change the parameter: <span class="">cfg.parameter = 'trial' and <span class="">stat = ft_timelockstatistics(cfg, dataresponse, databaseline);<br class="">
I obtained the p-value =</span></span> <span class="">0.040. Is this p-value correct? or is it still wrong because of my cfg.design?</span></span></div>
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<div>cfg.parameter = ‘avg’ in combination with avgdataresponse/avgdatabaseline is not going to work, because the function expects data in the input which have the original observations present (in your case the indivdual trials)</div>
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<div>Next to this, you do something completely different (as compared to the above), because (when using dataresponse/databaseline) now 1) the input data are single observations, and 2) you specify to average across time. <br class="">
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class=""><span class="">3. I tried also with <span class="">"</span><span class="">ft_statfun_<wbr class="">depsamplesT" with the same configuration as no.1 and added the cfg.uvar =2, I obtained
errors:<br class="">
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<div class=""><i class="">Error using ft_statfun_depsamplesT (line 78)</i></div>
<div class=""><i class="">Invalid specification of the design array.</i></div>
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<div class=""><i class="">Error in ft_statistics_analytic (line 94)</i></div>
<div class=""><i class=""> [stat, cfg] = statfun(cfg, dat, design);</i></div>
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<div class=""><i class="">Error in ft_timelockstatistics (line 185)</i></div>
<div class=""><i class=""> [stat, cfg] = statmethod(cfg, dat, design);</i></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">Is there a significant design array for a particular parametric T-test? What is the specification of the design array for independent and dependent T-test? (I tried searching about it
but I couldn't find the answers.)</span></div>
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<div>This is because you cannot input an average into the statistics function, and then use a design that specifies the individual trials. In that sense ft_statfun_depsamplesT is correct: the design is invalid</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class="">4. When I still use "<span class="">ft_statfun_depsamplesT" but changing cfg.parameter = 'trial' and <span class="">stat = ft_timelockstatistics(cfg, dataresponse, databaseline); I acquired the p value
= 0.0989.</span></span></div>
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The difference between ft_statfun_depsamplesT and ft_statfun_indpesamplesT is the same as the difference between a paired (ttest) and unpaired (ttest2) t-test.</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">Why is the p-value still different than the one obtained from the ttest2 function? What should I change in order to obtain a similar value as what is acquired on the ttest2 Matlab function?
Based on the tutorial, the p-value should be the same.</span></div>
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<div>If you want ttest2 to behave the same as ft_timelockstatistics (with cfg.statistic = ‘ft_statfun_indepsamplesT’, + the specified latency, and average across time as per your specification), you need to extract from databaseline and dataresponse the relevant
samples, i.e. something like</div>
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<div>x1 = mean(databaseline.trial(:,10, tbeg:tend),3);</div>
<div>x2 = mean(dataresponse.trial(:,10, tbeg:tend),3); </div>
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<div>and then use x1 and x2 as input to ttest2. tbeg and tend can be obtained as: tbeg = nearest(databaseline.time,0.017), and tend = nearest(databaseline.time, 0.6)</div>
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<div>Good luck and happy computing,</div>
<div>Jan-Mathijs</div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">I would really appreciate any help and advice on these problems.</span></div>
<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">Thank you very much and have a great day.</span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">Best regards,</span></div>
<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span class="">Nova Resfita</span></div>
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<div style="margin-top: 0px; margin-bottom: 0px;" class=""><span style="font-family: Cambria, Georgia, serif; font-size: 10pt;" class="">Nova Resfita, M.Sc.</span></div>
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