[Eeglablist] ask again about bootstrap and group analysis. pleasehelp

Sat Mar 4 23:54:59 PST 2006

Dear Arno and all,

First, thanks for the link to your book chapter. It seems like something
many people would find extremely useful. Secondly, I would like to draw your
attention to the fact that many permutation methods for statistical analysis
of EEG/MEG data are implemented in Fieldtrip toolbox
(http://www.ru.nl/fcdonders/fieldtrip/) in the clusterrandanalysis routine
(http://www2.ru.nl/fcdonders/fieldtrip/doku.php?id=fieldtrip:documentation:r
eference:clusterrandanalysis). Fieldtrip is an open source Matlab toolbox
with interface to EEGLAB
(http://www2.ru.nl/fcdonders/fieldtrip/doku.php?id=fieldtrip:documentation:i
ntegrating_with_eeglab). 

Best,

Vladimir Litvak

Technion - Israel Institute of Technology

-----Original Message-----
From: eeglablist-bounces at sccn.ucsd.edu
[mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Arnaud Delorme
Sent: Sunday, March 05, 2006 12:52 AM
To: Christopher Summerfield
Cc: eeglab_maillist; mfneelon at wisc.edu; wu xiang
Subject: Re: [Eeglablist] ask again about bootstrap and group analysis.
pleasehelp

Dear all,

I agree with Chris's description below. It is important to note that this is
not bootstrap but permutation statistics. Bootstraping would be a way to
find better confidence interval for the t-value BEFORE performing a
parametric test on it (comparing it to a table). Permutation statistics
involve recomputing the distribution of t-values using permutation of
conditions (by the way, this does not have necessarily to be a t-value and
can be any distance measure between the two distributions). This may also be
generalized to more than two conditions using ANOVA and reconstruvtion of
the ANOVA distribution using permutation of conditions. This is described
with example page 19 in the following document:

Delorme, A. (2005) Statistical methods. Encyclopedia of Medical Device and
Instrumentation, in press. PDF
<http://www.sccn.ucsd.edu/%7Earno/mypapers/statistics.pdf>  link.

A custom way to compensate for multiple comparisons is also described below:

Tanji, K., Suzuki, F., Delorme, A., Shamoto, H., and Nakasato N. (2005)
High-Frequency Gamma-Band Activity in the Basal Temporal Cortex during
Picture-Naming and Lexical-Decision Tasks. J Neuroscience, 25, 3287-3293.
Journal's link
<http://www.jneurosci.org/cgi/content/full/25/13/3287?maxtoshow=&HITS=10&hit
s=10&RESULTFORMAT=1&author1=delorme&andorexacttitle=and&andorexacttitleabs=a
nd&andorexactfulltext=and&searchid=1112980421492_6553&stored_search=&FIRSTIN
DEX=0&sortspec=relevance&journalcode=jneuro> .

Hope this helps,

Arno

Christopher Summerfield wrote: 

dear wu xiang

I think the procedure is outlined quite clearly in the Burgess & Gruzelier
article Phil Grieve just sent you, but to explain step by step...

1) you have coherence estimates at p electrode pairs for s subjects in 2
conditions.  This means that you can perform a t-test at each electrode.
If you have several estimates per pair, for example several time bins, or
perhaps even an entire time-frequency map, you may have several
t-estimates per pair. I will call these 'point estimate' statistics.

2) at each pair, take your subject-condition assignments and swap them
1000 (or more times), recomputing your t-values at every pair or at every
timepoint or whatever.  This will give you a null distribution
corresponding to every point estimate stat.

3) you can then determine whether each point estimate falls within alpha
of its null distribution.

4) to correct for multiple comparisons (for example, imagine that you have
20 electrodes=190 pairings), rather than checking each point estimate
against its distribution, enter (for each permutation) the maximum
statistic across your entire search space. So, in our example, if the
maximum permuted value from permutation 1 comes from electrode 188, you
enter that value into the null distribtuion. on the 2nd permutation, the
max value might come from another electrode.  You then check all your
electrodes against this distribution. This will provide an 'familywise
error' correction for multiple comparisons.

good luck
Chris

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-- 

Arnaud Delorme, Ph.D. 
Swartz Center for Computational Neuroscience, INC, University of San Diego
California 
La Jolla, CA92093-0961, USA 

Tel :(+1)-858-458-1927 ext 15 
Fax :(+1)-858-458-1847 
Web page: sccn.ucsd.edu/~arno <http://www.sccn.ucsd.edu/%7Earno>  
To think upon:

We have met the enemy, and he is us. 

Walt Kelly

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