[Eeglablist] ICA Misinformation

Arnaud Delorme arno at ucsd.edu
Mon Jun 19 22:12:38 PDT 2017

Dear Robert,

1) On my ICA decomposition analysis on your data. You have selected a subset of the file where there is 1 minute and 41 second data of eye free data. I was only able to select 40 seconds in the same file, and I also showed that even in this short file, there was some residual eye movements. Jason and Stefan agreed with me. This is the reason why ICA components power spectrum over frontal channels (and frontal channels only) was affected below 10 Hz frequency band in my data analysis. So on my ICA decomposition, our disagreement comes from the interpretation. You feel that the power we remove at low frequency in frontal channel is not eye movement. In an attempt to convince you, I have picked up a clean region from your EDF dataset, and did some dipole localization at this latency. We see that in the clean data, the best dipolar fit (with 2 symmetrical dipoles) ends up near the eye balls with a residual variance of 6.9%. Hopefully this convinces you that your data is not free of eye movement artifacts. If you are willing to take a step further you might contemplate the idea that ICA can remove this residual spurious activity.

2) On the WinEEG ICA decomposition analysis. It is critical for us to see the scalp topography (and if possible continuous activity) of the components the people at the Australia workshop selected. Without this, it is not possible for us to comment on the cleaned data. I agree with you that there was some phase distortion in alpha (visible directly in the raw data in the first email you sent) and that this should not be the case. However, without seing the ICA decomposition, it is not possible for us to conclude as to wether people selected the wrong ICA components or if the ICA decomposition implemented in this software is buggy (ICA is not a simple algorithm and it is sensitive to numerical imprecision and a lot of other parameters - a suboptimal implementation could easily explain the WinEEG results). Also, you seem to imply that the WinEEG people were running ICA on their data then throwing away the raw data (which is why their ICA biased neurofeedback database is useless for practical purposes). Is that correct? One should never throw away the raw data. If they did throw away the raw data, it is an indication that the WinEEG are not rigorous in their approach and therefore might not have implemented ICA in an optimal way. If it is not the case, one may easily reconstruct the database of measures with or without ICA decomposition (assuming ICA is done right which does not seem to be the case) then assess data measure distoritions (power, phase index, etc…) in a statistical fashion.

Best wishes,



> On Jun 18, 2017, at 11:44 AM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:
> <Pre-ICA-Hand Artifact free selections.edf>

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