[Eeglablist] ERSP bootstrap outside of newtimef

Max Cantor Max.Cantor at colorado.edu
Thu Oct 26 09:00:36 PDT 2017

Following up on my attempt to use bootstat on an ersp array outside of
pop_newtimef: I think I have now gotten the former implementation working
(shuffling over samples on the across-channels mean difference grand
average ersp)! Hopefully this will be useful if anyone else tries to do
what I'm doing in the future.

I was including a baseline vector in the bootstat input since my ersps were
baselined along those samples and I assumed that that should be accounted
for in bootstat. However, when I take the basevect out (meaning, if I
understand correctly, that bootstat demeans across the whole ersp, which I
guess is appropriate since the ersp is already baselined?) and compare ersp
plots of the "raw" esrp and the masked ersp, I get what look like sensible
time-frequency clusters. Maybe it would be better to not baseline my ersps,
but then include the baseline in bootstat?

Does what I've done make methodological sense? Conceptually, am I correct
in assuming that this is similar to the fieldtrip cluster permutation test?
I see a paper cited in the bootstrap function that I intend to read,
although some direct confirmation/explanation would certainly be


On Mon, Oct 23, 2017 at 5:05 PM, Max Cantor <Max.Cantor at colorado.edu> wrote:

> Ok, last followup for today! I have tried two things- the above, and
> another method which I'll discuss below. In both cases, comparing tftopo
> plots both with the signifs argument and without it turned out identically,
> which makes me skeptical of whether what I'm doing is working at all.
> The second method I'm trying is:
> *    idx = 1;*
> *    for k = freqs*
> *        [rsignif(idx,:) rbot{idx}] = bootstat({a(idx,:) b(idx,:)}, 'arg1
> - arg2', ...*
> *            'basevect', [27 28 29 30 31 32 33 34 35 36 37], 'alpha',
> 0.01, 'dimaccu', 2);*
> *        idx = idx+1;*
> *    end*
> Where a and b are freqs x samples matrices for the two conditions,
> averaged across my channels of interest. So it loops through each
> frequency, shuffles samples, and if I understand correctly, should make a
> comparison across permutations based on the difference between the two
> conditions. I feel like either method should be fine for the purpose of
> masking the ersps and I'm not even sure if the rsignifs for these two
> methods would even be all that different, but the fact that it doesn't seem
> to be working suggests I'm doing something wrong or misunderstanding
> something.
> Any advice appreciated!
> Best,
> Max
> On Mon, Oct 23, 2017 at 4:13 PM, Max Cantor <Max.Cantor at colorado.edu>
> wrote:
>> To follow up, I believe I have gotten the code to "work", although I
>> would appreciate confirmation on whether what I'm doing is conceptually
>> sound, or if I am misunderstanding the code or purpose of the statistic.
>> Below is my code:
>> *[rsignif rbot] = bootstat(permute(g_ersp, [2,1,3]), 'mean(arg1,3);',
>> 'basevect', [27 28 29 30 31 32 33 34 35 36 37], 'alpha', 0.01, 'dimaccu',
>> 2);*
>> Where g_ersp is a grand average of ersp of 200 samples (of data from ~
>> -1000 to 2000ms) x 48 frequencies (log-spaced) x 7 channels. This grand
>> average is the difference between two conditions, averaged across all
>> subjects, where each subjects ersp is an average across all trials. I
>> permute the matrix so that it is freqs x samples x chans, which is similar
>> to what bootstat is doing on a subject-by-subject basis in my pop_newtimef
>> code, except there it's only one condition, not a difference between
>> conditions, and the third dimension is trials instead of channels.
>> The mean(arg1,3) formula came directly from debugging in bootstat from
>> pop_newtimef. While in pop_newtimef this would be averaging across trials,
>> if I'm interested in finding which time-frequency clusters are significant
>> across all channels (or from the average across all channels), does this
>> still make sense?
>> The baseline vector comes from the baseline I used on these ersp data,
>> alpha is consistent with what I did on the single-subject/single-channel
>> level, and I don't even know what dimaccu is but this was also taken from
>> debug.
>> rsignif ends up being my freq x 2 matrix which I can input in tftopo, so
>> I run this:
>> *tftopo(mean(g_ersp,3), times, freqs, 'logfreq', 'native', 'signifs',
>> rsignif);*
>> So I'm plotting the grand-average difference ersp averaged across all
>> channels, using the appropriate times and freqs axis, the ersp were
>> natively log-scaled, and my mask is rsignif from above.
>> To reiterate, is this mask appropriate for these data? Is this showing me
>> the time-frequency clusters averaged across all channels which are
>> significant (relative to my alpha)?
>> Thanks,
>> Max
>> On Thu, Oct 19, 2017 at 5:33 PM, Max Cantor <Max.Cantor at colorado.edu>
>> wrote:
>>> Hi,
>>> I've produced grand-averaged TFRs across all of my subjects, at select
>>> channels, and taken the difference between two conditions, by looping
>>> through pop_newtimef for each subject and each channel of interest across
>>> both conditions separately, outputting these into separate 4-D arrays,
>>> averaging across subjects, and then taking the difference between the two
>>> arrays, such that I end up with an ersp difference array of freq x
>>> timepoint x chan.
>>> This array is compatible with the tftopo function and I would like to
>>> apply a bootstrapped statistical mask to this array through the tftopo
>>> function. Testing it with an erspboot mask outputted by pop_newtimef was
>>> functional, however this erspboot mask was obviously specific to that
>>> particular subject and channel and not appropriate for my grand averaged
>>> array.
>>> Is there a conceptually and computationally simple way to run this array
>>> through the bootstat function (which I believe is what's producing the
>>> statistical masking) outside of pop_newtimef and produce a freqs x 2
>>> erspboot mask that would be compatible with tftopo, or would it be easier
>>> for me to just reformat my array into a fieldtrip structure and use their
>>> functions for nonparametric cluster-based permutation testing, which to the
>>> best of my knowledge is the mathematical implementation eeglab uses anyway?
>>> Any advice appreciated.
>>> Thanks,
>>> Max
>>> --
>>> Max Cantor
>>> Graduate Student
>>> Cognitive Neuroscience of Language Lab
>>> University of Colorado Boulder
>> --
>> Max Cantor
>> Graduate Student
>> Cognitive Neuroscience of Language Lab
>> University of Colorado Boulder
> --
> Max Cantor
> Graduate Student
> Cognitive Neuroscience of Language Lab
> University of Colorado Boulder

Max Cantor
Graduate Student
Cognitive Neuroscience of Language Lab
University of Colorado Boulder
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