[Eeglablist] Is ITC biased by trial numbers?

Michiel Spape Michiel.Spape at nottingham.ac.uk
Tue Nov 23 00:53:00 PST 2010


Hi Zara,
Having some experience with ITC, or at least cross-coherence, I would say yes - since the ITC of using only one trial is necessarily 1. I find that subtracting the baseline does help somewhat, but usually, one still find effects to be larger (and often more variable) if there are fewer trials. This, I guess, is more or less the same thing one would see in typical ERP type of studies - any component being larger depending on the number of trials one averages over. One idea I've heard before on this list is to correct for this is by using random subsets of the same number of trials as in the condition with fewest trials and average over these, so that they become comparable in magnitude. Does anyone else have suggested as to what might be possible pitfalls in this type of analysis? Any rules of thumb on the amount of subsets to use, for instance?

Best,
Mich

Michiel Spapé
Research Fellow
Perception & Action group
University of Nottingham
School of Psychology
www.cognitology.eu


-----Original Message-----
From: eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Zara Bergström
Sent: 19 November 2010 16:50
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
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