[Eeglablist] Is ITC biased by trial numbers?

[Spencer, Kevin M.]

Dear Zara,

Yes, ITC (in general, not just in EEGLAB) is biased by the number of trials used to compute it. One recent paper that discusses this issue is Edwards et al., 2009, J Neurophysiology.

In my experience, the best way to overcome this problem is to select approximately equal numbers of trials for each condition. With respect to baseline correction, not just the overall ITC value but the range of effects can be biased by the number of trials, so baseline correction is not the solution. I've tried statistical mapping with the permutation test, but this still seems to report too many false positives.

Good luck,
Kevin
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Kevin M. Spencer, Ph.D. 
Director, Neural Dynamics Laboratory (http://ndl.hms.harvard.edu)
Research Health Scientist, VA Boston Healthcare System
Assistant Professor of Psychiatry, Harvard Medical School
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________________________________________
From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Zara Bergström [zmb25 at cam.ac.uk]
Sent: Friday, November 19, 2010 11:50 AM
To: eeglablist at sccn.ucsd.edu
Subject: [Eeglablist] Is ITC biased by trial numbers?

Dear EEG experts,

is the ITC measure as implemented by EEGLAB biased towards lower trial
numbers (i.e. higher itc when fewer trials are used in the computation, as
some measures of phase coherence supposedly are), and if so, how do you
deal with that issue when comparing conditions with different trial
numbers? Do you think it is appropriate to compute baseline corrected ITC,
which might help?

I analysed epoched datasets (-1-2s using default pre-stimulus baseline for
ERSP) with wavelets using newtimef to get complex ITC values (using the
default 'phasecoher' option), converted the ITC to real numbers between 0-1
(absitc=sqrt(real(itc).^2+imag(itc).^2)), and averaged these into
participant x condition x time x frequency ITC matrices for use in group
level statistics.

The attached line plot shows grand average (24 subjects) ITC averaged
across the alpha band (8-12 hz) for four conditions. The condition with the
fewest average trial numbers (red line) has significantly higher ITC than
the other conditions throughout the trial, even before stimulus onset,
which cannot be explained by psychological factors since these conditions
were presented randomly intermixed. If I however were to subtract the
average baseline period ITC from the post-stimulus data, it seems that the
difference would disappear. Would that be an appropriate step to take here?

Do you think this pattern is caused by a trial number bias, or have I done
something wrong in the analysis pipeline?

Any thoughts would be very much appreciated.

Thanks very much for you time,

Zara Bergstrom