[Eeglablist] time-frequency contiguity threshold?

[Scott Makeig]

Paul - It should be straightforward for newtimef() to collect a histogram of
adjacent alpha-level significant voxels in the surrogate data computed to
determine the bootstrap significance threshold. Just collect the number of
such contiguous, all-significant patches of the given size and shape. For
example,
'contig', [2 3]  % This would ask for a contiguous 2x3 patch of
(freqs,times) to be significant
                     % at the (separately specified or default) alpha level

The function could also return and/or print out the heuristically determined
probability of this in the surrogate data.

Scott Makeig

On Fri, Jun 20, 2008 at 4:19 AM, Kieffaber, Paul <kieffaberp at upmc.edu>
wrote:

>  I've been looking (so far in vein) for some precedent or algorithm to
> determine a reasonable contiguity threshold for time-frequency analysis.  My
> understanding is that the zero-masking done by newtimef() is on a pixel
> (resel?) by pixel basis, showing only those pixels where the deviation from
> baseline is statistically significant.  I'm wondering if anyone is using the
> added constraint of a contiguity threshold in order to mask even those
> pixels with statistically significant deviations by requiring that there be
> multiple consecutive (in time and/or frequency) pixels with significant
> deviations before that time-frequency perturbation is considered
> "significant."  Two alternatives I'm imagining are: (1)a theoretically
> determined threshold in terms of time and frequency (e.g., perturbation must
> span 50ms and/or 5Hz) that is independent of the temporal/frequency
> resolution and (2) an algorithmically determined threshold that is based on
> the dimensions of the time-frequency  matrix (e.g., the joint probability of
> N consecutive time-bins and N consecutive frequency-bins is less than
> alpha).  Any comments,  recommendations and/or references would be greatly
> appreciated.
>
>
>
> Thanks!
>
> Paul
>
>
>
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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