[Eeglablist] SIFT resampling surrogate distributions with 1 trial

[Tim Mullen]

PS: in Matlab 2016 you can just use the autocorr() function for an
autocorrelation plot with confidence intervals.







On Thu, Aug 25, 2016 5:13 PM, Tim Mullen mullen.tim at gmail.com wrote:
Ok. Yes, as you've surmised, the key trickiness in this sort of statistical
problem is that you need to consider the autocorrelation in each time-series
(and they are definitely autocorrelated here, since the causal estimates are
obtained from a sliding window). Probably the worst thing you can do is perform
a standard unpaired t-test, which has hugely inflated Type I error rate if
samples are significantly autocorrelated.
Perhaps you could try a method like this:
Performing T-tests to Compare Autocorrelated Time Series Data Collected from
Direct-Reading Instruments. J Occup Environ Hyg. 2015;12(11):743-52. doi: 10.1080/15459624.2015.1044603. ncbi.nlm.nih.gov

Alternately, one possibility might be to compute the autocorrelation function
for each time-series and if it decays to non-significant amplitude after K lags,
then you could just select every K+1th sample for subsequent analysis. The
serial correlation should be minimal at that point (you could run a
Durbin-Watson test to confirm). Here is an example of how to use an autocorrelation plot for this. One potential issue here
(there's always one) is that the analytic confidence bounds for the null
hypothesis of zero autocorrelation generally rely on a Gaussianity assumption on
the data. The granger-causal estimates are definitely not Gaussian (probably
closer to gamma-distributed). You could try log-transforming them to render more
Gaussian -- it's a monotonic transform so it won't affect the statistics for
differences in means.
There are other more complex approaches involving fitting ARMA models (and
probably some more simple ones I'm not considering at the moment).
Tim






On Tue, Aug 23, 2016 11:38 PM, Winslow Strong winslow.strong at gmail.com wrote:
Only in a difference in means over the entire condition.
On Tue, Aug 23, 2016 at 11:00 PM, Tim Mullen < mullen.tim at gmail.com > wrote:
Yes, skipping one or more trials may at least mitigate some of the
autocorrelation effects. Are you only interested in whether there is a
difference in means over the whole condition, or whether there are differences
at specific points in time?
On Tue, Aug 23, 2016 at 4:17 PM, Winslow Strong < winslow.strong at gmail.com > wrote:
Hi Tim,
Yes I was searching for some approximate test stats and p-vals generated by
creating pseudotrials within each trial. I'll try this out. I'm thinking it
might be wise to leave a gap between the pseudo trials (i.e. not make them
contiguous EEG segments) to make them closer to independent. Leaving out
every-other pseudotrial might be a reasonable tradeoff. One could get 2 test
stats or just 2 sample variances this way: one from the even pseudotrials and
one from the odds.
This is a bit hacky though, and I wonder if there are canonical methods to deal
with the lack of independence.
On Mon, Aug 22, 2016 at 12:59 PM, Tim Mullen < mullen.tim at gmail.com > wrote:
Winslow, Makoto,

As a statistical principle, bootstrapping can only be used when you have
multiple independent and identically distributed (i.i.d) observations available.
The observations are resampled with replacement from the original set to
construct an empirical probability distribution.

It is not possible to use bootstrapping to test for statistical differences
between only two observations (i.e. two trials). In general, with any test, your
statistical power will be extremely low if you have only one observation per
condition.

You can try to mitigate this by segmenting your long continuous trials into
short 'pseudo-trials' and then testing for differences in the pseudo-trial
between conditions. Make sure that you average your causal measure over time
within each trial before computing your stats. One concern is that the
pseudotrials may be far from i.i.d within a condition, so if using bootstrap,
your bootstrap distribution may not converge to the true distribution of the
estimator and your stats will be biased.

Depending on your specific null hypothesis and protocol, however, there may be
alternative parametric and nonparametric tests you can apply.

Otherwise try to collect data for more subjects (then you simply bootstrap
across subjects e.g. using statcond with SIFT matrices) or more trials (run your
experiment more than once per condition).

Tim



On Aug 18, 2016 11:09 AM, "Makoto Miyakoshi" < mmiyakoshi at ucsd.edu > wrote:
Dear Winslow,
Yes, unfortunately the bootstrap seems to be designed for across trials.
Makoto
On Sat, Aug 13, 2016 at 4:57 PM, Winslow Strong < winslow.strong at gmail.com > wrote:
I'd like to use a resampling technique (e.g. bootstrap) to get p-values and test
stats for SIFT connectivity metrics for 1 subject across n conditions.
This is a steady-state condition study, hence there's only 1 trial per
condition. I'm trying to analyze whether certain connectivity metrics (i.e.
their averages over a condition) are statistically significantly different
across the conditions. I was under the impression I could use SIFT's surrogate
distribution generator to obtain the surrogate distribution for these
calculations, but when I run that from the GUI for bootstrap, I get the error:
"Unable to compute bootstrap distributions for a single trial"
Is this surrogate function only designed to do boostrapping over trials? Or is
there a way to do it over windows within a condition?
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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego

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