[Eeglablist] ERSP bootstrap outside of newtimef
Max.Cantor at colorado.edu
Mon Oct 23 15:13:33 PDT 2017
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',
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
rsignif ends up being my freq x 2 matrix which I can input in tftopo, so I
*tftopo(mean(g_ersp,3), times, freqs, 'logfreq', 'native', 'signifs',
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)?
On Thu, Oct 19, 2017 at 5:33 PM, Max Cantor <Max.Cantor at colorado.edu> wrote:
> 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
> 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.
> Max Cantor
> Graduate Student
> Cognitive Neuroscience of Language Lab
> University of Colorado Boulder
Cognitive Neuroscience of Language Lab
University of Colorado Boulder
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