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Dear Field-trippers<br>
First of all, I would like to thank the Fieldtrip mentors, and all
the contributors.<br>
I find this toolbox more than a toolbox. The website and the active
mailing list makes it stimulating and definitively instructive.<br>
Which motivates me to share with you some reflections and questions.<br>
<br>
I did some statistics on data from implanted electrodes (ECoG) in
human. For the purpose of this analysis, I mainly looked at the time
frequency space, so I first ran the following script:<br>
(with data being the output of the <i>ft_preprocessing</i>
function)<br>
<br>
<font color="#3333ff">load data;<br>
trig = [3 4 5];<br>
for cond = 1:length(trig)<br>
cfg = [];<br>
cfg.method = 'wavelet';<br>
cfg.output = 'fourier';<br>
cfg.foi = 2:2:50;<br>
cfg.toi = -0.5:0.02:0.05;<br>
cfg.keeptrials = 'yes';<br>
cfg.keeptaper = 'yes';<br>
cfg.width = 5;<br>
cfg.trials = find(data.trialinfo(:,1) == trig(cond));<br>
TF_Mwlt_fourier{cond} = ft_freqanalysis(cfg, data);<br>
end;<br>
</font><br>
I used Morlet wavelet because a previous post from Robert that
recommended not to use multitapering for PLF <br>
(<a class="moz-txt-link-freetext"
href="http://mailman.science.ru.nl/pipermail/fieldtrip/2006-March/000446.html">http://mailman.science.ru.nl/pipermail/fieldtrip/2006-March/000446.html</a>).<br>
And also to facilitate comparison with other studies.<br>
<br>
The output being Fourier, I computed power and phase concentration
(aka PLF or ITC),<br>
(both calculated at single trial level for stats and then averaged
for <i>ft_multiplot</i><i>TFR</i> )<br>
<br>
<font color="#3333ff">powplf_data.pow =
abs(data.fourierspctrm) .^2;<br>
powplf_data.powspctrm =
abs(mean(squeeze(powplf_data.pow),1));<br>
powplf_data.plf =
data.fourierspctrm./abs(data.fourierspctrm);<br>
powplf_data.plf_average =
abs(mean(squeeze(powplf_data.plf),1));</font><br>
<br>
<br>
The first statistics I wanted to run was a comparison of the power
and the PLF for each condition against their respective baseline
period.<br>
To do so I applied the following method (based on Delorme et Makeig
2004) for a given channel:<br>
- draw a value within the baseline period for each trial
(independently for each time point and frequency).<br>
- average along the trial dimension<br>
- repeat those steps thousand time<br>
- use those thousand repetition to construct the distribution<br>
- count the percentage of values above (/below) the observed
post-onset value from the data (at a given latency and frequency).<br>
Define significance for power using two tails (p < 0.025 & p
> 0.975), and one tail for PLF (p<0.05).<br>
<br>
I decided to write my own function because I was not sure that I
could do it using Field trip.<br>
I noticed that there is the <i>statfun_actvsblT</i> that can be
specified in <i>cfg.method</i> field of <i>ft_freqstatistics</i>.<br>
But I have two concerns about it:<br>
- I prefer to used a randomization method for distribution reason.
Indeed even if for power it seems to be ok with my data (seems to be
normally distributed),<br>
it is by definition not the case with PLF (which is more like a
gamma or F distribution; because values are more concentrated near
to zero, rare value toward 1).<br>
- this function average over the specified baseline time period.
This average step makes more sense to me in the case of ERP
analysis, but less with time frequency.<br>
Especially with PLF, since the average will have the tendency to
compress the values toward 0.<br>
<br>
I guess that an alternative would be to use <i>statfun_diff_itc</i>
with one condition being post-onset period and the other "fake"
condition being the baseline.<br>
But in this case the length (duration) of the two pools should be
identical, as the time points would be "paired". Am I correct about
this or is it more flexible ?<br>
<br>
Here are the points I would like to discuss:<br>
1) I think I did my analysis the correct way, but it might not be
the case, so any comments about the method are very welcome.<br>
2) when someone is interested in the phase, is there a "better"
method to compute the time frequency transform?<br>
(or any method is good as long as there is no frequency smoothing)<br>
3) can we imagine a future extra/new option in the <i>statfun_actvsbl</i>
that would allow for choosing between averaging and taking a random
time point within the specified time period ?<br>
Maybe this makes less sense for T-stats than in the case of a
randomization test ? and even less when this is not done at the
level of single trials ?<br>
<br>
Thanks in advance for your comments.<br>
<br>
Manuel<br>
<br>
<br>
<br>
<pre class="moz-signature" cols="72">--
Manuel Mercier, PhD
Research Fellow
Cognitive Neurophysiology Laboratory,
Children’s Evaluation and Rehabilitation Center (CERC),
Departments of Pediatrics
Albert Einstein College of Medicine,
1225 Morris Park Avenue
Bronx , New York, NY 10461
phone: +1 (718) 862 1824
fax: +1 (718) 862 1807</pre>
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