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Bug 2161 - make FAQ "why demean before ICA"

Status	ASSIGNED
Reported	2013-05-08 15:50:00 +0200
Modified	2013-09-23 15:05:48 +0200
Product:	FieldTrip
Component:	documentation
Version:	unspecified
Hardware:	PC
Operating System:	Windows
Importance:	P3 normal
Assigned to:	Jim Herring
URL:
Tags:
Depends on:
Blocks:
See also:

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Eelke Spaak - 2013-05-08 15:50:12 +0200

see bug 962 I don't immediately have the answer to the question, so someone with more ICA expertise can feel free to do this :)

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Johanna - 2013-05-08 16:34:53 +0200

For the FAQ: 1) Why in general ICA requires demeaning. 2) That cfg.demean in ft_componentanalysis is performing a per-trial demeaning. However, and maybe this does require a code modification, different methods have different defaults or assumptions within them. I haven't looked at all, but the 2 most common: 'runica' states that, if data comes from dis-continuous trials, then the mean from each trial should be removed first, prior to input to runica. In other words, cfg.demean='yes' should be mandatory in combination with 'runica'. 'fastica' removes the mean as a first step. However, this is performed on the trial-concatenated data. If cfg.demean=yes, then this step does nothing. If cfg.demean=no, then could end up with some outlier trials nowhere near the mean. 'pca' it will matter signficantly whether per-trial demean or not.

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Jim Herring - 2013-08-31 16:05:18 +0200

I've added myself to the CC as this bug is relevant to me for a part of the TMS-EEG tutorial. For the FAQ, according to Hyvärinen and Oja (http://www.bsp.brain.riken.jp/ICApub/NN00.pdf) demeaning on the entire dataset is done to simplify the ICA. According to them if the mixed signal has zero-mean, you can assume the source has zero-mean as well. This per-channel demeaning is done (in 'runica' and 'fastica') regardless of what the setting for cfg.demean is. Per-trial demeaning is done to avoid non-physiological components reflecting the mean of the trial. However, this is problematic in the case of trials where your event of interest causes an offset. According to Makeig: "When the channel means of the input data differ significantly from the baseline means, making the data mean-zero prior to ICA training introduces an 'active' DC (square-wave) component without physiological significance into the ICA decomposition. This problem may may be minimized by appending response epochs without baseline offsets to the input data (example: responses to standard stimuli appended to responses to target stimuli containing large monophasic late waves)." - http://sccn.ucsd.edu/~scott/tutorial/icafaq.html.